Due to the need to hospitalize a large number of patients during the COVID-19 pandemic, the psychological conditions of hospitalized patients were often overlooked. This study focuses on the qualitative analysis of the subjective experiences of patients with a severe COVID-19 disease in Slovakia during hospitalization. A total of 27 Slovak participants (11 men and 16 women, mean age 57.10 years) who were hospitalized with severe COVID-19 disease were interviewed about their subjective experiences during hospitalization. The data was analysed using thematic analysis. The main themes included negative emotions such as distress, discomfort with the illness, discomfort with the medical environment and helplessness. The main sources of distress were the sense of isolation, witnessing the death of another patient, own death concerns, and concerns for others. Sources and strategies used by patients to improve their mental state included interpersonal resources such as contact with relatives and friends, instrumental support from them, mutual help among patients and professional psychological support. Interpersonal resources included optimism, hope, religion and spirituality, recollection of significant others, and reconciliation with the possibility of death. The results have implications for medical staff as they help them to understand the psychological state of COVID-19 patients during hospitalization and can inform psychological interventions to improve hospital care for these patients.

We aim to identify Libyans' knowledge, attitudes, and acceptance regarding the COVID-19 vaccine. A cross-sectional survey was electronically distributed to the Libyan population aged 18 and older between May and September 2023. The questionnaire had three sections: socio-demographics, COVID-19 vaccination and infection, and knowledge and attitudes toward the COVID-19 vaccine. The chi-square test was used to assess the associations. A total of 1,043 respondents completed the questionnaire. Of these, 590 (56.6%) were vaccinated, and 453 (43.4%) were unvaccinated. Only age, educational level, employment status, history of COVID-19 infection, and source of information had a significant association with vaccination status; all shared a p-value <.05. However, Monthly income did not. Regarding knowledge, 63.7% agreed that vaccines in general are an effective way to prevent and control infectious diseases, and 76.6% agreed that they can prevent disease and mortality. However, regarding COVID-19 vaccine, 48.4% agreed that the benefits outweigh the risks. Regarding COVID-19 safety, 40.8% responded that COVID-19 vaccines are only slightly safe or not safe at all. COVID-19 vaccine acceptance was at 57.2%, and only age and source of information were significantly associated. Those who held favorable views were more likely to accept the vaccine, while those who had concerns about safety were more vaccine hesitant. There is a gap between the perception of the COVID-19 vaccine compared to other vaccines among Libyans. Our study revealed that 57.2% of Libyans accept the COVID-19 vaccine. However, only 34% of the Libyan population is vaccinated. A comprehensive health policy is needed.

We propose a new theory of information-based voluntary social distancing in which people's responses to disease prevalence depend on the credibility of reported cases and fatalities and vary locally. We embed this theory into a new pandemic prediction and policy analysis framework that blends compartmental epidemiological/economic models with Machine Learning. We find that lockdown effectiveness varies widely across US States during the early phases of the COVID-19 pandemic. We find that voluntary social distancing is higher in more informed states, and increasing information could have substantially changed social distancing and fatalities.

Our research focuses on local-level estimation of the effective reproductive number, which describes the transmissibility of an infectious disease and represents the average number of individuals one infectious person infects at a given time. The ability to accurately estimate the infectious disease reproductive number in geographically granular regions is critical for disaster planning and resource allocation. However, not all regions have sufficient infectious disease outcome data; this lack of data presents a significant challenge for accurate estimation.

To overcome this challenge, we propose a two-step approach that incorporates existing [Formula: see text] estimation procedures (EpiEstim, EpiFilter, EpiNow2) using data from geographic regions with sufficient data (step 1), into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predict [Formula: see text] in regions with sparse or missing data (step 2). Our flexible framework effectively allows us to implement any existing estimation procedure for [Formula: see text] in regions with coarse or entirely missing data. We perform external validation and a simulation study to evaluate the proposed method and assess its predictive performance.

We applied our method to estimate [Formula: see text]using data from South Carolina (SC) counties and ZIP codes during the first COVID-19 wave ('Wave 1', June 16, 2020 - August 31, 2020) and the second wave ('Wave 2', December 16, 2020 - March 02, 2021). Among the three methods used in the first step, EpiNow2 yielded the highest accuracy of [Formula: see text] prediction in the regions with entirely missing data. Median county-level percentage agreement (PA) was 90.9% (Interquartile Range, IQR: 89.9-92.0%) and 92.5% (IQR: 91.6-93.4%) for Wave 1 and 2, respectively. Median zip code-level PA was 95.2% (IQR: 94.4-95.7%) and 96.5% (IQR: 95.8-97.1%) for Wave 1 and 2, respectively. Using EpiEstim, EpiFilter, and an ensemble-based approach yielded median PA ranging from 81.9 to 90.0%, 87.2-92.1%, and 88.4-90.9%, respectively, across both waves and geographic granularities.

These findings demonstrate that the proposed methodology is a useful tool for small-area estimation of [Formula: see text], as our flexible framework yields high prediction accuracy for regions with coarse or missing data.

Our research focuses on local-level estimation of the effective reproductive number, which describes the transmissibility of an infectious disease and represents the average number of individuals one infectious person infects at a given time. The ability to accurately estimate the infectious disease reproductive number in geographically granular regions is critical for disaster planning and resource allocation. However, not all regions have sufficient infectious disease outcome data; this lack of data presents a significant challenge for accurate estimation.

To overcome this challenge, we propose a two-step approach that incorporates existingR t estimation procedures (EpiEstim, EpiFilter, EpiNow2) using data from geographic regions with sufficient data (step 1), into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predictR t in regions with sparse or missing data (step 2). Our flexible framework effectively allows us to implement any existing estimation procedure forR t in regions with coarse or entirely missing data. We perform external validation and a simulation study to evaluate the proposed method and assess its predictive performance.

We applied our method to estimateR t using data from South Carolina (SC) counties and ZIP codes during the first COVID-19 wave ('Wave 1', June 16, 2020 - August 31, 2020) and the second wave ('Wave 2', December 16, 2020 - March 02, 2021). Among the three methods used in the first step, EpiNow2 yielded the highest accuracy ofR t prediction in the regions with entirely missing data. Median county-level percentage agreement (PA) was 90.9% (Interquartile Range, IQR: 89.9-92.0%) and 92.5% (IQR: 91.6-93.4%) for Wave 1 and 2, respectively. Median zip code-level PA was 95.2% (IQR: 94.4-95.7%) and 96.5% (IQR: 95.8-97.1%) for Wave 1 and 2, respectively. Using EpiEstim, EpiFilter, and an ensemble-based approach yielded median PA ranging from 81.9%-90.0%, 87.2%-92.1%, and 88.4%-90.9%, respectively, across both waves and geographic granularities.

These findings demonstrate that the proposed methodology is a useful tool for small-area estimation ofR t , as our flexible framework yields high prediction accuracy for regions with coarse or missing data.

The global Coronavirus disease (COVID-19) pandemic disrupted healthcare systems, reducing access to medical services. In Bangladesh, strict lockdowns, healthcare worker shortages, and resource diversion further strained the system. Despite these challenges, the impact on inpatient and outpatient service utilisation in Bangladesh remains unaddressed. This study explored the levels of inpatient admissions and outpatient visits in public healthcare facilities before and during COVID-19 pandemic in Bangladesh.

We conducted a cross-sectional secondary analysis of inpatient and outpatient data from all public hospitals collected via District Health Information System, version 2 (DHIS2) from January 2017 to June 2021. Using 2017-2019 as the baseline, we analysed healthcare utilisation indicators (outpatient visits and inpatient admissions) with descriptive and segmented Poisson regression to assess the impact of COVID-19 in 2020 and 2021.

In 2020, outpatient visits and inpatient admissions significantly declined to 34.1 million and 37.5 million, respectively, from 47.6 million and 56.2 million in 2019. Segmented regression analysis confirmed these drops, especially in Dhaka (IRR =  0.62, p < 0.001) and Barisal (IRR =  0.69, p < 0.002) for outpatient visits, and in Dhaka (IRR =  0.64, p < 0.000) and Khulna (IRR =  0.70, p < 0.000) for inpatient admissions. In 2021, most divisions saw an increase in outpatient visit and inpatient admission numbers, with the lowest rebound in Sylhet.

The COVID-19 pandemic significantly reduced Outpatient Department (OPD) visits and Inpatient Department (IPD) admissions in Bangladesh in 2020, with partial recovery in 2021. To ensure sustained access to care, it is crucial to strengthen healthcare facilities and equip healthcare providers to be prepared for future pandemics or emergencies.

Influenza virus, respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are acute respiratory infections (ARIs) that can cause substantial morbidity and mortality among at-risk individuals, including older adults. In this narrative review, we summarize themes identified in the literature regarding the epidemiology, seasonality, immunity after infection, clinical presentation, and transmission for these ARIs, along with the impact of the COVID-19 pandemic on seasonal patterns of influenza and RSV infections, with consideration of data specific to older adults when available. As the older adult population increases globally, it is of paramount importance to fully characterize the true disease burden of ARIs in order to develop appropriate mitigation strategies to minimize their impact in vulnerable populations. Challenges associated with characterizing the burden of these diseases include the shared symptomology and clinical presentation of influenza virus, RSV, and SARS-CoV-2, which complicate accurate diagnosis and highlight the need for improved testing and surveillance practices. To this end, multiple regional, national, and global virologic and disease surveillance systems have been established to provide accurate knowledge of viral epidemiology, support appropriate preparedness and response to potential outbreaks, and help inform prevention strategies to reduce disease severity and transmission. Beyond the burden of acute illness, long-term health consequences can also result from influenza virus, RSV, and SARS-CoV-2 infection. These include cardiovascular and pulmonary complications, worsening of existing chronic conditions, increased frailty, and reduced life expectancy. ARIs among older adults can also place a substantial financial burden on society and healthcare systems. Collectively, the existing data indicate that influenza virus, RSV, and SARS-CoV-2 infections in older adults present a substantial global health challenge, underscoring the need for interventions to improve health outcomes and reduce the disease burden of respiratory illnesses.Graphical abstract and video abstract available for this article.

The novel coronavirus-induced severe acute respiratory syndrome (COVID-19) led to one of the most significant global pandemics of the 21st century, causing substantial challenges for healthcare systems worldwide, including those in Brazil. This study aimed to investigate the demographic and clinical profiles of hospitalized patients in Brazil who had both COVID-19 and Crohn's disease (CD) over a 2-year period.

An epidemiological analysis was conducted using data from Open-Data-SUS. The study focused on describing the demographic characteristics, clinical manifestations, comorbidities, and hospitalization details of patients afflicted with severe acute respiratory syndrome due to COVID-19 and CD, with the aim of predicting mortality risk.

The states of São Paulo, Paraná, and Minas Gerais accounted for 50% of the reported COVID-19 cases. The most affected racial group consisted of individuals who self-declared as mixed race. Common comorbidities included heart disease, diabetes mellitus, and obesity. The age group most affected was 25 to 60 years old, particularly among hospitalized patients with both CD and COVID-19 who ultimately succumbed to the illness. A multivariable analysis was conducted to identify the following significant risk factors for death: (a) the presence of neurological disorder (OR = 6.716; 95% CI = 1.954-23.078), (b) the need for intensive care (OR = 3.348; 95% CI = 1.770-6.335), and (c) the need for invasive mechanical ventilation (OR = 59.017; 95% CI = 19.796-175.944).

There was no discernible gender-based prevalence among hospitalized patients with CD and COVID-19; however, individuals of mixed race were disproportionately affected. The 25 to 60 age group emerged as the most vulnerable demographic group, with high risks of hospitalization and mortality. Moreover, the study highlights the potential for COVID-19 to induce systemic pathologies that may result in long-term degenerative effects and sequelae.

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

CRISPR based nucleic acid detection technology provides a deployable approach to point of care testing. While, there remain challenges limiting its practical applications, such as the need for pre-amplification and the long turnaround time. Here, we present a self-cascade signal amplification method with LwaCas13a and an artificially designed "U" rich RNA of stem-loop structure (URH) for pre-amplification-free ultra-fast and ultra-sensitive point-of-care testing (PASSPORT). The PASSPORT system contains: URH, crRNA targeted the URH, crRNA targeted the interesting RNA, fluorescent RNA reporter and LwaCas13a. The assay realized the detection of 100 copies/mL, within 5 min. The PASSPORT platform was further adopted for the detection of marker gene from SASR-CoV-2 and Severe fever with thrombocytopenia syndrome virus (SFTSV), respectively, and 100% accuracy for the analysis of clinical specimens (100 SASR-CoV-2 specimens and 16 SFTSV specimens) was obtained. Integrated with a lateral flow assay device, this assay could provide an alternative platform for the development of point of care testing (POCT) biosensors. PASSPORT has the potential to enable sensitive, specific, user-friendly, rapid, affordable, equipment-free and point-of-care testing for the purpose of large-scale screening and in case of epidemic outbreak.

Robust testing capacity was necessary for public health agencies to respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 19 (COVID-19) pandemic. As the nation faced the need for robust testing capacity, it became necessary to use all possible resources. In many cases, veterinary diagnostic laboratories rose to meet this demand because these facilities routinely perform high throughput diagnostic testing of large animal populations and are typically familiar with pathogens of high pandemic concern. In this study, we evaluated the impact of veterinary diagnostic laboratories in the United States on SARS-CoV-2 testing. Results of surveys, semi-structured interviews, and analysis of publicly available information showed that veterinary diagnostic laboratories had a substantial impact on human health through population-level testing in the COVID-19 response, supporting timely and informed public health interventions. This success was not without significant hurdles, as many participating veterinary diagnostic laboratories experienced restriction in their response due to difficulties obtaining the Clinical Laboratory Improvement Amendments (CLIA) certification required to conduct human diagnostic testing. Our results point out the importance of reducing hurdles before the next major public health emergency to enhance access to testing resources overall and to ultimately improve population health.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in December 2019 with staggering economic fallout and human suffering. The unique structure of SARS-CoV-2 and its underlying pathogenic mechanism were responsible for the global pandemic. In addition to the direct damage caused by the virus, SARS-CoV-2 triggers an abnormal immune response leading to a cytokine storm, culminating in acute respiratory distress syndrome and other fatal diseases that pose a significant challenge to clinicians. Therefore, potential treatments should focus not only on eliminating the virus but also on alleviating or controlling acute immune/inflammatory responses. Current management strategies for COVID-19 include preventative measures and supportive care, while the role of the host immune/inflammatory response in disease progression has largely been overlooked. Understanding the interaction between SARS-CoV-2 and its receptors, as well as the underlying pathogenesis, has proven to be helpful for disease prevention, early recognition of disease progression, vaccine development, and interventions aimed at reducing immunopathology have been shown to reduce adverse clinical outcomes and improve prognosis. Moreover, several key mutations in the SARS-CoV-2 genome sequence result in an enhanced binding affinity to the host cell receptor, or produce immune escape, leading to either increased virus transmissibility or virulence of variants that carry these mutations. This review characterizes the structural features of SARS-CoV-2, its variants, and their interaction with the immune system, emphasizing the role of dysfunctional immune responses and cytokine storm in disease progression. Additionally, potential therapeutic options are reviewed, providing critical insights into disease management, exploring effective approaches to deal with the public health crises caused by SARS-CoV-2.

The new viral strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are continuously rising, becoming more virulent, and transmissible. Therefore, the development of new antiviral drugs is essential. Due to its significant role in the viral life cycle of SARS-CoV-2, the main protease (Mpro) enzyme is a leading target for antiviral drug design. The Mpro monomer consists of domain DI, DII, and DI-DII interface. Twenty-one conserved water molecules (W4-W24) are occupied at these domains according to multiple crystal structure analyses. The crystal and MD structures reveal the presence of eight conserved water sites in domain DI, DII and remaining in the DI-DII interface. Grid-based inhomogeneous fluid solvation theory (GIST) was employed on MD structures of Mpro native to predict structural and thermodynamic properties of each conserved water site for focusing to identify the specific conserved water molecules that can easily be displaced by proposed ligands. Finally, MD water W13 is emerged as a promising candidate for water mimic drug design due to its low mean interaction energy, loose binding character with the protein, and its involvement in a water-mediated H-bond with catalytic His41 via the interaction Thr25(OG)---W13---W---His41(NE2). In this context, water occupancy, relative interaction energy, entropy, and topologies of W13 are thermodynamically acceptable for the water displacement method. Therefore, the strategic use of W13's geometrical position in the DI domain may be implemented for drug discovery against COVID disease by designing new ligands with appropriately oriented chemical groups to mimic its structural, electronic, and thermodynamic properties.

COVID-19 has become a global pandemic that has affected millions of people worldwide. The disease is caused by the novel coronavirus that was first reported in Wuhan, China, in December 2019. The virus is highly contagious and can spread from person to person through respiratory droplets when an infected person coughs, sneezes, talks, or breathes. The symptoms of COVID-19 include fever, cough, and shortness of breath, and in severe cases, it can lead to respiratory failure, pneumonia, and death. The Spanish flu, caused by the H1N1 influenza virus, and the COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 are two of the most significant global health crises in history. While these two pandemics occurred almost a century apart and are caused by different types of viruses, there are notable similarities in their impact, transmission, and public health responses. Here are some key similarities between the Spanish flu and SARS-CoV-2. The Spanish flu pandemic of 1918-1919 stands as one of the deadliest pandemics in human history, claiming the lives of an estimated 50 million people worldwide. Its impact reverberated across continents, leaving behind a legacy of devastation and lessons that, unfortunately, seem to have been forgotten or ignored over time. Despite the advancements in science, medicine, and public health in the intervening century, humanity found itself facing a strikingly similar situation with the outbreak of the COVID-19 pandemic. Additionally, amidst the search for effective measures to combat COVID-19, novel approaches such as iodine complexes, such as Iodine-V has emerged as potential interventions, reflecting the ongoing quest for innovative solutions to mitigate the impact of pandemics. This raises the poignant question: why did we not learn from the Spanish flu?

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

We use a compartmental model with a time-varying transmission parameter to describe county level COVID-19 transmission in the greater St. Louis area of Missouri and investigate the challenges in fitting such a model to time-varying processes. We fit this model to synthetic and real confirmed case and hospital discharge data from May to December 2020 and calculate uncertainties in the resulting parameter estimates. We also explore non-identifiability within the estimated parameter set. We find that the death rate of infectious non-hospitalized individuals, the testing parameter and the initial number of exposed individuals are not identifiable based on an investigation of correlation coefficients between pairs of parameter estimates. We also explore how this non-identifiability ties back into uncertainties in the estimated parameters and find that it inflates uncertainty in the estimates of our time-varying transmission parameter. However, we do find that R0 is not highly affected by non-identifiability of its constituent components and the uncertainties associated with the quantity are smaller than those of the estimated parameters. Parameter values estimated from data will always be associated with some uncertainty and our work highlights the importance of conducting these analyses when fitting such models to real data. Exploring identifiability and uncertainty is crucial in revealing how much we can trust the parameter estimates.

During the COVID-19 pandemic, effective contact tracing was recognized as a crucial public health response to mitigate the spread of SARS-CoV-2 and reduce COVID-19-related morbidity and mortality, particularly before widespread vaccination. The World Health Organization (WHO) recommended implementing active surveillance strategies to trace and quarantine contacts of confirmed or suspected COVID-19 cases.

A detailed review and analysis of the COVID-19 contact tracing responses was conducted in five European countries and territories, between March 2021 and August 2022. The countries and territories were selected to ensure geographical representation across the WHO European Region and applied a mixed-methods approach of in-depth interviews with various stakeholders across different administrative levels to identify good practices in COVID-19 contact tracing. The interviews covered 12 themes, including methods and procedures for COVID-19 contact tracing, information technology, quality assurance and key performance indicators.

The findings demonstrate that the policy approach, digitalization capabilities and implementation approach varied in the countries and territories and were dynamic throughout the pandemic. The analysis revealed that some practices were applicable across all countries and territories, while others were context-specific, catering to each country's and territory's unique needs. The study highlighted a need for all countries to institutionalize contact tracing as an essential function of existing health systems, to digitalize contact tracing practices and processes, and to build and retain contact tracing capacities for better pandemic preparedness.

The lessons related to COVID-19 contact tracing should be utilized to strengthen future outbreak response operations as part of epidemic and pandemic preparedness.

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

During the early outbreak phase of COVID-19 in China, lockdowns prevailed as the only available policy tools to mitigate the spread of infection. To evaluate the impact of lockdown policies in the context of the first phase of COVID-19 pandemic, we leverage data on daily confirmed cases per million people and related characteristics of a large set of cities. The study analyzed 369 Chinese cities, among which 188 implemented lockdowns of varying severity levels from January 23 to March 31, 2020. We use nationwide Baidu Mobility data to estimate the impact of lockdown policies on mitigating COVID-19 cases through reducing human mobility. We adopt a heterogeneous treatment effect model to quantify the effect of lockdown policies on containing confirmed case counts. Our results suggest that lockdowns substantially reduced human mobility, and larger reduction in mobility occurred within-city compared to between-city. The COVID-19 daily confirmed cases per million people decreased by 9% - 9.2% for every ten-percentage point fall in within-city travel intensity in t+7 timeframe. We also find that one city's lockdowns can effectively reduce the spillover cases of the traveler's destination cities. We find no evidence that stricter lockdowns are more effective at mitigating COVID-19 risks. Our findings provide practical insights about the effectiveness of NPI during the early outbreak phase of the unprecedented pandemic.

Following the outbreak of COVID-19, scientists rushed to develop vaccines to protect individuals and ferry the world out of the pandemic. Unfortunately, vaccine hesitancy is a major threat to the success of vaccination campaigns. Research on previous pandemics highlighted the centrality of perceived risk and confidence as core determinants of vaccine acceptance. Research on COVID-19 is less conclusive, and frequently it relies on one-country, cross-sectional data, thus making it hard to generalize results across contexts and observe these relationships over time. To bridge these gaps, in this article, we analyzed the association between perceived risk, confidence, and vaccine acceptance cross-sectionally at individual and country levels. Then, we longitudinally explored whether a within-country variation in perceived risk and confidence was correlated with a variation in vaccine acceptance. We used data from a large-scale survey of individuals in 23 countries and 19 time-points between June 2020 and March 2021 and comparative longitudinal multilevel models to estimate the associations at different levels of analysis simultaneously. Results show the existence of cross-sectional relationships at the individual and country levels but no significant associations within countries over time. This article contributes to our understanding of the roles of risk perception and confidence in COVID-19 vaccines' acceptance by underlining that these relationships might differ at diverse levels of analysis. To foster vaccine uptake, it might be important to address individual concerns and persisting contextual characteristics, but increasing levels of perceived risk and confidence might not be a sufficient strategy to increase vaccine acceptance rates.

Insufficient knowledge about COVID-19 and low socioeconomic status have been associated with distrustful attitudes towards vaccination against COVID-19.

The aim of this study was to explore determinants of COVID-19 vaccine uptake among the general population and health workers.

A cross sectional study was conducted in 16 councils which included; Milele, Mpanda, Newala, Simanjiro, Nanyumbu, Muleba, Longido, Ulanga, Igunga, Mbulu, Karatu, Mufindi, Mvomero, Kilolo and Tabora Town. A total of 427 health care workers and 1,907 individuals were sampled from health facilities and households. Structured questionnaires were used to collect the required information.

Although the majority (93.2%) of health workers were vaccinated, 35.4% perceived their risk of getting COVID-19 infection as high. Self-reported uptake of COVID-19 vaccine was 42.4% among the general population. Significantly low proportion of the general population in Mufindi district council (7.5%) were vaccinated against COVID-19. Health workers' knowledge and perception on COVID-19 vaccination did not vary with socio-demographic factors. Among the general population, those who were separated/divorced (ARR: 0.8: 95% CI; 0.7 to 0.9), those who attained primary level of education (ARR: 0.8: 95% CI; 0.7 to 0.9), self-employed (ARR: 0.8: 95% CI; 0.7 to 0.9) and unemployed (ARR: 0.7: 95% CI; 0.6 to 0.8) were less likely to be vaccinated against COVID-19. Having positive attitude (ARR: 1.2: 95% CI; 1.1 to 1.5) and perception (ARR:1.8: 95% CI; 1.5 to 2.2), and knowledge on COVID-19 prevention (ARR: 3.0: 95% CI; 2.1to 4.4) increased the likelihood COVID-19 vaccine uptake. Prior experience of vaccination against other diseases (ARR:1.2: 95% CI; 1.0 to1.3), having history of chronic diseases (ARR:1.3: 95% CI; 1.2 to 1.4) and a family member who died of COVID-19 (ARR:1.3: 95% CI; 1.1to1.4) were also determinants of COVID-19 vaccine uptake.

Uptake of COVID-19 vaccine among the general population was significantly low among individuals with primary level of education, self-employed, unemployed, and those who were divorced or separated. Individuals with comprehensive knowledge on COVID-19 vaccination, those with positive attitude and perception on COVID-19 vaccination, having history of chronic diseases, prior vaccination against other diseases, and having a family member who succumbed to COVID-19 increased the likelihood COVID-19 vaccine uptake among the general population. Provision of health education and implementation of socio-behavioural communication change interventions are necessary to equip the general population with appropriate knowledge to transform their negative attitude and perception on COVID-19 vaccination.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic that emerged in 2019 has been an unprecedented event in international science, as it has been possible to sequence millions of genomes, tracking their evolution very closely. This has enabled various types of secondary analyses of these genomes, including the measurement of their sequence selection pressure. In this work, we have been able to measure the selective pressure of all the described SARS-CoV-2 genes, even analyzed by sequence regions, and we show how this type of analysis allows us to separate the genes between those subject to positive selection (usually those that code for surface proteins or those exposed to the host immune system) and those subject to negative selection because they require greater conservation of their structure and function. We have also seen that when another gene with an overlapping reading frame appears within a gene sequence, the overlapping sequence between the two genes evolves under a stronger purifying selection than the average of the non-overlapping regions of the main gene. We propose this type of analysis as a useful tool for locating and analyzing all the genes of a viral genome when an adequate number of sequences are available.IMPORTANCEWe have analyzed the selection pressure of all severe acute respiratory syndrome coronavirus 2 genes by means of the nonsynonymous (Ka) to synonymous (Ks) substitution rate. We found that protein-coding genes are exposed to strong positive selection, especially in the regions of interaction with other molecules (host receptor and genome of the virus itself). However, overlapping coding regions are more protected and show negative selection. This suggests that this measure could be used to study viral gene function as well as overlapping genes.

The aim of this study was to determine the seropositivity rate of pharmacists and pharmacy staff after the administration of two doses of the CoronaVac-SinoVac vaccine and to assess changes in their antibody levels according to sociodemographic characteristics.

This descriptive study was conducted between June 04, 2021 and September 30, 2021 in pharmacies located in Istanbul, Türkiye. The results of self-initiated immunoglobulin (Ig) G testing of the pharmacists and pharmacy staff, conducted at diagnostic laboratories contracted by the Istanbul Chamber of Pharmacists, were obtained using an online data collection tool. IgG measurements taken from 15 days up to 120 days after the two vaccine doses were included in the study. Participants were asked whether they smoked, had any chronic diseases (hypertension, chronic obstructive pulmonary disease, asthma, diabetes, etc.), or took any medications. Subgroup analyses were performed for each method used to measure antibody levels.

The study included 329 pharmacists/pharmacy staff (298 pharmacists and 31 pharmacy staff). The mean age of the participants was 49.7 ± 13.7 years, and 71.4% were female. The antibody positivity of the 329 participants was 94.9% following the two doses. The positivity rate was 95.4% in participants under 65 years of age, whereas it was 91.8% in those aged 65 years and over. There was no significant difference in the mean age between those with positive and negative antibody results (p > 0.05). Although antibody levels were lower older people, smokers, and those with chronic diseases, this difference was not statistically significant (p > 0.05).

Seropositivity developed following the administration of two doses of CoronaVac-Sinovac vaccines. IgG antibody levels were lower in older adults, smokers, and those with chronic diseases, although not to a statistically significant extent. Further studies are needed to better understand the reasons for the different immunological responses to COVID-19.

Respiratory masks are the primary and most effective means of protecting individuals from airborne hazards such as droplets and particulate matter during public engagements. However, conventional electrostatically charged melt-blown microfiber masks typically require thick and dense membranes to achieve high filtration efficiency, which in turn cause a significant pressure drop and reduce breathability. In this study, we have developed a multielectrospinning system to address this issue by manipulating the pore structure of nanofiber networks, including the use of uniaxially aligned nanofibers created via an electric-field-guided electrospinning apparatus. In contrast to the common randomly collected microfiber membranes, partially aligned dual-nanofiber membranes, which are fabricated via electrospinning of a random 150 nm nanofiber base layer and a uniaxially aligned 450 nm nanofiber spacer layer on a roll-to-roll collector, offer an efficient way to modulate nanofiber membrane pore structures. Notably, the dual-nanofiber configuration with submicron pore structure exhibits increased fiber density and decreased volume density, resulting in an enhanced filtration efficiency of over 97% and a 50% reduction in pressure drop. This leads to the highest quality factor of 0.0781. Moreover, the submicron pore structure within the nanofiber networks introduces an additional sieving filtration mechanism, ensuring superior filtration efficiency under highly humid conditions and even after washing with a 70% ethanol solution. The nanofiber mask provides a sustainable solution for safeguarding the human respiratory system, as it effectively filters and inactivates human coronaviruses while utilizing 130 times fewer polymeric materials than melt-blown filters. This reusability of our filters and their minimum usage of polymeric materials would significantly reduce plastic waste for a sustainable global society.

Inflammasomes are large protein complexes that play a major role in sensing inflammatory signals and triggering the innate immune response. Each inflammasome complex has three major components: an upstream sensor molecule that is connected to a downstream effector protein such as caspase-1 through the adapter protein ASC. Inflammasome formation typically occurs in response to infectious agents or cellular damage. The active inflammasome then triggers caspase-1 activation, followed by the secretion of pro-inflammatory cytokines and pyroptotic cell death. Aberrant inflammasome activation and activity contribute to the development of diabetes, cancer, and several cardiovascular and neurodegenerative disorders. As a result, recent research has increasingly focused on investigating the mechanisms that regulate inflammasome assembly and activation, as well as the potential of targeting inflammasomes to treat various diseases. Multiple clinical trials are currently underway to evaluate the therapeutic potential of several distinct inflammasome-targeting therapies. Therefore, understanding how different inflammasomes contribute to disease pathology may have significant implications for developing novel therapeutic strategies. In this article, we provide a summary of the biological and pathological roles of inflammasomes in health and disease. We also highlight key evidence that suggests targeting inflammasomes could be a novel strategy for developing new disease-modifying therapies that may be effective in several conditions.

The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.

COVID-19 vaccine hesitancy is a major barrier to vaccine uptake, and the achievement of herd immunity is required to reduce morbidity and mortality and protect the most vulnerable populations. In Nigeria, COVID-19 vaccine hesitancy has been high, and uptake remains very low. Healthcare workers (HCWs) in Nigeria can help support public health efforts to increase vaccine uptake.

This study evaluates Nigerian HCWs' acceptance and intent to recommend the COVID-19 vaccine.

Cross-sectional survey among 1,852 HCWs in primary, secondary, and tertiary care settings across Nigeria. Respondents included doctors, nurses, pharmacy workers, and clinical laboratory professionals who have direct clinical contact with patients in various healthcare settings. A 33-item questionnaire was used in the study, with two of the questions focused on the COVID-19 vaccine. The responses to the two questions were analyzed using Chi-square (c2) tests and independent t-tests to determine the acceptance of the vaccine.

The majority of respondents were younger than 34 years (n = 1,227; 69.2%) and primarily worked in hospitals (n = 1,278; 72.0%). Among the respondents, 79.2% (n = 1,467) endorsed the COVID-19 vaccine as a critical tool in reducing the impact of the disease, and 76.2% (n = 1,412) will accept and recommend the vaccine to their patients. The younger HCWs were more likely to endorse and recommend the vaccine to their patients.

There is a moderately high COVID-19 vaccine acceptance rate among HCWs surveyed in our study. The confidence of HCWs in its use and their willingness to recommend it to their patients can provide a potentially useful element in increasing acceptance by the larger population in Nigeria.

The outbreak of the COVID-19 pandemic sparked numerous studies on policy options for managing public health emergencies, especially regarding how to choose the intensity of prevention and control to maintain a balance between economic development and disease prevention.

We constructed a cost-benefit model of COVID-19 pandemic prevention and control policies based on an epidemic transmission model. On this basis, numerical simulations were performed for different economies to analyse the dynamic evolution of prevention and control policies. These economies include areas with high control costs, as seen in high-income economies, and areas with relatively low control costs, exhibited in upper-middle-income economies.

The simulation results indicate that, at the outset of the COVID-19 pandemic, both high-and low-cost economies tended to enforce intensive interventions. However, as the virus evolved, particularly in circumstances with relatively rates of reproduction, short incubation periods, short spans of infection and low mortality rates, high-cost economies became inclined to ease restrictions, while low-cost economies took the opposite approach. However, the consideration of additional costs incurred by the non-infected population means that a low-cost economy is likely to lift restrictions as well.

This study concludes that variations in prevention and control policies among nations with varying income levels stem from variances in virus transmission characteristics, economic development, and control costs. This study can help researchers and policymakers better understand the differences in policy choice among various economies as well as the changing trends of dynamic policy choices, thus providing a certain reference value for the policy direction of global public health emergencies.

The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R0. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R0 range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. ONE-SENTENCE SUMMARY: Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

Humoral and cellular immune responses to SARS-CoV-2 and other coronaviruses in lung transplant recipients are unknown. We measured antibodies and T cell responses against the SARS-CoV-2 spike S2 and nucleocapsid antigens and spike antigens from common respiratory coronaviruses (229E, NL63, OC43, and HKU1) after vaccination or infection of LTxRs. 148 LTxRs from single center were included in this study: 98 after vaccination and 50 following SARS-CoV-2 infection. Antibodies were quantified by enzyme-linked immunosorbent assay. The frequency of T cells secreting IL2, IL4, IL10, IL17, TNFα, and IFNγ were enumerated by enzyme-linked immunospot assay. Our results have shown the development of antibodies to SARS-CoV-2 spike protein in infected LTxRs (39/50) and vaccinated LTxRs (52/98). Vaccinated LTxRs had higher number of T cells producing TNFα but less cells producing IFNγ than infected LTxRs in response to the nucleocapsid antigen and other coronavirus spike antigens. We didn't find correlation between the development of antibodies and cellular immune responses against the SARS-CoV-2 spike protein after vaccination. Instead, LTxRs have pre-existing cellular immunity to common respiratory coronaviruses, leading to cross-reactive immunity against SARS-CoV-2 which likely will provide protection against SARS-Cov-2 infection.

The aim of this study was to describe the experiences of post-sedation COVID-19 patients in rehabilitation. Eleven Israeli men and women were interviewed in semi-structured interviews. They were patients recovering in a neurological rehabilitation unit from severe COVID-19 post-mechanical ventilation and sedation. Five themes were generated through thematic analysis: "an unexpected turn of events," "filling the gaps," "emotional reactions," "ambiguity regarding medical condition," and "sense and meaning-making." Findings suggest a need for improved communication between patients and medical staff to enhance a sense of control and coherence. Psychological support should be considered to facilitate sense and meaning-making processes during hospitalization.

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) identified in 2003 infected ∼8000 people in 26 countries with 800 deaths, which was soon contained and eradicated by syndromic surveillance and enhanced quarantine. A closely related coronavirus SARS-CoV-2, the causative agent of COVID-19 identified in 2019, has been dramatically more contagious and catastrophic. It has infected and caused various flu-like symptoms of billions of people in >200 countries, including >6 million people died of or with the virus. Despite the availability of several vaccines and antiviral drugs against SARS-CoV-2, finding new therapeutics is needed because of viral evolution and a possible emerging coronavirus in the future. The main protease (Mpro) of these coronaviruses plays important roles in their life cycle and is essential for the viral replication. This article represents a comprehensive review of the function, structure and inhibition of SARS-CoV and -CoV-2 Mpro, including structure-activity relationships, protein-inhibitor interactions and clinical trial status.

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

The COVID-19 pandemic continues to pose challenges for global public healthcare, even with the authorisation of several vaccines worldwide. To better understand rural communities' knowledge, attitudes, perceptions and barriers towards these vaccines, we conducted a qualitative cross-sectional study with adults in rural Bangladesh.

This cross-sectional study was conducted in the rural areas of Sylhet and Natore in Bangladesh from August 2021 to February 2022.

Our study involved 15 in-depth interviews with rural adults and 2 key informant interviews with health workers.

We analysed data thematically, resulting in four main themes: (1) knowledge and perception aspects, (2) myths and misconceptions, (3) practice and attitude and (4) barriers and challenges of COVID-19 vaccines.

The findings indicate that rural populations lack sufficient knowledge about COVID-19 vaccines but have a more favourable attitude towards them. Misconceptions, beliefs and personal experiences were found to be the main reasons for vaccine avoidance. To address these challenges and dispel the spread of misinformation, health education programmes play a pivotal role in improving vaccine management. Policy-makers should initiate these programmes without delay to create a well-informed and enlightened community, given that the COVID-19 is still spreading.

Desmoglein-2 (DSG2) has emerged as a potential biomarker for coronavirus disease 2019 (COVID-19) complications, particularly cardiac and cardiovascular involvement. The expression of DSG2 in lung tissues has been detected at elevated levels, and circulating DSG2 levels correlate with COVID-19 severity. DSG2 may contribute to myocardial injury, cardiac dysfunction and vascular endothelial dysfunction in COVID-19. Monitoring DSG2 levels could aid in risk stratification, early detection and prognostication of COVID-19 complications. However, further research is required to validate DSG2 as a biomarker. Such research will aim to elucidate its precise role in pathogenesis, establishing standardized assays for its measurement and possibly identifying therapeutic targets.

The effects of COVID-19 infection persist beyond the active phase. Comprehensive description and analysis of the post COVID sequelae in various population groups are critical to minimise the long-term morbidity and mortality associated with COVID-19. This analysis was conducted with an objective to estimate the frequency of post COVID sequelae and subsequently, design a framework for holistic management of post COVID morbidities.

Follow-up data collected as part of a registry-based observational study in 31 hospitals across India since September 2020-October 2022 were used for analysis. All consenting hospitalised patients with COVID-19 are telephonically followed up for up to 1 year post-discharge, using a prestructured form focused on symptom reporting.

Dyspnoea, fatigue and mental health issues were reported among 18.6%, 10.5% and 9.3% of the 8042 participants at first follow-up of 30-60 days post-discharge, respectively, which reduced to 11.9%, 6.6% and 9%, respectively, at 1-year follow-up in 2192 participants. Patients who died within 90 days post-discharge were significantly older (adjusted OR (aOR): 1.02, 95% CI: 1.01, 1.03), with at least one comorbidity (aOR: 1.76, 95% CI: 1.31, 2.35), and a higher proportion had required intensive care unit admission during the initial hospitalisation due to COVID-19 (aOR: 1.49, 95% CI: 1.08, 2.06) and were discharged at WHO ordinal scale 6-7 (aOR: 49.13 95% CI: 25.43, 94.92). Anti-SARS-CoV-2 vaccination (at least one dose) was protective against such post-discharge mortality (aOR: 0.19, 95% CI: 0.01, 0.03).

Hospitalised patients with COVID-19 experience a variety of long-term sequelae after discharge from hospitals which persists although in reduced proportions until 12 months post-discharge. Developing a holistic management framework with engagement of care outreach workers as well as teleconsultation is a way forward in effective management of post COVID morbidities as well as reducing mortality.

Worldwide, Severe acute respiratory syndrome Coronavirus (SARS-CoV-2) pandemic crisis, causing many morbidities, mortality, and devastating impact on economies, so the current outbreak of the CoV-2 is a major concern for global health. The infection spread quickly and caused chaos in many countries around the world. The slow discovery of CoV-2 and the limited treatment options are among the main challenges. Therefore, the development of a drug that is safe and effective against CoV-2 is urgently needed. The present overview briefly summarizes CoV-2 drug targets ex: RNA-dependent RNA polymerase (RdRp), papain-like protease (PLpro), 3-chymotrypsin-like protease (3CLpro), transmembrane serine protease enzymes (TMPRSS2), angiotensin-converting enzyme 2 (ACE2), structural protein (N, S, E, and M), and virulence factors (NSP1, ORF7a, and NSP3c) for which drug design perspective can be considered. In addition, summarize all anti-COVID-19 medicinal plants and phytocompounds and their mechanisms of action to be used as a guide for further studies.

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.