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Qu Y, Lee CY. Estimation of standardized real-time fatality rate for ongoing epidemics. PLoS One 2024; 19:e0303861. [PMID: 38771824 PMCID: PMC11108209 DOI: 10.1371/journal.pone.0303861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/02/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages. METHODS To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022. FINDINGS The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy. CONCLUSIONS As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.
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Affiliation(s)
- Yuanke Qu
- Department of Computer Science and Engineering, Guangdong Ocean University, Zhanjiang, People’s Republic of China
| | - Chun Yin Lee
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
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Mohamadpour F, Askarian A, Askarian M. Picture analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic. Sci Rep 2024; 14:6310. [PMID: 38491112 PMCID: PMC10943230 DOI: 10.1038/s41598-024-56758-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024] Open
Abstract
Today, advertising science is a tool that helps advertisers to design their advertising to meet the needs of the audience. In this regard, knowing and understanding the audience is one of the most important points that advertisers should pay attention to before advertising in order to better attract the audience. This study has been done with the aim of billboards and infographics analysis related to promoting preventive behaviors and vaccination against the Coronavirus disease pandemic and investigating the opinion of the general adult population of Iran. The method used in this research is the qualitative method. In this research, according to the type of data and research goals, Kress and Van Leeuwen's discourse theory method has been used. The sample size includes 36 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). Then, an online survey about promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach's alpha internal consistency coefficient, yielded a coefficient of 0.968. In this study, data analysis was conducted using IBM SPSS Statistics software, version 15.0 (IBM Corp., Armonk, NY, USA). Finally, users' opinions about of billboards and infographics were analyzed using descriptive statistics. The results of component analysis and surveys show that visual components such as «The staring look at the spectator (Demand)», «Head-on Shot (inclusion)», «Down Shot (Creating a sense of participation for the represented person)», «Close-up (intimate/individual relationship)», «Level Shot (equality)» and «High-Angle Shot (Presenting power)» in medical advertising has had a great impact in arousing public opinion to create a positive attitude towards preventive measures and vaccination during the Coronavirus disease epidemic. The results of this research show that in visual communication, visual components play a significant role in creating and maintaining target ideologies. Also, advertising in the field of preventive measures in medical sciences requires certain rules that determine people's culture and the main foundation of their attitude and thinking. Therefore, it is necessary to know such knowledge and learn it by the medical staff to deal with critical situations.
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Affiliation(s)
- Fereshteh Mohamadpour
- Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ardalan Askarian
- College of Arts and Science, University of Saskatchewan, Saskatoon, Canada
| | - Mehrdad Askarian
- Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
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Mohamadpour F, Groot G, Askarian A, Askarian M. Text analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the coronavirus pandemic and investigating the opinions of the Iranian adult population. BMC Public Health 2024; 24:651. [PMID: 38429731 PMCID: PMC10905937 DOI: 10.1186/s12889-024-18135-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/17/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Advertising is one of the most important solutions that health centers and medical services around the world use to try to encourage public opinion to create a positive attitude towards preventive measures and vaccination. This study has been done with the aim of text analysis of billboards and infographics related to promoting preventive behaviors and vaccination against the coronavirus pandemic and providing solutions and models for preventive information and advertising in the field of health. METHODS The study method in this research is a combination of qualitative and content analysis. Data collection was done in a targeted manner. The sample size includes 33 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). The data was analyzed based on the three-dimensional discourse analysis theory of Fairclough. Then, an online survey about promoting preventive behaviors and vaccination against the coronavirus pandemic in the format of billboards and infographics was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The age group of people was selected from 18 to 70 years. Considering that the number of participants should be representative of the entire community under investigation, therefore, based on Cochran's formula, the sample size was equal to 350 people. Finally, users' opinions were analyzed using descriptive statistics. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach's alpha internal consistency coefficient, yielded a coefficient of 0.968. RESULTS The results show that among the four linguistic components of words, syntax, coherence and text structure; "live metaphors", "pronoun "we", "collocation and reference", and "attitude markers" have the most impact on the audience. The frequency percentage of the data shows that these language elements have tremendous power in attracting the audience to perform preventive behaviors. The results show that the language reflects the culture, opinions and needs of people in the society. Also, the results show that encouraging people to perform preventive behaviors should be through the integration of medical information with motivational linguistic factors in order to attract the audience more. CONCLUSIONS It can be concluded that the use of the appropriate pattern of medical advertising discourse and correct communication strategies, will help public participation in the field of epidemic control. The language of effective health education and health communication during an epidemic must be related to the ways of thinking and speaking of ordinary people. Also, words with metaphorical and ironic meanings have a high potential to influence the health performance of people in society and increase public awareness of health communication. Therefore, using them to create a new value system with the aim of controlling and overcoming the consequences of the epidemic is very effective.
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Affiliation(s)
- Fereshteh Mohamadpour
- Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Gary Groot
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada
| | - Ardalan Askarian
- College of Arts & Science, University of Saskatchewan, Saskatoon, Canada
| | - Mehrdad Askarian
- Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
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4
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Ai M, Wang W. Optimal vaccination ages for emerging infectious diseases under limited vaccine supply. J Math Biol 2023; 88:13. [PMID: 38135859 DOI: 10.1007/s00285-023-02030-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 06/13/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023]
Abstract
Rational allocation of limited vaccine resources is one of the key issues in the prevention and control of emerging infectious diseases. An age-structured infectious disease model with limited vaccine resources is proposed to explore the optimal vaccination ages. The effective reproduction number [Formula: see text] of the epidemic disease is computed. It is shown that the reproduction number is the threshold value for eradicating disease in the sense that the disease-free steady state is globally stable if [Formula: see text] and there exists a unique endemic equilibrium if [Formula: see text]. The effective reproduction number is used as an objective to minimize the disease spread risk. Using the epidemic data from the early spread of Wuhan, China and demographic data of Wuhan, we figure out the strategies to distribute the vaccine to the age groups to achieve the optimal vaccination effects. These analyses are helpful to the design of vaccination schedules for emerging infectious diseases.
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Affiliation(s)
- Mingxia Ai
- School of Mathematics and Statistics, Southwest University, Chongqing, 400715, China
| | - Wendi Wang
- School of Mathematics and Statistics, Southwest University, Chongqing, 400715, China.
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Coskun A, Demirci B, Turkdogan KA. Association of carbon monoxide poisonings and carboxyhemoglobin levels with COVID-19 and clinical severity. World J Methodol 2023; 13:248-258. [PMID: 37771862 PMCID: PMC10523238 DOI: 10.5662/wjm.v13.i4.248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/08/2023] [Accepted: 07/25/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), which recently spread throughout the entire world, is still a significant health issue. Additionally, the most common cause of risky poisoning in emergency services is carbon monoxide (CO) poisoning. Both disorders seem to merit more research as they have an impact on all bodily systems via the lungs. AIM To determine how arterial blood gas and carboxyhemoglobin (COHb) levels affect the clinical and prognostic results of individuals requiring emergency treatment who have both COVID-19 and CO poisoning. METHODS Between January 2018 and December 2021, 479 CO-poisoning patients participated in this single-center, retrospective study. Patients were primarily divided into two groups for analysis: Pre-pandemic and pandemic periods. Additionally, the pandemic era was divided into categories based on the presence of COVID-19 and, if present, the clinical severity of the infection. The hospital information system was used to extract patient demographic, clinical, arterial blood gas, COVID-19 polymerase chain reaction, and other laboratory data. RESULTS The mean age of the 479 patients was 54.93 ± 11.51 years, and 187 (39%) were female. 226 (47%) patients were in the pandemic group and 143 (30%) of them had a history of COVID-19. While the mean potential of hydrogen (pH) in arterial blood gas of all patients was 7.28 ± 0.15, it was 7.35 ± 0.10 in the pre-pandemic group and 7.05 ± 0.16 in the severe group during the pandemic period (P < 0.001). COHb was 23.98 ± 4.19% in the outpatients and 45.26% ± 3.19% in the mortality group (P < 0.001). Partial arterial oxygen pressure (PaO2) was 89.63 ± 7.62 mmHg in the pre-pandemic group, and 79.50 ± 7.18 mmHg in the severe group during the pandemic period (P < 0.001). Despite the fact that mortality occurred in 35 (7%) of all cases, pandemic cases accounted for 30 of these deaths (85.7%) (P <0.001). The association between COHb, troponin, lactate, partial arterial pressure of carbon dioxide, HCO3, calcium, glucose, age, pH, PaO2, potassium, sodium, and base excess levels in the pre-pandemic and pandemic groups was statistically significant in univariate linear analysis. CONCLUSION Air exchange barrier disruption caused by COVID-19 may have pulmonary consequences. In patients with a history of pandemic COVID-19, clinical results and survival are considerably unfavorable in cases of CO poisoning.
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Affiliation(s)
- Abuzer Coskun
- Emergency Medicine Clinic, Istanbul Bagcilar Training and Research Hospital, Istanbul 34200, Turkey
| | - Burak Demirci
- Emergency Medicine Clinic, Istanbul Bagcilar Training and Research Hospital, Istanbul 34200, Turkey
| | - Kenan Ahmet Turkdogan
- Emergency Medicine Department, Istanbul Çam and Sakura City Hospital, Istanbul 34494, Turkey
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Ding L, Wei J, Wang B. The Impact of COVID-19 on the Prevalence, Mortality, and Associated Risk Factors for Mortality in Patients with Hip Fractures: A Meta-Analysis. J Am Med Dir Assoc 2023; 24:846-854. [PMID: 37062371 PMCID: PMC10027948 DOI: 10.1016/j.jamda.2023.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/24/2023]
Abstract
OBJECTIVE This study aimed to assess (1) the prevalence of COVID-19 in patients with hip fracture; (2) the mortality rate of patients with hip fracture associated with COVID-19; (3) risk factors associated with mortality in patients with hip fracture; and (4) the effects of COVID-19 on surgical outcomes of patients with hip fracture. DESIGN Meta-analysis. SETTING AND PARTICIPANTS Patients with hip fractures during COVID-19. METHODS PubMed, Web of Science, and Embase were systematically reviewed. The outcomes included the prevalence of COVID-19, case fatality rate, 30-day mortality, cause of death, risk factors associated with the mortality of patients with hip fracture, time to surgery, surgical time, and length of hospitalization. Risk ratio or weight mean difference with 95% confidence intervals were used to pool the estimates. RESULTS A total of 60 studies were included in this meta-analysis. The pooled estimate showed that the prevalence of COVID-19 was 21% in patents with hip fractures. Patients with hip fracture with COVID-19 had an increased 30-day mortality risk compared with those without the infection. The main causes of death were respiratory failure, COVID-19-associated pneumonia, multiorgan failure, and non-COVID-19 pneumonia. The hospitalization was longer in patients with COVID-19 when compared with those without the infection, but was shorter in patients during the pandemic period. The surgery time and time to surgery were not significantly different between patients during or before the pandemic period and in those with or without COVID-19. CONCLUSIONS AND IMPLICATIONS The 30-day mortality rate was significantly higher in patients with hip fracture with COVID-19 infection than those without. Patients with COVID-19 had a higher all-cause mortality rate than those without. This information can be used by the medical community to guide the management of patients with hip fracture with COVID-19.
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Affiliation(s)
- Lifeng Ding
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jingzan Wei
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Bin Wang
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China.
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Roy Pradhan S, Yashavarddhan MH, Gupta A, Kumar P, Kumar A, Arif N, Agrawal U, Kumar RS, Singh S. Insights from establishing a high throughput viral diagnostic laboratory for SARS-CoV-2 RT-PCR testing facility: challenges and experiences. Front Public Health 2023; 11:1122715. [PMID: 37143990 PMCID: PMC10152062 DOI: 10.3389/fpubh.2023.1122715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/13/2023] [Indexed: 05/06/2023] Open
Abstract
Background: The World Health Organization declared the coronavirus disease 2019 (COVID-19) a global pandemic on 11 March 2020. Identifying the infected people and isolating them was the only measure that was available to control the viral spread, as there were no standardized treatment interventions available. Various public health measures, including vaccination, have been implemented to control the spread of the virus worldwide. India, being a densely populated country, required laboratories in different zones of the country with the capacity to test a large number of samples and report test results at the earliest. The Indian Council of Medical Research (ICMR) took the lead role in developing policies, generating advisories, formulating guidelines, and establishing and approving testing centers for COVID-19 testing. With advisories of ICMR, the National Institute of Cancer Prevention and Research (NICPR) established a high-throughput viral diagnostic laboratory (HTVDL) for RT-PCR-based diagnosis of SARS-CoV-2 in April 2020. HTVDL was established during the first lockdown to serve the nation in developing and adopting rapid testing procedures and to expand the testing capacity using "Real-Time PCR." The HTVDL provided its testing support to the national capital territory of Delhi and western Uttar Pradesh, with a testing capacity of 6000 tests per day. The experience of establishing a high-throughput laboratory with all standard operating procedures against varied challenges in a developing country such as India is explained in the current manuscript which will be useful globally to enhance the knowledge on establishing an HTVDL in pandemic or non-pandemic times.
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Affiliation(s)
- Sanchita Roy Pradhan
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - M. H. Yashavarddhan
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Ashish Gupta
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Pramod Kumar
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Anuj Kumar
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Nazneen Arif
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Usha Agrawal
- Department of Histopathology and Cytology, National Institute of Pathology, Safdarjung Hospital, New Delhi, India
| | - R. Suresh Kumar
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
| | - Shalini Singh
- High-Throughput Viral Diagnostic Laboratory (HTVDL), ICMR - National Institute of Cancer Prevention and Research (NICPR), Ministry of Health and Family Welfare, Government of India, Noida, Uttar Pradesh, India
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Izadi N, Taherpour N, Mokhayeri Y, Sotoodeh Ghorbani S, Rahmani K, Hashemi Nazari SS. Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis. Med J Islam Repub Iran 2022; 36:155. [PMID: 36654849 PMCID: PMC9832936 DOI: 10.47176/mjiri.36.155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Indexed: 12/24/2022] Open
Abstract
Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.
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Affiliation(s)
- Neda Izadi
- Department of Epidemiology, School of Public Health and Safety, Shahid
Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloufar Taherpour
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti
University of Medical Sciences, Tehran, Iran
| | - Yaser Mokhayeri
- Cardiovascular Research Center, Shahid Rahimi Hospital, Lorestan
University of Medical Sciences, Khorramabad, Iran
| | - Sahar Sotoodeh Ghorbani
- Department of Epidemiology, School of Public Health and Safety, Shahid
Beheshti University of Medical Sciences, Tehran, Iran
| | - Khaled Rahmani
- Liver and Digestive Research Center, Research Institute for Health
Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Seyed Saeed Hashemi Nazari
- Prevention of Cardiovascular Disease Research Center, Department of
Epidemiology, School of Public Health and Safety, Shahid Beheshti University of
Medical Sciences, Tehran, Iran
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Hayashi K, Nishiura H. Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022. BMC Infect Dis 2022; 22:933. [PMID: 36510193 PMCID: PMC9744068 DOI: 10.1186/s12879-022-07929-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death. METHODS We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated. RESULTS The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively. CONCLUSION We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.
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Affiliation(s)
- Katsuma Hayashi
- grid.258799.80000 0004 0372 2033Graduate School of Medicine, Kyoto University, Yoshidakonoecho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Hiroshi Nishiura
- grid.258799.80000 0004 0372 2033Graduate School of Medicine, Kyoto University, Yoshidakonoecho, Sakyo-ku, Kyoto, 606-8501 Japan
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10
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Rangachev A, Marinov GK, Mladenov M. The Impact and Progression of the COVID-19 Pandemic in Bulgaria in Its First Two Years. Vaccines (Basel) 2022; 10:vaccines10111901. [PMID: 36366409 PMCID: PMC9696094 DOI: 10.3390/vaccines10111901] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/06/2022] [Indexed: 11/12/2022] Open
Abstract
After initially having low levels of SARS-CoV-2 infections for much of the year, Bulgaria experienced a major epidemic surge at the end of 2020, which caused the highest recorded excess mortality in Europe, among the highest in the word (Excess Mortality Rate, or EMR ∼0.25%). Two more major waves followed in 2021, followed by another one in early 2022. In this study, we analyze the temporal and spatial patterns of excess mortality at the national and local levels and across different demographic groups in Bulgaria and compare those to the European levels. Bulgaria has continued to exhibit the previous pattern of extremely high excess mortality, as measured both by crude mortality metrics (an EMR of ∼1.05%, up to the end of March 2022) and by standardized ones—Potential Years of Life Lost (PYLL) and Aged-Standardized Years of life lost Rate (ASYR). Unlike Western Europe, the bulk of excess mortality in Bulgaria, as well as in several other countries in Eastern Europe, occurred in the second year of the pandemic, likely related to the differences in the levels of vaccination coverage between these regions. We also observe even more extreme levels of excess mortality at the regional level and in some subpopulations (e.g., total EMR values for males ≥ 2% and EMR values for males aged 40–64 ≥ 1% in certain areas). We discuss these observations in light of the estimates of infection fatality rate (IFR) and eventual population fatality rate (PFR) made early in the course of the pandemic.
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Affiliation(s)
- Antoni Rangachev
- Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
- International Center for Mathematical Sciences-Sofia, 1113 Sofia, Bulgaria
| | - Georgi K. Marinov
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Correspondence:
| | - Mladen Mladenov
- Tilburg University, Warandelaan 2, 5037 AB Tilburg, The Netherlands
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11
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Qu Y, Lee CY, Lam KF. A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy. Sci Rep 2022; 12:18277. [PMID: 36316534 PMCID: PMC9619021 DOI: 10.1038/s41598-022-23138-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 10/25/2022] [Indexed: 12/31/2022] Open
Abstract
An accurate estimator of the real-time fatality rate is warranted to monitor the progress of ongoing epidemics, hence facilitating the policy-making process. However, most of the existing estimators fail to capture the time-varying nature of the fatality rate and are often biased in practice. A simple real-time fatality rate estimator with adjustment for reporting delays is proposed in this paper using the fused lasso technique. This approach is easy to use and can be broadly applied to public health practice as only basic epidemiological data are required. A large-scale simulation study suggests that the proposed estimator is a reliable benchmark for formulating public health policies during an epidemic with high accuracy and sensitivity in capturing the changes in the fatality rate over time, while the other two commonly-used case fatality rate estimators may convey delayed or even misleading signals of the true situation. The application to the COVID-19 data in Germany between January 2020 and January 2022 demonstrates the importance of the social restrictions in the early phase of the pandemic when vaccines were not available, and the beneficial effects of vaccination in suppressing the fatality rate to a low level since August 2021 irrespective of the rebound in infections driven by the more infectious Delta and Omicron variants during the fourth wave.
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Affiliation(s)
- Yuanke Qu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, People's Republic of China
- Guangdong Ocean University, Zhanjiang, People's Republic of China
| | - Chun Yin Lee
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - K F Lam
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, People's Republic of China.
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
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12
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Morales Chacón LM, Galán García L, Cruz Hernández TM, Pavón Fuentes N, Maragoto Rizo C, Morales Suarez I, Morales Chacón O, Abad Molina E, Rocha Arrieta L. Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units. Behav Sci (Basel) 2022; 12:234. [PMID: 35877304 PMCID: PMC9312189 DOI: 10.3390/bs12070234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/05/2022] [Indexed: 12/10/2022] Open
Abstract
Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)-hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys-asthma, cough and fever; class 3, mean age 74.5 ys-hypertension, diabetes and cough; class 4, mean age 67.8 ys-hypertension and no symptoms; class 5, mean age 53 ys-cough and no comorbidities; class 6, mean age 60 ys-without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson's disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.
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Affiliation(s)
| | | | | | - Nancy Pavón Fuentes
- International Center for Neurological Restoration, Havana 11300, Cuba; (N.P.F.); (C.M.R.); (E.A.M.)
| | - Carlos Maragoto Rizo
- International Center for Neurological Restoration, Havana 11300, Cuba; (N.P.F.); (C.M.R.); (E.A.M.)
| | | | - Odalys Morales Chacón
- Languages Center, Technological University of Havana Jose Antonio Echeverria, La Habana 3H3M+XJ6, Cuba;
| | - Elianne Abad Molina
- International Center for Neurological Restoration, Havana 11300, Cuba; (N.P.F.); (C.M.R.); (E.A.M.)
| | - Luisa Rocha Arrieta
- Center for Research and Advanced Studies México, Ciudad de México 14330, Mexico;
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13
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Wang S, Li Y, Wang X, Zhang Y, Yuan Y, Li Y. The Impact of Lockdown, Patient Classification, and the Large-Scale Case Screening on the Spread of the Coronavirus Disease 2019 (COVID-19) in Hubei. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8920117. [PMID: 35535036 PMCID: PMC9077452 DOI: 10.1155/2022/8920117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/15/2021] [Accepted: 04/04/2022] [Indexed: 11/17/2022]
Abstract
The coronavirus disease (COVID-19) which emerged in Wuhan, China, in December 2019, is widely controlled now in China. However, the global epidemic is still severe. To study and comment on Hubei's approaches for responding to the disease, the paper considered some factors such as suspected cases (part of them are influenza patients or common pneumonia patients, etc.), quarantine, patient classification (three types), clinically diagnosed cases, and lockdown of Wuhan and Hubei. After that, the paper established an SELIHR model based on the surveillance data of Hubei published by the Hubei Health Commission from 10 January 2020 to 30 April 2020 and used the fminsearch optimization method to estimate the optimal parameters of the model. We obtained the basic reproduction number ℛ 0 = 3.1571 from 10 to 22 January. ℛ 0 was calculated as 2.0471 from 23 to 27 January. From 28 January to 30 April, ℛ 0 = 1.5014. Through analysis, it is not hard to find that the patients without classification during the period of confirmed cases will result in the cumulative number of cases in Hubei to increase. In addition, regarding the lockdown measures implemented by Hubei during the epidemic, our simulations also show that if the lockdown time of either Hubei or Wuhan is advanced, it will effectively curb the spread of the epidemic. If the lockdown measures are not taken, the total cumulative number of cases will increase substantially. From the results of the study, it can be concluded that the lockdown, patient classification, and the large-scale case screening are essential to slow the spread of COVID-19, which can provide references for other countries or regions.
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Affiliation(s)
- Shengtao Wang
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China
| | - Yan Li
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China
| | - Ximei Wang
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China
| | - Yuanyuan Zhang
- School of Foreign Studies, Yangtze University, Jingzhou 434023, China
| | - Yiyi Yuan
- Viterbi School of Engineering, University of Southern California, Los Angeles CA 90007, USA
| | - Yong Li
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China
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14
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Shi N, Huang J, Ai J, Wang Q, Cui T, Yang L, Ji H, Bao C, Jin H. Transmissibility and pathogenicity of the severe acute respiratory syndrome coronavirus 2: A systematic review and meta-analysis of secondary attack rate and asymptomatic infection. J Infect Public Health 2022; 15:297-306. [PMID: 35123279 PMCID: PMC8801962 DOI: 10.1016/j.jiph.2022.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 11/16/2021] [Accepted: 01/23/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Understanding the transmissibility and pathogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for control policies, but evidence remains limited. METHODS We presented a systematic and meta-analytic summary concerning the transmissibility and pathogenicity of COVID-19. RESULTS A total of 105 studies were identified, with 35042 infected cases and 897912 close contacts. 48.6% (51/105) of studies on secondary transmissions were from China. We estimated a total SIR of 7.8% (95% confidence interval [CI], 6.8%-8.8%), SAR of 6.6% (95% CI, 5.7%-7.5%), and symptomatic infection ratio of 86.9% (95%CI, 83.9%-89.9%) with a disease series interval of 5.84 (95%CI, 4.92-6.94) days. Household contacts had a higher risk of both symptomatic and asymptomatic infection, and transmission was driven between index cases and second-generation cases, with little transmission occurring in second-to-later-generation cases (SIR, 12.4% vs. 3.6%). The symptomatic infection ratio was not significantly different in terms of infection time, generation, type of contact, and index cases. CONCLUSIONS Our results suggest a higher risk of infection among household contacts. Transmissibility decreased with generations during the intervention. Pathogenicity of SARS-CoV-2 varied among territories, but didn't change over time. Strict isolation and medical observation measures should be implemented.
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Affiliation(s)
- Naiyang Shi
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jinxin Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jing Ai
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Qiang Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Tingting Cui
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Liuqing Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Hong Ji
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Changjun Bao
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Hui Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
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15
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Zheng X, Guo Y, Ma W, Yang H, Luo L, Wen L, Zhou X, Li Q, Bi J, Wang P, Wang H. A Longitudinal Study on the Mental Health of College Students in Jinan During the Peak Stage of the COVID-19 Epidemic and the Society Reopening. Biomed Hub 2021; 6:102-110. [PMID: 34950671 PMCID: PMC8613590 DOI: 10.1159/000519586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/06/2021] [Indexed: 12/19/2022] Open
Abstract
Introduction COVID-19, a continuously emerging human-to-human infectious disease, has exerted a significant impact on the mental health of college students. However, little is known regarding the variations in the mental health issues experienced by college students during the peak versus reopening stages of the COVID-19 epidemic in China. Methods To assess these issues, an online longitudinal survey was conducted via a WeChat applet. Undergraduates (n = 300) were recruited from 26 universities throughout Jinan in February 2020 (T1 - the epidemic peak stage) and in January 2021 (T2 - the society reopening stage). Their mental status was determined using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 item, and the Insomnia Severity Index. Results Of the original 300 college students recruited for this survey, 294 responses at T1 and 285 at T2 were analyzed. Compared with responses obtained at T1, college students at T2 showed a greater prevalence of depression (65.3 vs. 51.0%; p = 0.001) and anxiety (47.7 vs. 38.1%, p = 0.019), and experienced more severe depression (p < 0.001) and anxiety (p < 0.001). Both males (p = 0.03) and females (p < 0.01) showed higher levels of depression at T2 versus T1, while no differences were obtained with regard to anxiety and insomnia. At T1, Grade 4 students showed greater levels of depression (p = 0.005) and anxiety (p = 0.008) than that of Grade 1 students. While at T2, only greater levels of depression (p = 0.004) were present when compared with that of Grade 1 students. Additionally, Grade 4 college students demonstrated a greater prevalence of depression at T2 versus T1 (p = 0.03), but no statistically differences were present for anxiety and insomnia. No statistically significant differences were obtained among the 4 grades of college students for insomnia at either the T1 or T2. Conclusion With progression of the COVID-19 epidemic, college students showed increasing levels of depression and anxiety, with Grade 4 college students being most seriously affected. It is imperative that intervention strategies be implemented to mitigate against these mental health issues resulting from the COVID-19 epidemic.
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Affiliation(s)
- Xiaolei Zheng
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Division of Neuropsychiatry and Psychosomatics, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yuji Guo
- Department of Histology and Embryology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wen Ma
- Center for Clinical Neurolinguistics, School of Foreign Languages and Literature, Shandong University, Jinan, China
| | - Hui Yang
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Liyan Luo
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Li Wen
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Xiaolan Zhou
- Center for Clinical Neurolinguistics, School of Foreign Languages and Literature, Shandong University, Jinan, China
| | - Qing Li
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jianzhong Bi
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ping Wang
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hongxing Wang
- Division of Neuropsychiatry and Psychosomatics, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Beijing Psychosomatic Disease Consultation Center, Xuanwu Hospital, Capital Medical University, Beijing, China
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16
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Ghimire BR, Parajuli RR, Khatiwada B, Poudel S, Sharma K, Mishra B. Covira: A COVID-19 risk assessment, visualization and communication tool. SOFTWAREX 2021; 16:100873. [PMID: 34778507 PMCID: PMC8570635 DOI: 10.1016/j.softx.2021.100873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 10/08/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
Assessing the possibility of Coronavirus infection and its risk on an individual's life, estimating the spatial transmission risk based on the dynamic condition of a particular place into consideration, and communicating the same to the public is crucial for minimizing the potential impact of COVID-19. With the increase in cases world-wide, new patterns are being unfolded. Nevertheless, an application for risk assessment will not only help the researcher to quickly verify the proof of concept but also is a powerful tool to bring into notice the results immediately as one of the perfect tools for risk communication. Covira (https://covira.info) is an open-source web-based software that captures the response, calculates personal as well as regional risk, and displays the result to the end-user in the form of maps and risk cards.
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17
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Kim DY, Shinde SK, Lone S, Palem RR, Ghodake GS. COVID-19 Pandemic: Public Health Risk Assessment and Risk Mitigation Strategies. J Pers Med 2021; 11:1243. [PMID: 34945715 PMCID: PMC8707584 DOI: 10.3390/jpm11121243] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 12/17/2022] Open
Abstract
A newly emerged respiratory viral disease called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is also known as pandemic coronavirus disease (COVID-19). This pandemic has resulted an unprecedented global health crisis and devastating impact on several sectors of human lives and economies. Fortunately, the average case fatality ratio for SARS-CoV-2 is below 2%, much lower than that estimated for MERS (34%) and SARS (11%). However, COVID-19 has a much higher transmissibility rate, as evident from the constant increase in the count of infections worldwide. This article explores the reasons behind how COVID-19 was able to cause a global pandemic crisis. The current outbreak scenario and causes of rapid global spread are examined using recent developments in the literature, epidemiological features relevant to public health awareness, and critical perspective of risk assessment and mitigation strategies. Effective pandemic risk mitigation measures have been established and amended against COVID-19 diseases, but there is still much scope for upgrading execution and coordination among authorities in terms of organizational leadership's commitment and diverse range of safety measures, including administrative control measures, engineering control measures, and personal protective equipment (PPE). The significance of containment interventions against the COVID-19 pandemic is now well established; however, there is a need for its effective execution across the globe, and for the improvement of the performance of risk mitigation practices and suppression of future pandemic crises.
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Affiliation(s)
- Dae-Young Kim
- Department of Biological and Environmental Science, Dongguk University-Seoul, 32 Dongguk-ro, Ilsandong-gu, Goyang-si 10326, Gyeonggi-do, Korea; (D.-Y.K.); (S.K.S.)
| | - Surendra Krushna Shinde
- Department of Biological and Environmental Science, Dongguk University-Seoul, 32 Dongguk-ro, Ilsandong-gu, Goyang-si 10326, Gyeonggi-do, Korea; (D.-Y.K.); (S.K.S.)
| | - Saifullah Lone
- Interdisciplinary Division for Renewable Energy and Advanced Materials (iDREAM), National Institute of Technology (NIT), Srinagar 190006, India;
| | - Ramasubba Reddy Palem
- Department of Medical Biotechnology, Dongguk University-Seoul, 32 Dongguk-ro, Ilsandong-gu, Goyang-si 10326, Gyeonggi-do, Korea;
| | - Gajanan Sampatrao Ghodake
- Department of Biological and Environmental Science, Dongguk University-Seoul, 32 Dongguk-ro, Ilsandong-gu, Goyang-si 10326, Gyeonggi-do, Korea; (D.-Y.K.); (S.K.S.)
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18
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Cheng X, Hu J, Luo L, Zhao Z, Zhang N, Hannah MN, Rui J, Lin S, Zhu Y, Wang Y, Yang M, Xu J, Liu X, Yang T, Liu W, Li P, Deng B, Li Z, Liu C, Huang J, Peng Z, Bao C, Chen T. Impact of interventions on the incidence of natural focal diseases during the outbreak of COVID-19 in Jiangsu Province, China. Parasit Vectors 2021; 14:483. [PMID: 34538265 PMCID: PMC8449989 DOI: 10.1186/s13071-021-04986-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/31/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND During the period of the coronavirus disease 2019 (COVID-19) outbreak, strong intervention measures, such as lockdown, travel restriction, and suspension of work and production, may have curbed the spread of other infectious diseases, including natural focal diseases. In this study, we aimed to study the impact of COVID-19 prevention and control measures on the reported incidence of natural focal diseases (brucellosis, malaria, hemorrhagic fever with renal syndrome [HFRS], dengue, severe fever with thrombocytopenia syndrome [SFTS], rabies, tsutsugamushi and Japanese encephalitis [JE]). METHODS The data on daily COVID-19 confirmed cases and natural focal disease cases were collected from Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial CDC). We described and compared the difference between the incidence in 2020 and the incidence in 2015-2019 in four aspects: trend in reported incidence, age, sex, and urban and rural distribution. An autoregressive integrated moving average (ARIMA) (p, d, q) × (P, D, Q)s model was adopted for natural focal diseases, malaria and severe fever with thrombocytopenia syndrome (SFTS), and an ARIMA (p, d, q) model was adopted for dengue. Nonparametric tests were used to compare the reported and the predicted incidence in 2020, the incidence in 2020 and the previous 4 years, and the difference between the duration from illness onset date to diagnosed date (DID) in 2020 and in the previous 4 years. The determination coefficient (R2) was used to evaluate the goodness of fit of the model simulation. RESULTS Natural focal diseases in Jiangsu Province showed a long-term seasonal trend. The reported incidence of natural focal diseases, malaria and dengue in 2020 was lower than the predicted incidence, and the difference was statistically significant (P < 0.05). The reported incidence of brucellosis in July, August, October and November 2020, and SFTS in May to November 2020 was higher than that in the same period in the previous 4 years (P < 0.05). The reported incidence of malaria in April to December 2020, HFRS in March, May and December 2020, and dengue in July to November 2020 was lower than that in the same period in the previous 4 years (P < 0.05). In males, the reported incidence of malaria in 2020 was lower than that in the previous 4 years, and the reported incidence of dengue in 2020 was lower than that in 2017-2019. The reported incidence of malaria in the 20-60-year age group was lower than that in the previous 4 years; the reported incidence of dengue in the 40-60-year age group was lower than that in 2016-2018. The reported cases of malaria in both urban and rural areas were lower than in the previous 4 years. The DID of brucellosis and SFTS in 2020 was shorter than that in 2015-2018; the DID of tsutsugamushi in 2020 was shorter than that in the previous 4 years. CONCLUSIONS Interventions for COVID-19 may help control the epidemics of natural focal diseases in Jiangsu Province. The reported incidence of natural focal diseases, especially malaria and dengue, decreased during the outbreak of COVID-19 in 2020. COVID-19 prevention and control measures had the greatest impact on the reported incidence of natural focal diseases in males and people in the 20-60-year age group.
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Affiliation(s)
- Xiaoqing Cheng
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | - Jianli Hu
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Nan Zhang
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | | | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zhihang Peng
- School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
| | - Changjun Bao
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China.
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China.
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19
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Ge Y, Chen Z, Handel A, Martinez L, Xiao Q, Li C, Chen E, Pan J, Li Y, Ling F, Shen Y. The impact of social distancing, contact tracing, and case isolation interventions to suppress the COVID-19 epidemic: A modeling study. Epidemics 2021; 36:100483. [PMID: 34284227 PMCID: PMC8275486 DOI: 10.1016/j.epidem.2021.100483] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 06/25/2021] [Accepted: 07/09/2021] [Indexed: 12/30/2022] Open
Abstract
INTRODUCTION Most countries are dependent on nonpharmaceutical public health interventions such as social distancing, contact tracing, and case isolation to mitigate COVID-19 spread until medicines or vaccines widely available. Minimal research has been performed on the independent and combined impact of each of these interventions based on empirical case data. METHODS We obtained data from all confirmed COVID-19 cases from January 7th to February 22nd 2020 in Zhejiang Province, China, to fit an age-stratified compartmental model using human contact information before and during the outbreak. The effectiveness of social distancing, contact tracing, and case isolation was studied and compared in simulation. We also simulated a two-phase reopening scenario to assess whether various strategies combining nonpharmaceutical interventions are likely to achieve population-level control of a second-wave epidemic. RESULTS Our study sample included 1,218 symptomatic cases with COVID-19, of which 664 had no inter-province travel history. Results suggest that 36.5 % (95 % CI, 12.8-57.1) of contacts were quarantined, and approximately five days (95 % CI, 2.2-11.0) were needed to detect and isolate a case. As contact networks would increase after societal and economic reopening, avoiding a second wave without strengthening nonpharmaceutical interventions compared to the first wave it would be exceedingly difficult. CONCLUSIONS Continuous attention and further improvement of nonpharmaceutical interventions are needed in second-wave prevention. Specifically, contact tracing merits further attention.
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Affiliation(s)
- Yang Ge
- University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, Georgia, United States
| | - Zhiping Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Andreas Handel
- University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, Georgia, United States; University of Georgia, College of Public Health, Health Informatics Institute, Athens, Georgia, United States; University of Georgia, Center for the Ecology of Infectious Diseases, Athens, Georgia, United States
| | - Leonardo Martinez
- Boston University, School of Public Health, Department of Epidemiology, Boston, Massachusetts, United States
| | - Qian Xiao
- University of Georgia, Department of Statistics, Athens, Georgia, United States
| | - Changwei Li
- University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, Georgia, United States; Tulane University, School of Public Health and Tropical Medicine, Department of Epidemiology, New Orleans, Louisiana, United States
| | - Enfu Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jinren Pan
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Yang Li
- Renmin University of China, Center for Applied Statistics, Beijing, China; Renmin University of China, School of Statistics, Beijing, China; Renmin University of China, Statistical Consulting Center, Beijing, China.
| | - Feng Ling
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
| | - Ye Shen
- University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, Georgia, United States.
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20
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Kollosche D, Meyerhöfer W. COVID-19, mathematics education, and the evaluation of expert knowledge. EDUCATIONAL STUDIES IN MATHEMATICS 2021; 108:401-417. [PMID: 34934242 PMCID: PMC8390842 DOI: 10.1007/s10649-021-10097-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2021] [Indexed: 06/06/2023]
Abstract
Maturity and citizenship in a democracy require that laypersons are able to critically evaluate experts' use of mathematics. Learning to critically reflect on the use of mathematics, including the acquisition of the mathematical knowledge and skills required to that end, has been repeatedly postulated as an indispensable goal of compulsory education in mathematics. However, it remained unclear in how far such reflection is possible, even for the well-educated layperson in mathematics. We use different discussions in German mass media on the pandemic policy in the SARS-CoV-2 crisis in 2020 as examples with far-reaching individual and social consequences. The selected discussions build heavily on mathematical concepts such as mortality rates, casualty numbers, reproduction numbers, and exponential growth. We identify the concepts and discuss how far they can be understood by laypersons. On the one hand, we found that some mathematical models are inappropriate, which can also be determined by laypersons. On the other hand, we found uses of mathematics where ideal concepts are intermingled with complex statistical concepts. While only the ideal concepts can be understood by laypersons, only the statistical concepts lead to actual data. The identification of both types of concepts leads to a situation where the use of mathematics evades social control and opens spaces for misconceptions and manipulation. We conclude that the evaluation of experts' use of mathematics by laypersons is not possible in all relevant cases, and we discuss possible implications of this result.
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21
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Shawaqfah M, Almomani F. Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy. RESULTS IN PHYSICS 2021; 27:104484. [PMID: 34178593 PMCID: PMC8215910 DOI: 10.1016/j.rinp.2021.104484] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 05/22/2023]
Abstract
The present study illustrates the outbreak prediction and analysis on the growth and expansion of the COVID-19 pandemic using artificial neural network (ANN). The first wave of the pandemic outbreak of the novel Coronavirus (SARS-CoV-2) began in September 2019 and continued to March 2020. As declared by the World Health Organization (WHO), this virus affected populations all over the globe, and its accelerated spread is a universal concern. An ANN architecture was developed to predict the serious pandemic outbreak impact in Qatar, Spain, and Italy. Official statistical data gathered from each country until July 6th was used to validate and test the prediction model. The model sensitivity was analyzed using the root mean square error (RMSE), the mean absolute percentage error and the regression coefficient index R2, which yielded highly accurate values of the predicted correlation for the infected and dead cases of 0.99 for the dates considered. The verified and validated growth model of COVID-19 for these countries showed the effects of the measures taken by the government and medical sectors to alleviate the pandemic effect and the effort to decrease the spread of the virus in order to reduce the death rate. The differences in the spread rate were related to different exogenous factors (such as social, political, and health factors, among others) that are difficult to measure. The simple and well-structured ANN model can be adapted to different propagation dynamics and could be useful for health managers and decision-makers to better control and prevent the occurrence of a pandemic.
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Affiliation(s)
- Moayyad Shawaqfah
- Department of Civil Engineering, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan
| | - Fares Almomani
- Department of Chemical Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
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22
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Wang Y, Li Q, Tarimo CS, Wu C, Miao Y, Wu J. Prevalence and risk factors of worry among teachers during the COVID-19 epidemic in Henan, China: a cross-sectional survey. BMJ Open 2021; 11:e045386. [PMID: 34233970 PMCID: PMC8266429 DOI: 10.1136/bmjopen-2020-045386] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE To evaluate the level of worry and its influencing factors during the COVID-19 epidemic among teachers in Henan Province in China. STUDY DESIGN A cross-sectional study was conducted. METHODS We designed a cross-sectional survey that included 88 611 teachers from three cities in Henan Province, China between 4 February 2020 and 12 February 2020. Level of worry was measured using a five-item Likert scale, with 1 being 'not worried' and 5 being 'very worried'. The OR and 95% CI of potential influencing factors for level of worry among study participants were estimated using ordinal logistic regression models. RESULTS About 59% of teachers reported being 'very worried' about the COVID-19 epidemic. The proportion of female teachers was higher than of male teachers (60.33% vs 52.89%). In all age groups considered in this study, a 'very worried' condition accounted for the highest proportion. The age group 40-49 years had the lowest proportion of participants who were very worried, 52.34% of whom were men and 58.62% were women. After controlling for potential confounding factors, age, education level, type of teacher, school location, attention level, fear level, anxiety level and behaviour status were all related to level of worry (all p<0.05). CONCLUSION During the COVID-19 epidemic, there was a high proportion of teachers who were 'very worried' about the situation in Henan Province, China. Our study may remind policymakers to consider factors including age, educational status, type of teacher, school location, source of information on COVID-19, attention level, anxiety level, fear level and behaviour status to alleviate worry.
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Affiliation(s)
- Yanqing Wang
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Quanman Li
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Clifford Silver Tarimo
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Department of Science and Laboratory Technology, Dar es Salaam Institute of Technology, Dar es Salaam, Tanzania
| | - Cuiping Wu
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Yudong Miao
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Jian Wu
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
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Hirschprung RS, Hajaj C. Prediction model for the spread of the COVID-19 outbreak in the global environment. Heliyon 2021; 7:e07416. [PMID: 34226882 PMCID: PMC8238641 DOI: 10.1016/j.heliyon.2021.e07416] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/31/2021] [Accepted: 06/23/2021] [Indexed: 01/22/2023] Open
Abstract
COVID-19 has long become a worldwide pandemic. It is responsible for the death of over two million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy, we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.
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Affiliation(s)
- Ron S. Hirschprung
- Department of Industrial Engineering and Management, Ariel University, Israel
| | - Chen Hajaj
- Department of Industrial Engineering and Management, Ariel University, Israel
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24
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Zheng X, Guo Y, Yang H, Luo L, Ya B, Xu H, Xue Z, Li Q, Shi J, Bi J, Ma W, Wang P. A Cross-Sectional Study on Mental Health Problems of Medical and Nonmedical Students in Shandong During the COVID-19 Epidemic Recovery Period. Front Psychiatry 2021; 12:680202. [PMID: 34177663 PMCID: PMC8226021 DOI: 10.3389/fpsyt.2021.680202] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/17/2021] [Indexed: 01/12/2023] Open
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has resulted in a plethora of psychological problems worldwide since its onset in December 2019. In the upheaval period, compared with medical college students, nonmedical students' psychological state deserves additional concern due to their lack of medical knowledge. Although the epidemic in China has been largely controlled for several months, the mental health problems resulting from the COVID-19 epidemic persist to this day. In this study, we assessed the mental health problems and associated risk factors experienced by nonmedical vs. medical college students in universities of Shandong Province during the COVID-19 epidemic recovery period. Methods: An online survey was conducted over the period from 17 to 19 December 2020. A total of 954 Chinese college students (486 nonmedical and 468 medical students) from three universities of Shandong Province participated in the survey. Mental health variables were assessed with use of Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Insomnia Severity Index (ISI). Results: Compared with medical students, nonmedical college students had higher prevalence rates of depression (53.9 vs. 46.4%; p = 0.020) and insomnia (28.0 vs. 22.4%, p = 0.049), as well as higher total scores on the PHQ-9 (p = 0.03) and ISI (p < 0.01). Among nonmedical college students, being female and native of non-Shandong were risk factors for anxiety and depression (p < 0.01), while only native of non-Shandong for insomnia (p < 0.01). Among medical students, age (p < 0.01) and living in rural areas (p = 0.04) were risk factors for depression, while only age (p < 0.05) was a risk factor for anxiety and insomnia. Conclusion: Nonmedical college students in the universities of Shandong Province had more mental health problems and more risk factors for developing them during the COVID-19 epidemic recovery period than medical students. These nonmedical students require additional attention and recovery programs to alleviate the increased incidence of psychological problems related to COVID-19.
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Affiliation(s)
- Xiaolei Zheng
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuji Guo
- Department of Histology and Embryology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui Yang
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Liyan Luo
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bailiu Ya
- Department of Physiology, School of Basic Medical, Jining Medical University, Jining, China
| | - Hong Xu
- Department of Philosophy School of Marxism, Dezhou University, Dezhou, China
| | - Zhiwei Xue
- Department of Clinical Medicine School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qing Li
- Department of Clinical Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jiale Shi
- Department of Basic Medical School, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jianzhong Bi
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wen Ma
- Center for Clinical Neurolinguistics, School of Foreign Languages and Literature, Shandong University, Jinan, China
| | - Ping Wang
- Department of Neurology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Lin S, Lin R, Yan N, Huang J. Traffic control and social distancing evidence from COVID-19 in China. PLoS One 2021; 16:e0252300. [PMID: 34077487 PMCID: PMC8171991 DOI: 10.1371/journal.pone.0252300] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/12/2021] [Indexed: 01/23/2023] Open
Abstract
We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city’s situation, in order to achieve a better epidemic prevention and control.
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Affiliation(s)
- Shanlang Lin
- School of Economics and Management, Tongji University, Shanghai, China
| | - Ruofei Lin
- School of Economics and Management, Tongji University, Shanghai, China
| | - Na Yan
- School of Economics and Management, Tongji University, Shanghai, China
| | - Junpei Huang
- School of Economics and Management, Tongji University, Shanghai, China
- * E-mail:
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26
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Huo X, Chen J, Ruan S. Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study. BMC Infect Dis 2021; 21:476. [PMID: 34034662 PMCID: PMC8148404 DOI: 10.1186/s12879-021-06078-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 04/15/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. METHODS By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. RESULTS We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. CONCLUSIONS We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.
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Affiliation(s)
- Xi Huo
- Department of Mathematics, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33146, USA
| | - Jing Chen
- Department of Mathematics, Nova Southeastern University, 3301 College Ave, Fort Lauderdale, FL, 33314, USA
| | - Shigui Ruan
- Department of Mathematics, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33146, USA.
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL, 33316, USA.
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27
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Djordjevic M, Rodic A, Salom I, Zigic D, Milicevic O, Ilic B, Djordjevic M. A systems biology approach to COVID-19 progression in population. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:291-314. [PMID: 34340771 PMCID: PMC8092812 DOI: 10.1016/bs.apcsb.2021.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.
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Affiliation(s)
| | - Andjela Rodic
- Computational Systems Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | - Igor Salom
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Dusan Zigic
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Ognjen Milicevic
- Department for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Bojana Ilic
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Marko Djordjevic
- Computational Systems Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia.
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28
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Perez-Fuentes MDC, Molero Jurado MDM, Martos Martinez A, Simon Marquez MDM, Gazquez Linares JJ. Mood and Affective Balance of Spaniards Confined by COVID-19: A Cross-Sectional Study. Int J Psychol Res (Medellin) 2021; 14:55-65. [PMID: 34306579 PMCID: PMC8297570 DOI: 10.21500/20112084.4765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/05/2020] [Accepted: 11/04/2020] [Indexed: 12/28/2022] Open
Abstract
The aim of this study was to analyze the mood and affective balance of Spaniards in quarantine and determine the predictive role of sociodemographic variables and mood on the negative affective balance. This cross-sectional study was carried out with a sample of 1014 Spanish adults, 67.2% were women and 32.8% men. The age ranged from 17 to 76. The instruments used were the Mood Assessment Scale and the Affective Balance Scale, which were implemented as a CAWI survey (Computer Aided Web Interviewing). Results showed that age correlated negatively with Sadness-Depression, Anxiety, and Happiness. Women had more Sadness-Depression, Anxiety, and negative affect, while men showed more Happiness and higher positive affect. Thus, the risk of a negative affective balance during confinement was greater for women and those who showed an emotional state marked by sadness-depression and anxiety, while older age and higher scores in happiness were associated with lower risk. In conclusion, knowing which groups are at risk of emotional and affective alteration can facilitate the detection and prevention of later disorders, such as severe stress and posttraumatic stress disorder, avoiding their generalized presence, and becoming a new public health problem derived from COVID-19.
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Affiliation(s)
- Maria Del Carmen Perez-Fuentes
- Facultad de Educación, Departamento de Psicología, Universidad de Almería. Spain. Universidad de Almería Universidad de Almería Spain
| | - Maria Del Mar Molero Jurado
- Facultad de Educación, Departamento de Psicología, Universidad de Almería. Spain. Universidad de Almería Universidad de Almería Spain
| | - Africa Martos Martinez
- Facultad de Educación, Departamento de Psicología, Universidad de Almería. Spain. Universidad de Almería Universidad de Almería Spain
| | - Maria Del Mar Simon Marquez
- Facultad de Educación, Departamento de Psicología, Universidad de Almería. Spain. Universidad de Almería Universidad de Almería Spain
| | - Jose Jesus Gazquez Linares
- Facultad de Educación, Departamento de Psicología, Universidad de Almería. Spain. Universidad de Almería Universidad de Almería Spain.,Universidad Autónoma de Chile. Chile. Universidad Autónoma de Chile Universidad Autónoma de Chile Chile
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Bürger R, Chowell G, Lara-Díaz LY. Measuring differences between phenomenological growth models applied to epidemiology. Math Biosci 2021; 334:108558. [PMID: 33571534 PMCID: PMC8054577 DOI: 10.1016/j.mbs.2021.108558] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 01/26/2021] [Accepted: 01/31/2021] [Indexed: 12/16/2022]
Abstract
Phenomenological growth models (PGMs) provide a framework for characterizing epidemic trajectories, estimating key transmission parameters, gaining insight into the contribution of various transmission pathways, and providing long-term and short-term forecasts. Such models only require a small number of parameters to describe epidemic growth patterns. They can be expressed by an ordinary differential equation (ODE) of the type C'(t)=f(t,C;Θ) for t>0, C(0)=C0, where t is time, C(t) is the total size of the epidemic (the cumulative number of cases) at time t, C0 is the initial number of cases, f is a model-specific incidence function, and Θ is a vector of parameters. The current COVID-19 pandemic is a scenario for which such models are of obvious importance. In Bürger et al. (2019) it is demonstrated that some PGMs are better at fitting data of specific epidemic outbreaks than others even when the models have the same number of parameters. This situation motivates the need to measure differences in the dynamics that two different models are capable of generating. The present work contributes to a systematic study of differences between PGMs and how these may explain the ability of certain models to provide a better fit to data than others. To this end a so-called empirical directed distance (EDD) is defined to describe the differences in the dynamics between different dynamic models. The EDD of one PGM from another one quantifies how well the former fits data generated by the latter. The concept of EDD is, however, not symmetric in the usual sense of metric spaces. The procedure of calculating EDDs is applied to synthetic data and real data from influenza, Ebola, and COVID-19 outbreaks.
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Affiliation(s)
- Raimund Bürger
- CI2MA and Departamento de Ingeniería Matemática, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Casilla 160-C, Concepción, Chile
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA,Simon A. Levin Mathematical and Computational Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, USA,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Leidy Yissedt Lara-Díaz
- CI2MA and Departamento de Ingeniería Matemática, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Casilla 160-C, Concepción, Chile,Corresponding author
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30
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Choi W, Shim E. Optimal strategies for social distancing and testing to control COVID-19. J Theor Biol 2021; 512:110568. [PMID: 33385403 PMCID: PMC7772089 DOI: 10.1016/j.jtbi.2020.110568] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/16/2022]
Abstract
The coronavirus disease (COVID-19) has infected more than 79 million individuals, with 1.7 million deaths worldwide. Several countries have implemented social distancing and testing policies with contact tracing as a measure to flatten the curve of the ongoing pandemic. Optimizing these control measures is urgent given the substantial societal and economic impacts associated with infection and interventions. To determine the optimal social distancing and testing strategies, we developed a mathematical model of COVID-19 transmission and applied optimal control theory, identifying the best approach to reduce the epidemiological burden of COVID-19 at a minimal cost. The results demonstrate that testing as a standalone optimal strategy does not have a significant effect on the final size of an epidemic, but it would delay the peak of the pandemic. If social distancing is the sole control strategy, it would be optimal to gradually increase the level of social distancing as the incidence curve of COVID-19 grows, and relax the measures after the curve has reached its peak. Compared with a single strategy, combined social distancing and testing strategies are demonstrated to be more efficient at reducing the disease burden, and they can delay the peak of the disease. To optimize these strategies, testing should be maintained at a maximum level in the early phases and after the peak of the epidemic, whereas social distancing should be intensified when the prevalence of the disease is greater than 15%. Accordingly, public health agencies should implement early testing and switch to social distancing when the incidence level begins to increase. After the peak of the pandemic, it would be optimal to gradually relax social distancing and switch back to testing.
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Affiliation(s)
- Wongyeong Choi
- Department of Mathematics, Soongsil University, 369 Sangdoro, Dongjak-Gu, Seoul 06978, Republic of Korea
| | - Eunha Shim
- Department of Mathematics, Soongsil University, 369 Sangdoro, Dongjak-Gu, Seoul 06978, Republic of Korea.
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31
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Shaw CL, Kennedy DA. What the reproductive number R 0 can and cannot tell us about COVID-19 dynamics. Theor Popul Biol 2021; 137:2-9. [PMID: 33417839 PMCID: PMC7785280 DOI: 10.1016/j.tpb.2020.12.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/02/2020] [Accepted: 12/17/2020] [Indexed: 12/18/2022]
Abstract
The reproductive number R (or R0, the initial reproductive number in an immune-naïve population) has long been successfully used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some misconceptions about the predictive ability of the reproductive number, focusing on how it changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R and R0 facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.
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Affiliation(s)
- Clara L Shaw
- Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, United States of America.
| | - David A Kennedy
- Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, United States of America.
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Menkir TF, Chin T, Hay JA, Surface ED, De Salazar PM, Buckee CO, Watts A, Khan K, Sherbo R, Yan AWC, Mina MJ, Lipsitch M, Niehus R. Estimating internationally imported cases during the early COVID-19 pandemic. Nat Commun 2021. [PMID: 33436574 DOI: 10.1101/2020.03.23.20038331v3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 - 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts.
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Affiliation(s)
- Tigist F Menkir
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Taylor Chin
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - James A Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Erik D Surface
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Pablo M De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | | | - Kamran Khan
- BlueDot, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
| | | | - Ada W C Yan
- Section of Immunology of Infection, Department of Infectious Disease, Imperial College London, London, UK
| | - Michael J Mina
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Rene Niehus
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
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Menkir TF, Chin T, Hay JA, Surface ED, De Salazar PM, Buckee CO, Watts A, Khan K, Sherbo R, Yan AWC, Mina MJ, Lipsitch M, Niehus R. Estimating internationally imported cases during the early COVID-19 pandemic. Nat Commun 2021; 12:311. [PMID: 33436574 PMCID: PMC7804934 DOI: 10.1038/s41467-020-20219-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/13/2020] [Indexed: 01/08/2023] Open
Abstract
Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 - 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts.
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Affiliation(s)
- Tigist F Menkir
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Taylor Chin
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - James A Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Erik D Surface
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Pablo M De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | | | - Kamran Khan
- BlueDot, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
| | | | - Ada W C Yan
- Section of Immunology of Infection, Department of Infectious Disease, Imperial College London, London, UK
| | - Michael J Mina
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Rene Niehus
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
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Tariq A, Undurraga EA, Laborde CC, Vogt-Geisse K, Luo R, Rothenberg R, Chowell G. Transmission dynamics and control of COVID-19 in Chile, March-October, 2020. PLoS Negl Trop Dis 2021; 15:e0009070. [PMID: 33481804 PMCID: PMC7857594 DOI: 10.1371/journal.pntd.0009070] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 02/03/2021] [Accepted: 12/13/2020] [Indexed: 12/22/2022] Open
Abstract
Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.
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Affiliation(s)
- Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
| | - Eduardo A. Undurraga
- Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Region Metropolitana, Chile
- Millennium Initiative for Collaborative Research in Bacterial Resistance (MICROB-R), Santiago, Region Metropolitana, Chile
- Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Region Metropolitana, Chile
| | - Carla Castillo Laborde
- Centro de Epidemiología y Políticas de Salud, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Region Metropolitana, Chile
| | - Katia Vogt-Geisse
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Region Metropolitana, Chile
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
| | - Richard Rothenberg
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
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Li Y, Liang M, Yin X, Liu X, Hao M, Hu Z, Wang Y, Jin L. COVID-19 epidemic outside China: 34 founders and exponential growth. J Investig Med 2021; 69:52-55. [PMID: 33023916 PMCID: PMC7803885 DOI: 10.1136/jim-2020-001491] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2020] [Indexed: 11/05/2022]
Abstract
COVID-19 raised tension both within China and internationally. Here, we used mathematical modeling to predict the trend of patient diagnosis outside China in future, with the aim of easing anxiety regarding the emergent situation. According to all diagnosis number from WHO website and combining with the transmission mode of infectious diseases, the mathematical model was fitted to predict future trend of outbreak. Daily diagnosis numbers from countries outside China were downloaded from WHO situation reports. The data used for this analysis were collected from January 21, 2020 and currently end at February 28, 2020. A simple regression model was developed based on these numbers, as follows: [Formula: see text], where [Formula: see text] is the total diagnosed patient till the i-th day and t=1 at February 1, 2020. Based on this model, we estimate that there were approximately 34 undetected founder patients at the beginning of the spread of COVID-19 outside China. The global trend was approximately exponential, with an increase rate of 10-fold every 19 days. Through establishment of this model, we call for worldwide strong public health actions, with reference to the experiences learned from China and Singapore.
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Affiliation(s)
- Yi Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
| | - Meng Liang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Xianhong Yin
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Xiaoyu Liu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Meng Hao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Zixin Hu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Li Jin
- Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
- Research Institute of Data Sciences, Fudan University, Shanghai, China
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Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model. Infect Dis Model 2020; 6:212-221. [PMID: 33398249 PMCID: PMC7773318 DOI: 10.1016/j.idm.2020.12.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/22/2020] [Indexed: 01/06/2023] Open
Abstract
To estimate the size of the novel coronavirus (COVID-19) outbreak in the early stage in Italy, this paper introduces the cumulated and weighted average daily growth rate (WR) to evaluate an epidemic curve. On the basis of an exponential decay model (EDM), we provide estimations of the WR in four-time intervals from February 27 to April 07, 2020. By calibrating the parameters of the EDM to the reported data in Hubei Province of China, we also attempt to forecast the evolution of the outbreak. We compare the EDM applied to WR and the Gompertz model, which is based on exponential decay and is often used to estimate cumulative events. Specifically, we assess the performance of each model to short-term forecast of the epidemic, and to predict the final epidemic size. Based on the official counts for confirmed cases, the model applied to data from February 27 until the 17th of March estimate that the cumulative number of infected in Italy could reach 131,280 (with a credibility interval 71,415-263,501) by April 25 (credibility interval April 12 to May 3). With the data available until the 24st of March the peak date should be reached on May 3 (April 23 to May 23) with 197,179 cumulative infections expected (130,033–315,269); with data available until the 31st of March the peak should be reached on May 4 (April 25 to May 18) with 202,210 cumulative infections expected (155.235–270,737); with data available until the 07st of April the peak should be reached on May 3 (April 26 to May 11) with 191,586 (160,861-232,023) cumulative infections expected. Based on the average mean absolute percentage error (MAPE), cumulated infections forecasts provided by the EDM applied to WR performed better across all scenarios than the Gompertz model. An exponential decay model applied to the cumulated and weighted average daily growth rate appears to be useful in estimating the number of cases and peak of the COVID-19 outbreak in Italy and the model was more reliable in the exponential growth phase.
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SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17249290. [PMID: 33322572 PMCID: PMC7764429 DOI: 10.3390/ijerph17249290] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 01/20/2023]
Abstract
COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).
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Bility MT, Agarwal Y, Ho S, Castronova I, Beatty C, Biradar S, Narala V, Periyapatna N, Chen Y, Nachega J. WITHDRAWN: Can Traditional Chinese Medicine provide insights into controlling the COVID-19 pandemic: Serpentinization-induced lithospheric long-wavelength magnetic anomalies in Proterozoic bedrocks in a weakened geomagnetic field mediate the aberrant transformation of biogenic molecules in COVID-19 via magnetic catalysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020:142830. [PMID: 33071142 PMCID: PMC7543923 DOI: 10.1016/j.scitotenv.2020.142830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/26/2020] [Accepted: 09/27/2020] [Indexed: 06/11/2023]
Abstract
This article has been withdrawn at the request of the authors and the editors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
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Affiliation(s)
- Moses Turkle Bility
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America.
| | - Yash Agarwal
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Sara Ho
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Isabella Castronova
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Cole Beatty
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Shivkumar Biradar
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Vanshika Narala
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Nivitha Periyapatna
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Yue Chen
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
| | - Jean Nachega
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, United States of America
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Mohsen A, Alarabi A. The impact of community containment implementation timing on the spread of COVID-19: A simulation study. F1000Res 2020; 9:452. [PMID: 32913638 PMCID: PMC7463296 DOI: 10.12688/f1000research.24156.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/21/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Community containment is one of the common methods used to mitigate infectious disease outbreaks. The effectiveness of such a method depends on how strictly it is applied and the timing of its implementation. An early start and being strict is very effective; however, at the same time, it impacts freedom and economic opportunity. Here we created a simulation model to understand the effect of the starting day of community containment on the final outcome, that is, the number of those infected, hospitalized and those that died, as we followed the dynamics of COVID-19 pandemic. Methods: We used a stochastic recursive simulation method to apply disease outbreak dynamics measures of COVID-19 as an example to simulate disease spread. Parameters are allowed to be randomly assigned between higher and lower values obtained from published COVID-19 literature. Results: We simulated the dynamics of COVID-19 spread, calculated the number of active infections, hospitalizations and deaths as the outcome of our simulation and compared these results with real world data. We also represented the details of the spread in a network graph structure, and shared the code for the simulation model to be used for examining other variables. Conclusions: Early implementation of community containment has a big impact on the final outcome of an outbreak.
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Affiliation(s)
- Attayeb Mohsen
- Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research (ArCHER). National Institutes of Biomedical Innovation, Health and Nutrition., Ibaraki city, Osaka, 567-0085, Japan
| | - Ahmed Alarabi
- Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M University, Kingsville, Texas, 78363, USA
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Siraj A, Worku A, Berhane K, Aregawi M, Eshetu M, Mirkuzie A, Berhane Y, Siraj D. Early estimates of COVID-19 infections in small, medium and large population clusters. BMJ Glob Health 2020; 5:e003055. [PMID: 32948617 PMCID: PMC7503195 DOI: 10.1136/bmjgh-2020-003055] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/05/2020] [Accepted: 08/09/2020] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Since its emergence in late December 2019, COVID-19 has rapidly developed into a pandemic in mid of March with many countries suffering heavy human loss and declaring emergency conditions to contain its spread. The impact of the disease, while it has been relatively low in the sub-Saharan Africa (SSA) as of May 2020, is feared to be potentially devastating given the less developed and fragmented healthcare system in the continent. In addition, most emergency measures practised may not be effective due to their limited affordability as well as the communal way people in SSA live in relative isolation in clusters of large as well as smaller population centres. METHODS To address the acute need for estimates of the potential impacts of the disease once it sweeps through the African region, we developed a process-based model with key parameters obtained from recent studies, taking local context into consideration. We further used the model to estimate the number of infections within a year of sustained local transmissions under scenarios that cover different population sizes, urban status, effectiveness and coverage of social distancing, contact tracing and usage of cloth face mask. RESULTS We showed that when implemented early, 50% coverage of contact tracing and face mask, with 33% effective social distancing policies can bringing the epidemic to a manageable level for all population sizes and settings we assessed. Relaxing of social distancing in urban settings from 33% to 25% could be matched by introduction and maintenance of face mask use at 43%. CONCLUSIONS In SSA countries with limited healthcare workforce, hospital resources and intensive care units, a robust system of social distancing, contact tracing and face mask use could yield in outcomes that prevent several millions of infections and thousands of deaths across the continent.
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Affiliation(s)
- Amir Siraj
- Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
- Notre Dame Environmental Change Initiative, University of Notre Dame, Notre Dame, Indiana, USA
| | - Alemayehu Worku
- Department of Preventive Medicine, Addis Ababa University College of Health Sciences, Addis Ababa, Ethiopia
| | - Kiros Berhane
- Biostatistics, Columbia University, New York, New York, USA
| | - Maru Aregawi
- Global Malaria Program, World Health Organization, Geneve, Switzerland
| | - Munir Eshetu
- COVID-19 and Essential Health Care, Ethiopia Ministry of Health, Addis Ababa, Ethiopia
| | - Alemnesh Mirkuzie
- National Data Managment Center for Health, Ethiopian Public Health Institute, Addis Ababa, Ethiopia
| | - Yemane Berhane
- Department of Epidemiology, Addis Continental Institute of Public Health, Addis Ababa, Ethiopia
| | - Dawd Siraj
- Division of Infectious Diseases, University of Wisconsin-Madison, Madison, Wisconsin, USA
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41
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Halasa T, Græsbøll K, Denwood M, Christensen LE, Kirkeby C. Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread. Front Vet Sci 2020; 7:513. [PMID: 33062646 PMCID: PMC7477293 DOI: 10.3389/fvets.2020.00513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 07/06/2020] [Indexed: 01/09/2023] Open
Abstract
The worldwide outbreak of Sars-CoV-2 resulted in modelers from diverse fields being called upon to help predict the spread of the disease, resulting in many new collaborations between different institutions. We here present our experience with bringing our skills as veterinary disease modelers to bear on the field of human epidemiology, building models as tools for decision makers, and bridging the gap between the medical and veterinary fields. We describe and compare the key steps taken in modeling the Sars-CoV-2 outbreak: criteria for model choices, model structure, contact structure between individuals, transmission parameters, data availability, model validation, and disease management. Finally, we address how to improve on the contingency infrastructure available for Sars-CoV-2.
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Affiliation(s)
- Tariq Halasa
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Kaare Græsbøll
- Department of Applied Mathematics and Computer Sciences, Technical University of Denmark, Lyngby, Denmark
| | - Matthew Denwood
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Lasse Engbo Christensen
- Department of Applied Mathematics and Computer Sciences, Technical University of Denmark, Lyngby, Denmark
| | - Carsten Kirkeby
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
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Diaz Perez FJ, Chinarro D, Otin RP, Martín RD, Diaz M, Mouhaffel AG. Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel. Rambam Maimonides Med J 2020; 11:RMMJ.10413. [PMID: 32792047 PMCID: PMC7426552 DOI: 10.5041/rmmj.10413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21-May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R2>0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries' health care systems do not collapse.
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Affiliation(s)
| | - David Chinarro
- Faculty of Health Sciences, University San Jorge, Zaragoza, Spain
| | - Rosa Pino Otin
- Faculty of Health Sciences, University San Jorge, Zaragoza, Spain
| | | | - Moises Diaz
- Department of Computer Science, University Atlántico Medio, Las Palmas, Spain
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Eshrati B, Baradaran HR, Erfanpoor S, Mohazzab A, Moradi Y. Investigating the factors affecting the survival rate in patients with COVID-19: A retrospective cohort study. Med J Islam Repub Iran 2020; 34:88. [PMID: 33306063 PMCID: PMC7711041 DOI: 10.34171/mjiri.34.88] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Indexed: 12/12/2022] Open
Abstract
Background: As hospitalized patients with COVID-19, especially those who are admitted to ICU or die afterwards, generally have comorbidities, the aim of this study was to determine the factors affecting the survival rate of COVID-19 patients in Iran using a retrospective cohort. Methods: This retrospective cohort study was conducted on patients with COVID-19 who referred to medical centers under the supervision of Iran University of Medical Sciences, Tehran, Iran, from February 22 to March 25, 2020. The final date of follow-up was April 19, 2020. All consecutive inpatients with laboratory-confirmed COVID-19 were included in this study. Clinical laboratory, radiological, treatment, and demographic data were collected and analyzed. The associations among gender, immune disease, diabetes, liver disease, cardiovascular disease, kidney disease, chronic pulmonary disease, cancer, chronic nervous disease, type of treatment, and risk of death were analyzed. The Kaplan-Meier and Log-rank tests were used to estimate survival rate and compare survival rates, respectively. Results: The total number of deaths or desired event in the study was 329 (10.3%).The risk of death in the age groups of 50-60 years, 60-70 years, and >70 years compared to the 30-40 age group was 2.17 (95% CI: 1.03, 4.55; p: 0.040); 3.72 (95 % CI: 1.80, 7.68; p: 0.001) and 5.09 (95 % CI: 2.49, 10.40; p: 0.001), respectively. The results showed men had 11.5% more risk of deaths than women (HR: 1.11; 95 % CI: 0.89, 1.39; p: 0.341). Kidney disease increased the risk of death by 52.3% in these patients, which was not statistically significant (HR: 1.78; 95 % CI: 1.04, 3.04; p: 0.035). Also, chronic pulmonary diseases and diabetes increased the risk of death in COVID-19 patients by 89.5% and 41.3% compared to COVID-19 patients without chronic pulmonary diseases and diabetes [(HR: 1.89; 95 % CI: 1.17, 3.04; p: 0.008), (HR: 1.41; 95 % CI: 1.01, 1.96; p: 0.038)]. Conclusion: Based on the results of this study, more attention and care should be paid to COVID-19 patients with underlying diseases, such as chronic obstructive pulmonary disease, diabetes, and kidney disease to reduce the number of deaths.
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Affiliation(s)
- Babak Eshrati
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Baradaran
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran
- Ageing Clinical & Experimental Research Team, Institute of Applied Health Sciences, University of Aberdeen, UK
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Saeed Erfanpoor
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Mohazzab
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Yousef Moradi
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
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Munayco C, Chowell G, Tariq A, Undurraga EA, Mizumoto K. Risk of death by age and gender from CoVID-19 in Peru, March-May, 2020. Aging (Albany NY) 2020; 12:13869-13881. [PMID: 32692724 PMCID: PMC7425445 DOI: 10.18632/aging.103687] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 06/29/2020] [Indexed: 01/08/2023]
Abstract
Peru implemented strict social distancing measures during the early phase of the epidemic and is now experiencing one of the largest CoVID-19 epidemics in Latin America. Estimates of disease severity are an essential indicator to inform policy decisions about the intensity and duration of interventions needed to mitigate the outbreak. Here we derive delay-adjusted case fatality risks (aCFR) of CoVID-19 in a middle-income country in South America.We utilize government-reported time series of CoVID-19 cases and deaths in Peru stratified by age group and gender.As of May 25, 2020, we estimate the aCFR for men and women at 10.8% (95%CrI: 10.5-11.1%) and 6.5% (95%CrI: 6.2-6.8%), respectively, whereas the overall aCFR was estimated at 9.1% (95%CrI: 8.9-9.3%). Our results show that senior individuals have been the most severely affected by CoVID-19, particularly men, with an aCFR of nearly 60% for those aged 80- years. We also found that men have a significantly higher cumulative morbidity ratio across most age groups (proportion test, p-value< 0.001), with the exception of those aged 0-9 years.The ongoing CoVID-19 pandemic is generating a substantial mortality burden in Peru. Senior individuals, especially those older than 70 years, are being disproportionately affected by the CoVID-19 pandemic.
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Affiliation(s)
- Cesar Munayco
- Centro Nacional de Epidemiología, Prevención y Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA
| | - Eduardo A. Undurraga
- Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Region Metropolitana, Chile
- Millennium Initiative for Collaborative Research in Bacterial Resistance, MICROB-R, Chile
| | - Kenji Mizumoto
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA
- Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University Yoshida-Nakaadachi-cho, Sakyo-ku, Kyoto, Japan
- Hakubi Center for Advanced Research, Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto, Japan
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Salje H, Tran Kiem C, Lefrancq N, Courtejoie N, Bosetti P, Paireau J, Andronico A, Hozé N, Richet J, Dubost CL, Le Strat Y, Lessler J, Levy-Bruhl D, Fontanet A, Opatowski L, Boelle PY, Cauchemez S. Estimating the burden of SARS-CoV-2 in France. Science 2020; 369:208-211. [PMID: 32404476 PMCID: PMC7223792 DOI: 10.1126/science.abc3517] [Citation(s) in RCA: 671] [Impact Index Per Article: 134.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 05/11/2020] [Indexed: 12/11/2022]
Abstract
France has been heavily affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and went into lockdown on 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find that 2.9% of infected individuals are hospitalized and 0.5% of those infected die (95% credible interval: 0.3 to 0.9%), ranging from 0.001% in those under 20 years of age to 8.3% in those 80 years of age or older. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project that 3.5 million people (range: 2.1 million to 6.0 million), or 5.3% of the population (range: 3.3 to 9.3%), will have been infected. Population immunity appears to be insufficient to avoid a second wave if all control measures are released at the end of the lockdown.
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Affiliation(s)
- Henrik Salje
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Cécile Tran Kiem
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Collège Doctoral, Sorbonne Université, Paris, France
| | - Noémie Lefrancq
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | | | - Paolo Bosetti
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Juliette Paireau
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Santé Publique France, French National Public Health Agency, Saint-Maurice, France
| | - Alessio Andronico
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Nathanaël Hozé
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Jehanne Richet
- DREES, Ministère des Solidarités et de la Santé, Paris, France
| | | | - Yann Le Strat
- Santé Publique France, French National Public Health Agency, Saint-Maurice, France
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daniel Levy-Bruhl
- Santé Publique France, French National Public Health Agency, Saint-Maurice, France
| | - Arnaud Fontanet
- Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France
- PACRI Unit, Conservatoire National des Arts et Métiers, Paris, France
| | - Lulla Opatowski
- Epidemiology and Modelling of Antibiotic Evasion Unit, Institut Pasteur, Paris, France
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France
| | - Pierre-Yves Boelle
- Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Université, INSERM, Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
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Killeen GF, Kiware SS. Why lockdown? Why national unity? Why global solidarity? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the 2019 novel coronavirus. Infect Dis Model 2020; 5:442-458. [PMID: 32691016 PMCID: PMC7342051 DOI: 10.1016/j.idm.2020.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 01/08/2023] Open
Abstract
As every country in the world struggles with the ongoing COVID-19 pandemic, it is essential that as many people as possible understand the epidemic containment, elimination and exclusion strategies required to tackle it. Simplified arithmetic models of COVID-19 transmission, control and elimination are presented in user-friendly Shiny and Excel formats that allow non-specialists to explore, query, critique and understand the containment decisions facing their country and the world at large. Although the predictive model is broadly applicable, the simulations presented are based on parameter values representative of the United Republic of Tanzania, which is still early enough in its epidemic cycle and response to avert a national catastrophe. The predictions of these models illustrate (1) why ambitious lock-down interventions to crush the curve represent the only realistic way for individual countries to contain their national-level epidemics before they turn into outright catastrophes, (2) why these need to be implemented so early, so stringently and for such extended periods, (3) why high prevalence of other pathogens causing similar symptoms to mild COVID-19 precludes the use of contact tracing as a substitute for lock down interventions to contain and eliminate epidemics, (4) why partial containment strategies intended to merely flatten the curve, by maintaining epidemics at manageably low levels, are grossly unrealistic, and (5) why local elimination may only be sustained after lock down ends if imported cases are comprehensively excluded, so international co-operation to conditionally re-open trade and travel between countries certified as free of COVID-19 represents the best strategy for motivating progress towards pandemic eradication at global level. The three sequential goals that every country needs to emphatically embrace are contain, eliminate and exclude. As recently emphasized by the World Health Organization, success will require widespread genuine national unity and unprecedented global solidarity.
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Affiliation(s)
- Gerry F Killeen
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Morogoro, United Republic of Tanzania
- School of Biological, Earth & Environmental Sciences and Environmental Research Institute, University College Cork, Ireland
| | - Samson S Kiware
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Morogoro, United Republic of Tanzania
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Munayco CV, Tariq A, Rothenberg R, Soto-Cabezas GG, Reyes MF, Valle A, Rojas-Mezarina L, Cabezas C, Loayza M, Chowell G. Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29 th-March 30 th, 2020. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.04.30.20077594. [PMID: 32511517 PMCID: PMC7273285 DOI: 10.1101/2020.04.30.20077594] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.
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Affiliation(s)
- César V. Munayco
- Centro Nacional de Epidemiología, Prevencióny Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Richard Rothenberg
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Gabriela G Soto-Cabezas
- Centro Nacional de Epidemiología, Prevencióny Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | - Mary F. Reyes
- Centro Nacional de Epidemiología, Prevencióny Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | - Andree Valle
- Centro Nacional de Epidemiología, Prevencióny Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | | | - César Cabezas
- Instituto Nacional de Salud, Peruvian Ministry of Health, Lima, Peru
| | - Manuel Loayza
- Centro Nacional de Epidemiología, Prevencióny Control de Enfermedades, Peruvian Ministry of Health, Lima, Peru
| | | | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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