The COVID-19 pandemic revealed global disparities in accessing medical countermeasures, as high-income countries prioritised their own interests while disregarding low- and middle-income countries. Despite global efforts to ensure an equitable pandemic response, these initiatives largely failed to achieve their objectives for LMICs due to systemic inequalities. This review critically examines these disparities, identifying that excessive stockpiling by HICs, fragmented international coordination, inadequate research and manufacturing capacity, restricted access to emergency research funding, intellectual property constraints, unequal participation in clinical trials, and inadequate regulatory harmonisation collectively hinder LMICs ability to respond effectively. By analysing diverse case scenarios and global response strategies, all plausible key shortcomings that contributed to the failure of coordinated pandemic preparedness were highlighted. Based on these insights, actionable strategies are proposed to address these gaps in LMICs so as to ensure affordability, accessibility, and equitable distribution of vaccines, diagnostics, and biotherapeutics in future public health emergencies, strengthening global biosecurity.

The relationships between air pollution, genetic susceptibility, and COVID-19-related outcomes, as well as the potential interplays between air pollution and genetic susceptibility, remain largely unexplored. The Cox proportional hazards model was used to assess associations between long-term exposure to air pollutants and the risk of COVID-19 outcomes (infection, hospitalization, and death) in a COVID-19-naive cohort (n = 458,396). Additionally, associations between air pollutants and the risk of COVID-19 severity (hospitalization and death) were evaluated in a COVID-19 infection cohort (n = 110,216). Furthermore, this study investigated the role of host genetic susceptibility in the relationships between exposure to air pollutants and the development of COVID-19-related outcomes. Long-term exposure to air pollutants was significantly associated with an increased risk of COVID-19-related outcomes in the COVID-19 naive cohort. Similarly, in COVID-19 infection cohort, hazard ratios (HRs) for COVID-19 hospital admission were 1.23 (1.19, 1.27) for PM2.5 and 1.22 (1.17, 1.26) for PM10, whereas HRs for COVID-19 death were 1.28 (1.18, 1.39) for PM2.5 and 1.25 (1.16, 1.36) for PM10. Notably, significant interactions were found between PM2.5/PM10 and genetic susceptibility in COVID-19 death. In COVID-19 infection cohort, participants with both high genetic risk and high air pollutants exposure had 1.86- to 1.97-fold and 1.91- to 2.14-fold higher risk of COVID-19 hospitalization and death compared to those with both low genetic risk and low air pollutants exposure. Exposure to air pollution is significantly associated with an increased burden of severe COVID-19, and air pollution-gene interactions may play a crucial role in the development of COVID-19-related outcomes.

ObjectiveTo evaluate the efficacy and adverse events of immunomodulators in the treatment of severe coronavirus disease 2019 (COVID-19).MethodsA literature search for the meta-analysis was performed using PubMed, The Cochrane Library, Embase, Wanfang Data, CNKI, and Web of Science to identify randomized controlled trials assessing the outcomes of patients treated with corticosteroids alone and/or interleukin-6 receptor antagonists for COVID-19. The risk of bias was assessed using the Cochrane method. The protocol was registered with PROSPERO (registry number: CRD42022356904).ResultsCompared with patients receiving standard of care, patients treated with corticosteroids alone had an increased risk of 14-day in-hospital death, whereas those treated with interleukin-6 receptor antagonists alone or in combination with corticosteroids had a lower risk of 14-day in-hospital death. Corticosteroid therapy alone was associated with increased risk of several adverse events, including intensive care unit admission and non-invasive ventilation, whereas interleukin-6 receptor antagonists alone or in combination with corticosteroids were not linked to adverse effects.ConclusionsThe findings supported the safety and efficacy of interleukin-6 receptor antagonists, either alone or together with corticosteroids, in patients with severe COVID-19; evidence supporting the efficacy and safety of corticosteroids monotherapy is lacking.

Due to the pervasive uncertainty in human society, super large and megacities are increasingly prone to becoming high-risk areas. However, the construction of urban resilience in this new era lacks sufficient research on the core conditions and complex interactive mechanisms governing it. Hence, this study proposes a specialized event-oriented framework for governing urban resilience in China based on the pressure-state-response (PSR) theory. We examined COVID-19 cases in 30 cities across China and analyzed the distribution of prevention and control achievements between high-level and non-high-level conditions. Our findings reveal the following key points: (1) High-level achievements in COVID-19 prevention and control rely on three condition configurations: non-pressure-responsive type, pressure-state type, and pressure-responsive type. (2) High economic resilience may indicate a robust state of urban systems amid demographic pressures. In cities experiencing fewer event pressure factors, the application of digital technology plays a crucial role in daily urban management. (3) The implementation of flexible policies proves beneficial in mitigating the impact of objective pressure conditions, such as environmental factors, on urban resilience.

Emergency management of municipal solid waste during public health emergencies is crucial for curbing the spread of viruses and controlling environmental pollution. Most existing solid waste management systems, are designed with a top-down approach, covering multiple industries and sectors. However, these systems often fail to effectively manage potentially viral solid waste in household waste, which impedes effective solid waste management during public health emergencies and increases the risk of viral outbreaks. For example, during the COVID-19 outbreak, negligent management of potentially viral solid waste may result in the formation of a potential transmission chain of COVID-19 virus. Improper waste management may contaminate natural vectors such as water, soil and air, thereby increasing the pathways of human contact with infected vectors and promoting the risk of transmission of viral diseases such as dengue, Zika and hepatitis A. In this study, the applicability and limitations of the Chinese public emergency management system in the disposal of Potentially Viral/Viral Municipal Solid Waste (PVMSW) were analyzed in the breakout of public health crises. To address the need for precise risk control, efficient collaboration among key stakeholders, functional integration crossing multiple management organizations and flexible responses to complex situations, we proposed a PVMSW emergency management system based on a "Beam-Column Structure" model. This system consists of four modules: main traction, organizational leadership, functional articulation and demand matching. Each module is supported by the corresponding safeguards, including multi-level subject responsibilities, multi-scenario organization and implementation, multi-chain process connection and multi-demand control methods. The PVMSW emergency management system proposed in this study contributes to extending the framework of existing waste management protocols and enhancing the timely collection, transportation and disposal of PVMSW in public health crises. It offers valuable insights and guidance on the practices for improving risk awareness and management capacity in China's solid waste emergency response.

The increased demand on healthcare professionals (HCPs) during the Coronavirus Disease (Covid-19) pandemic reduces opportunities for HCPs to deliver compassionate care to patients and to maintain self-care. This study explored how HCPs understand and experience compassionate working practices during the Covid-19 pandemic to better support HCPs' wellbeing and to sustain quality of care.

All nineteen participants worked as an HCP during the Covid-19 pandemic, resided in the United Kingdom (UK) and took part in individual semi-structured interviews.

Using a thematic analysis approach, we developed three themes: (a) The art of compassionate care: Feeling and action, (b) The impact of Covid-19 on compassionate care and (c) Suffering during Covid-19: The importance of self-compassion and self-care.

Overall, the analysis illustrated the strong and negative impact that Covid-19 had on the ability to deliver compassionate care, resulting in moral injury and psychological and behavioural difficulties for HCPs, particularly in terms of self-care.

This research demonstrates an imperative need for organisations to better support HCPs' health and wellbeing, through a self-care system that promotes self-compassionate and self-care practices.

Following the COVID-19 restrictions, there was a sharp decline in global air quality and related environmental metrics. Due to the limited availability of in situ atmospheric data in Bangladesh, this study collected data on various air pollutants (NO2, SO2, CO, and PM2.5), greenhouse gases (CO2, CH4, and O3), as well as meteorological variables like Land Surface Temperature (LST), Relative Humidity (RH), Precipitation, surface albedo and Aerosol Optical Depth (AOD) from different datasets by Google Earth Engine (GEE), the International Energy Agency (IEA), NASA Giovanni, and NASA Power Access Viewer, covering periods before (2019), during (2020), and after (2021-2023) the COVID-19 lockdown in Bangladesh. GIS-based assessment alongside Principal Component Analysis (PCA) has been performed to explore the patterns, trends and correlations among the observed variables. Results showed in 2020 compared to 2019, NO2, SO2, CO, PM2.5, and CO2 concentrations decreases by 1.94, 16.67, 1.95, 2.08, and 6 %, respectively, while CH4 and O3 continued to rise. Meteorological variables exhibited a 0.16 °C decreases in LST, 6.4 % increases in RH, a 6 % reduction in AOD, and 6.36 % declines in surface albedo. Post-lockdown in 2021, air pollutants surged (NO2, SO2, CO, and PM2.5 increases by 17.3, 23.6, 0.6, and 8.3 %, respectively), with CO2, LST, and AOD rising by 8.5 %, 0.13 °C, and 8.3 %, and a slight 0.46 % decrease in RH compared to 2019 due to resuming more economic activities, transportation and industrial production works. The years 2022-2023 saw slight improvements in most variables except CH4, though concentrations did not revert to those of 2019. The findings of correlation coefficients revealed that pollutants and GHG are highly correlated with the meteorological variables specially with RH. This study underscores the substantial shifts in atmospheric variables from pre-to post-lockdown periods, offering valuable insights for more effective management of the greenhouse effect and air pollution control strategies.

The aim of this paper is to assess the influence of selected geographical factors on the diversity of the development of the COVID-19 pandemic in Europe's regions, and on its dynamics across the continent. The work took into account 250 of NUTS-2 regions. The datasets included the course of the COVID-19 pandemic (two dependent variables), intervening actions (four variables of the research background), and potential environmental and socio-economic conditioning (twelve independent variables). The dependent variables' set was composed of two indexes: morbidity and temporal inertia. The temporal scope of the research was 23 March 2020-15 May 2022, with weekly resolution. By means of multiple linear regression model, the influence of the administrative actions and of the selected natural and socio-economic factors was assessed. Finally, a synthetic Regional Epidemic Vulnerability Index (REVI) for each individual region was calculated. It allowed us to classify the regions into three categories: resistant, neutral, or sensitive. REVI's spatial distribution indicates that the zone of above-average vulnerability occurred in the western part of Europe and around the Alps. Therefore, focus ought to extend beyond regional statistics, towards spatial relationships, like contiguous or transit position. This research also validated the strong impact of national borders.

COVID-19, a viral infection that emerged in late 2019, induces a severe acute respiratory syndrome marked by significant clinical symptoms, and the potential for progressive respiratory failure and death. People facing the threat of COVID-19 not only feared being infected, but were also worried about the side-effects of vaccination. This conflict affected their epidemic prevention behavior. To understand this issue, the present study explored whether infection anxiety affected the psychological avoidance or approach to getting vaccinated and the intention to take epidemic prevention measures. The study implemented a cross-sectional, web-based survey. We created questionnaires using Surveycake, an online e-form questionnaire platform. We used the snowball sampling method via a social media app to recruit participants. If individuals were willing to participate in the research, we emailed the e-form questionnaire link to them to collect data. After questionnaire collection, 288 questionnaires were returned, and 277 valid questionnaires were obtained for structural equation modeling analysis. According to the statistical results, it was found that infection anxiety was positively related to avoidance-avoidance conflict, and the power of infection anxiety on avoidance conflict was 23.0%. Infection anxiety was negatively related to approach-approach conflict regarding vaccination, and the power of infection anxiety on approach-approach conflict was 22.0%. Approach-approach conflict regarding vaccination was negatively related to prevention behavior, while avoidance-avoidance conflict regarding vaccination was positively related to prevention behavior. The two conflicts explained 12.5% of the total variance in prevention behavior. The study results highlight the long-term importance of achieving vaccine goals in order to prepare for future health emergencies similar to the recent COVID-19 pandemic.

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

SARS-CoV-2 variants of concern (VOCs) emerged and rapidly replaced the original strain worldwide. The increased transmissibility of these new variants led to increases in infections, hospitalizations, and mortality. However, there is a scarcity of retrospective investigations examining the severity of all the main VOCs in presence of key public health measures and within various social determinants of health (SDOHs).

This study aims to provide a retrospective assessment of the clinical severity of COVID-19 VOCs in the context of heterogenous SDOHs and vaccination rollout.

We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity (hospitalizations, intensive care unit [ICU] admissions, and deaths) of Gamma, Delta, and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazard modeling. We also conducted a subanalysis among unvaccinated individuals, as assessed severity differed across VOCs and SDOHs.

We included 91,964 individuals infected with a SARS-CoV-2 VOC (Alpha: n=20,487, 22.28%; Gamma: n=15,223, 16.55%; Delta: n=49,161, 53.46%; and Omicron: n=7093, 7.71%). Delta was associated with the most severe disease in terms of hospitalization, ICU admissions, and deaths (hospitalization: adjusted hazard ratio [aHR] 2.00, 95% CI 1.92-2.08; ICU: aHR 2.05, 95% CI 1.91-2.20; death: aHR 3.70, 95% CI 3.23-4.25 relative to Alpha), followed generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated individual subanalysis, although the proportion of individuals infected with Delta and Omicron who were hospitalized was 2 times higher in those unvaccinated than in those fully vaccinated. Regarding SDOHs, the proportion of hospitalized individuals was higher in areas with lower income across all VOCs, whereas among Alpha and Gamma infections, 2 VOCs that cocirculated, differential distributions of hospitalizations were found among racially minoritized groups.

Our study provides robust severity estimates for all VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be the most severe, followed by Gamma and Omicron. This study highlights the importance of targeted testing and sequencing to ensure timely detection and accurate estimation of severity in emerging variants. It further sheds light on the importance of vaccination coverage and SDOHs in the context of pandemic preparedness to support the prioritization of allocation for resource-constrained or minoritized groups.

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2 weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs.

The COVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact worldwide, leading to widespread morbidity and mortality. Vaccination against COVID-19 is a critical tool in controlling the spread of the virus and reducing the severity of the disease. However, the rapid development and deployment of COVID-19 vaccines have raised concerns about potential adverse events following immunization (AEFIs). Understanding the temporal and spatial patterns of these AEFIs is crucial for an effective public health response and vaccine safety monitoring.

This study aimed to analyze the temporal and spatial characteristics of AEFIs associated with COVID-19 vaccines in the United States reported to the Vaccine Adverse Event Reporting System (VAERS), thereby providing insights into the patterns and distributions of the AEFIs, the safety profile of COVID-19 vaccines, and potential risk factors associated with the AEFIs.

We conducted a retrospective analysis of administration data from the Centers for Disease Control and Prevention (n=663,822,575) and reports from the surveillance system VAERS (n=900,522) between 2020 and 2022. To gain a broader understanding of postvaccination AEFIs reported, we categorized them into system organ classes (SOCs) according to the Medical Dictionary for Regulatory Activities. Additionally, we performed temporal analysis to examine the trends of AEFIs in all VAERS reports, those related to Pfizer-BioNTech and Moderna, and the top 10 AEFI trends in serious reports. We also compared the similarity of symptoms across various regions within the United States.

Our findings revealed that the most frequently reported symptoms following COVID-19 vaccination were headache (n=141,186, 15.68%), pyrexia (n=122,120, 13.56%), and fatigue (n=121,910, 13.54%). The most common symptom combination was chills and pyrexia (n=56,954, 6.32%). Initially, general disorders and administration site conditions (SOC 22) were the most prevalent class reported. Moderna exhibited a higher reporting rate of AEFIs compared to Pfizer-BioNTech. Over time, we observed a decreasing reporting rate of AEFIs associated with COVID-19 vaccines. In addition, the overall rates of AEFIs between the Pfizer-BioNTech and Moderna vaccines were comparable. In terms of spatial analysis, the middle and north regions of the United States displayed a higher reporting rate of AEFIs associated with COVID-19 vaccines, while the southeast and south-central regions showed notable similarity in symptoms reported.

This study provides valuable insights into the temporal and spatial patterns of AEFIs associated with COVID-19 vaccines in the United States. The findings underscore the critical need for increasing vaccination coverage, as well as ongoing surveillance and monitoring of AEFIs. Implementing targeted monitoring programs can facilitate the effective and efficient management of AEFIs, enhancing public confidence in future COVID-19 vaccine campaigns.

Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling.

The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs.

A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548-5.7895)], PM10 [RR = 2.9427: (2.2290-3.8850)], NO2 [RR = 3.2750: (3.1420-3.4136)], SO2 [RR = 3.3400: (2.7931-3.9940)], CO [RR = 2.6244: (2.5208-2.7322)] and O3 [RR = 2.4008: (2.1859-2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344-3.3838)], PM10 [RR = 2.6202: (2.1602-3.1781)], NO2 [RR = 3.2226: (2.1411-4.8504)], CO [RR = 1.8021 (0.8045-4.0370)] and O3 [RR = 2.3270 (1.5906-3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations.

The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

This study updates the COVID-19 pandemic surveillance in the Middle East and North Africa (MENA) we first conducted in 2020 with 2 additional years of data for the region.

The objective of this study is to determine whether the MENA region meets the criteria for moving from a pandemic to endemic. In doing so, this study considers pandemic trends, dynamic and genomic surveillance methods, and region-specific historical context for the pandemic. These considerations continue through the World Health Organization (WHO) declaration of the end of the public health emergency for the COVID-19 pandemic on May 5, 2023.

In addition to updates to traditional surveillance data and dynamic panel estimates from the original study by Post et al, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed of COVID-19 spread was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data from September 4, 2020, to May 12, 2023.

The speed of COVID-19 spread for the region had remained below the outbreak threshold for 7 continuous months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant and positive, the weekly shift parameters suggested the coefficients had most recently turned negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of the speed of spread equal to 10 became entirely insignificant from October 2022 onward.

The COVID-19 pandemic had far-reaching effects on MENA, impacting health care systems, economies, and social well-being. Although COVID-19 continues to circulate in the MENA region, the rate of transmission remained well below the threshold of an outbreak for over 1 year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had transitioned to endemic by the time of the WHO declaration.

In May 2020, the World Health Organization (WHO) declared Latin America and the Caribbean (LAC) the epicenter of the COVID-19 pandemic, with over 40% of worldwide COVID-19-related deaths at the time. This high disease burden was a result of the unique circumstances in LAC.

This study aimed to (1) measure whether the pandemic was expanding or contracting in LAC when the WHO declared the end of COVID-19 as a public health emergency of international concern on May 5, 2023; (2) use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history; and (3) provide, with a focus on prevention policies, a historical context for the course of the pandemic in the region.

In addition to updates of traditional surveillance data and dynamic panel estimates from the original study, we used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern (VOCs). We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Additionally, we conducted a 1-sided t test for whether the regional weekly speed (rate of novel COVID-19 transmission) was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the period from August 2020 to May 2023.

The speed of pandemic spread for the region had remained below the outbreak threshold for 6 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant for the 120-day period ending on the week of May 5, 2023, the coefficients were relatively modest in magnitude (0.457 and 0.491, respectively). Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, did not indicate any change in this clustering effect of cases on future cases. From December 2021 onward, Omicron was the predominant VOC in sequenced viral samples. The rolling t test of speed=10 became entirely insignificant from January 2023 onward.

Although COVID-19 continues to circulate in LAC, surveillance data suggest COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. However, the region experienced a high COVID-19 burden in the early stages of the pandemic, and prevention policies should be an immediate focus in future pandemics. Ahead of vaccination development, these policies can include widespread testing of individuals and an epidemiological task force with a contact-tracing system.

As global governments continue to inoculate populations in the aftermath of the SARS-CoV-19 epidemic, health protection and related problems such as vaccination reluctance have sparked conflicting emotions and discourses. Stakeholder concerns about vaccination promotion have arisen in this context. Consequently, this research investigates vaccination uptake behavior using an integrated framework that includes protective motivation theory and an extended version of the theory of planned behavior. Except for perceived severity, empirical study results based on the structural equation modeling technique demonstrated a positive relationship between PMT factors (i.e. perceived vulnerability, reaction efficacy, self-efficacy, and cost response) and intention to vaccinate. TPB factors (attitude, perceived behavior control, subjective norm, personal moral norm, and descriptive norm) were also found to have a positive connection with intention. The model's utility and suitability are indicated by the predictive and explanatory potency score (R2 = .415). Study implications for COVID-19 vaccine uptake promotion and ideas for future investigations are explored in light of our findings.

In Japan, long-distance domestic travel was banned while the ancestral SARS-CoV-2 strain was dominant under the first declared state of emergency from March 2020 until the end of May 2020. Subsequently, the "Go To Travel" campaign travel subsidy policy was activated, allowing long-distance domestic travel, until the second state of emergency as of January 7, 2021. The effects of this long-distance domestic travel ban on SARS-CoV-2 infectivity have not been adequately evaluated.

We evaluated the effects of the long-distance domestic travel ban in Japan on SARS-CoV-2 infectivity, considering climate conditions, mobility, and countermeasures such as the "Go To Travel" campaign and emergency status.

We calculated the effective reproduction number R(t), representing infectivity, using the epidemic curve in Kagoshima prefecture based on the empirical distribution of the incubation period and procedurally delayed reporting from an earlier study. Kagoshima prefecture, in southern Japan, has several resorts, with an airport commonly used for transportation to Tokyo or Osaka. We regressed R(t) on the number of long-distance domestic travelers (based on the number of airport limousine bus users provided by the operating company), temperature, humidity, mobility, and countermeasures such as state of emergency declarations and the "Go To Travel" campaign in Kagoshima. The study period was June 20, 2020, through February 2021, before variant strains became dominant. A second state of emergency was not declared in Kagoshima prefecture but was declared in major cities such as Tokyo and Osaka.

Estimation results indicated a pattern of declining infectivity with reduced long-distance domestic travel volumes as measured by the number of airport limousine bus users. Moreover, infectivity was lower during the "Go To Travel" campaign and the second state of emergency. Regarding mobility, going to restaurants, shopping malls, and amusement venues was associated with increased infectivity. However, going to grocery stores and pharmacies was associated with decreased infectivity. Climate conditions showed no significant association with infectivity patterns.

The results of this retrospective analysis suggest that the volume of long-distance domestic travel might reduce SARS-CoV-2 infectivity. Infectivity was lower during the "Go To Travel" campaign period, during which long-distance domestic travel was promoted, compared to that outside this campaign period. These findings suggest that policies banning long-distance domestic travel had little legitimacy or rationale. Long-distance domestic travel with appropriate infection control measures might not increase SARS-CoV-2 infectivity in tourist areas. Even though this analysis was performed much later than the study period, if we had performed this study focusing on the period of April or May 2021, it would likely yield the same results. These findings might be helpful for government decision-making in considering restarting a "Go To Travel" campaign in light of evidence-based policy.

The investigation goal here was to analyze how the level of public debt affects preparedness of health systems to face emergencies. In particular, this study examined the negative effects of high public debt on health systems of European countries in the presence of the COVID-19 pandemic crisis. Empirical evidence revealed that European countries with a lower level of government debt as a percentage of GDP both in 2009 and 2019 (the period before the arrival of the pandemic) had lower COVID-19 fatality rates compared to countries with higher levels of public debt. The explanation is that high levels of public debt in countries trigger budget constraints that limit their ability to allocate resources to healthcare systems (e.g., health expenditures and investments), weakening health system performance and causing systemic vulnerability and lower preparedness during emergencies, such as with the COVID-19 pandemic. Implications of health policies are suggested to improve strategies of crisis management.

The associations between non-optimal ambient temperature, air pollution and SARS-CoV-2 infection and post COVID-19 condition (PCC) remain constrained in current understanding. We conducted a retrospective analysis to explore how ambient temperature affected SARS-CoV-2 infection in individuals who later developed PCC compared to those who did not. We investigated if these associations were modified by air pollution.

We conducted a bidirectional time-stratified case-crossover study among individuals who tested positive for SARS-CoV-2 between May 2021 and June 2022. We included 6302 infections, with 2850 PCC cases. We used conditional logistic regression and distributed lag non-linear models to obtain odds ratios (OR) and 95% confidence intervals (CI) for non-optimal temperatures relative to the period median temperature (10.6 °C) on lags 0 to 5. For effect modification, daily average PM2.5 concentrations were categorized using the period median concentration (8.8 μg/m3). Z-tests were used to compare the results by PCC status and PM2.5.

Non-optimal cold temperatures increased the cumulative odds of infection (OR = 1.93; 95%CI:1.67-2.23, OR = 3.53; 95%CI:2.72-4.58, for moderate and extreme cold, respectively), with the strongest associations observed for non-PCC cases. Non-optimal heat temperatures decreased the odds of infection except for moderate heat among PCC cases (OR = 1.32; 95%CI:0.89-1.96). When PM2.5 was >8.8 μg/m3, the associations with cold were stronger, and moderate heat doubled the odds of infection with later development of PCC (OR = 2.18; 95%CI:1.01-4.69). When PM2.5 was ≤8.8 μg/m3, exposure to non-optimal temperatures reduced the odds of infection.

Exposure to cold increases SARS-CoV2 risk, especially on days with moderate to high air pollution. Heated temperatures and moderate to high air pollution during infection may cause PCC. These findings stress the need for mitigation and adaptation strategies for climate change to reduce increasing trends in the frequency of weather extremes that have consequences on air pollution concentrations.

The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion.

This study probes into Macao's COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media.

We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities.

"Vaccine importance" and "COVID-19 risk" were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize "COVID-19 risk" and "vaccine effectiveness," while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions.

This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises.

Observational studies have linked exposure to fine (PM2.5) and coarse (PM10) particulate matter air pollution with adverse COVID-19 outcomes, including higher incidence and mortality. However, some studies questioned the effect of air pollution on COVID-19 susceptibility, raising questions about the causal nature of these associations. To address this, a less biased method like Mendelian randomization (MR) is utilized, which employs genetic variants as instrumental variables to infer causal relationships in observational data.

We performed two-sample MR analysis using public genome-wide association studies data. Instrumental variables correlated with PM2.5 concentration, PM2.5 absorbance, PM2.5-10 concentration and PM10 concentration were identified. The inverse variance weighted (IVW), robust adjusted profile score (RAPS) and generalized summary data-based Mendelian randomization (GSMR) methods were used for analysis.

IVW MR analysis showed PM2.5 concentration [odd ratio (OR) = 3.29, 95% confidence interval (CI) 1.48-7.35, P-value = 0.0036], PM2.5 absorbance (OR = 5.62, 95%CI 1.98-15.94, P-value = 0.0012), and PM10 concentration (OR = 3.74, 95%CI 1.52-9.20, P-value = 0.0041) increased the risk of COVID-19 severity after Bonferroni correction. Further validation confirmed PM2.5 absorbance was associated with heightened COVID-19 severity (OR = 6.05, 95%CI 1.99-18.38, P-value = 0.0015 for RAPS method; OR = 4.91, 95%CI 1.65-14.59, P-value = 0.0042 for GSMR method) and hospitalization (OR = 3.15, 95%CI 1.54-6.47, P-value = 0.0018 for RAPS method). No causal links were observed between particulate matter exposure and COVID-19 susceptibility.

Our study established a causal relationship between smaller particle pollution, specifically PM2.5, and increased risk of COVID-19 severity and hospitalization. These findings highlight the importance of improving air quality to mitigate respiratory disease progression.

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

Ecological studies suggested a link between air pollution and severe COVID-19 outcomes, while studies accounting for individual-level characteristics are limited. In the present study, we aimed to investigate the impact of short-term ambient air pollution exposure on disease severity among a cohort of 569 laboratory confirmed COVID-19 patients admitted to designated hospitals in Zhejiang province, China, from January 17 to March 3, 2020, and elucidate the possible biological processes involved using transcriptomics. Compared with mild cases, severe cases had higher proportion of medical conditions as well as unfavorable results in most of the laboratory tests, and manifested higher air pollution exposure levels. Higher exposure to air pollutants was associated with increased risk of severe COVID-19 with odds ratio (OR) of 1.89 (95% confidence interval (CI): 1.01, 3.53), 2.35 (95% CI: 1.20, 4.61), 2.87 (95% CI: 1.68, 4.91), and 2.01 (95% CI: 1.10, 3.69) for PM2.5, PM10, NO2 and CO, respectively. OR for NO2 remained significant in two-pollutant models after adjusting for other pollutants. Transcriptional analysis showed 884 differentially expressed genes which mainly were enriched in virus clearance related biological processes between patients with high and low NO2 exposure levels, indicating that compromised immune response might be a potential underlying mechanistic pathway. These findings highlight the impact of short-term air pollution exposure, particularly for NO2, on COVID-19 severity, and emphasize the significance in mitigating the COVID-19 burden of commitments to improve air quality.

Climatic change, which influences population growth and land usage, has been theorized to be linked to the emergence and spread of new viruses like the currently unfolding COVID-19 pandemic. In this chapter, we explain how climate change may have altered the beginning, transmission, and maybe even the sickness consequences of the COVID-19 pandemic. Where possible, we also provide mechanistic explanations for how this may have occurred. We have presented evidence that suggests climate change may have had a role in the establishment and transmission of SARS-CoV-2 infection, and most possibly even in some of its clinical effects. Human activities bringing people into closer contact with bats and animals like pangolins that potentially represent the intermediate hosts, and evidence that climate-induced changes in vegetation are the main reservoir source of coronaviruses for human infection, are among the explanations. Although there are still unsubstantiated indications that the first viral pathogen may have escaped from a laboratory, it is possible that this encounter took place in the field or in marketplaces in the instance of COVID-19. We also present the argument that climate change is working to enhance transmission between diseased and uninfected humans, and this is true regardless of the source of the original development of the disease. Changes in temperature and humidity make it easier for viruses to survive, and the impacts of industrial pollution induce people to cough and sneeze, which releases highly infectious aerosols into the air. These three factors combine to make this a more likely scenario than it would otherwise be. We suggest that changes in climate are contributing to create conditions that are favorable for the development of more severe symptoms of illness. It is more difficult to build the argument for this circumstance, and much of it is indirect. However, climate change has caused some communities to adjust their nutritional habits, both in terms of the quantity of food they eat and the quality of the foods they consume. The effects frequently become apparent as a result of alterations that are imposed on the microbiome of the gut, which, in turn, influence the types of immune responses that are produced. The incidence of comorbidities like diabetes and animal vectors like bats that transmit other illnesses that modify vulnerability to SARS-CoV-2 are also two examples of the factors that have been affected by climate change. In order to curb the development of infectious illnesses caused by new viruses, it is necessary to understand the connection between environmental dynamics and the emergence of new coronaviruses. This knowledge should lead to initiatives aimed at reducing global greenhouse gas emissions.

The SARS-CoV-2 (COVID-19) pandemic has highlighted the urgent need for strategies that rapidly inactivate airborne respiratory viruses and break the transmission cycle of indoor spaces. Air ions can reduce viable bacteria, mold, and virus counts, however, most studies use small test enclosures with target microbes and ion sources in close vicinity. To evaluate ion performance in real-world spaces, experiments were conducted in a large, room-size BSL-3 Chamber. Negative and positive ions were delivered simultaneously using a commercially available bipolar air ion device. The device housed Needle Point Bipolar ionization (NPBI) technology. Large chamber studies often use unrealistically high virus concentrations to ensure measurable virus is present at the trial end. However, excessively high viral concentrations bias air cleaning devices towards underperformance. Hence, devices that provide a substantial impact for protecting occupants in real-world spaces with real-world virus concentrations are often dismissed as poor performers. Herein, both real-world and excessive virus concentrations were studied using Influenza A and B, Human Respiratory Syncytial Virus (RSV), and the SARS-CoV-2 Alpha and Delta strains. The average ion concentrations ranged from 4,100 to 24,000 per polarity over 60-minute and 30-minute time trials. The reduction rate was considerably greater for trials that used real-world virus concentrations, reducing infectivity for Influenza A and B, RSV, and SARS-CoV-2 Delta by 88.3-99.98% in 30 minutes, whereas trials using in-excess concentrations showed 49.5-61.2% in 30 minutes. These findings strongly support the addition of NPBI ion technology to building management strategies aimed to protect occupants from contracting and spreading infective respiratory viruses indoors.

Understanding how non-household activities contributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections under different levels of national health restrictions is vital.

Among adult Virus Watch participants in England and Wales, we used multivariable logistic regressions and adjusted-weighted population attributable fractions (aPAF) assessing the contribution of work, public transport, shopping, and hospitality and leisure activities to infections.

Under restrictions, among 17,256 participants (502 infections), work [adjusted odds ratio (aOR) 2.01 (1.65-2.44), (aPAF) 30% (22-38%)] and transport [(aOR 1.15 (0.94-1.40), aPAF 5% (-3-12%)], were risk factors for SARS-CoV-2 but shopping, hospitality and leisure were not. Following the lifting of restrictions, among 11,413 participants (493 infections), work [(aOR 1.35 (1.11-1.64), aPAF 17% (6-26%)] and transport [(aOR 1.27 (1.04-1.57), aPAF 12% (2-22%)] contributed most, with indoor hospitality [(aOR 1.21 (0.98-1.48), aPAF 7% (-1-15%)] and leisure [(aOR 1.24 (1.02-1.51), aPAF 10% (1-18%)] increasing. During the Omicron variant, with individuals more socially engaged, among 11,964 participants (2335 infections), work [(aOR 1.28 (1.16-1.41), aPAF (11% (7-15%)] and transport [(aOR 1.16 (1.04-1.28), aPAF 6% (2-9%)] remained important but indoor hospitality [(aOR 1.43 (1.26-1.62), aPAF 20% (13-26%)] and leisure [(aOR 1.35 (1.22-1.48), aPAF 10% (7-14%)] dominated.

Work and public transport were important to transmissions throughout the pandemic with hospitality and leisure's contribution increasing as restrictions were lifted, highlighting the importance of restricting leisure and hospitality alongside advising working from home, when facing a highly infectious and virulent respiratory infection.

Several studies have examined the possible relationship between air pollutants and the risk of COVID-19 but most returned controversial findings. We tried to assess the association between (short- and long-term) exposure to particulate and gaseous pollutants, SARS-CoV-2 infections, and immune response in a population of healthcare workers (HCWs) with well-characterized individual data. We collected occupational and clinical characteristics of all HCWs who performed a nasopharyngeal swab (NPS) for detecting SARS-CoV-2 at the Policlinico Hospital in Milan (Lombardy, Italy) between February 24, 2020 (day after first documented case of COVID-19 in our hospital) and December 26, 2020 (day before start of the vaccination campaign). Each subject was assigned daily average levels of particulate matter ≤10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) retrieved from the air quality monitoring station closest to his/her residential address. Air pollution data were treated as time-dependent variables, generating person-days at risk. Multivariate Poisson regression models were fit to evaluate the rate of positive NPS and to assess the association between air pollution and antibody titer among NPS-positive HCWs. Among 3712 included HCWs, 635 (17.1%) had at least one positive NPS. A 10 μg/m3 increase in NO2 average concentration in the four days preceding NPS was associated with a higher risk of testing positive [Incidence Rate Ratio (IRR) = 1.08, 95% confidence interval (CI): 1.01; 1.16)]. When considering a 1 μg/m3 increase in 2019 annual NO2 average, we observed a higher risk of infection (IRR: 1.02, 95%CI: 1.00; 1.03) and an increased antibody titer (+2.4%, 95%CI: 1.1; 3.6%). Findings on PM10 and O3 were less consistent and, differently from NO2, were not confirmed in multipollutant models. Our study increases the body of evidence suggesting an active role of air pollution exposure on SARS-CoV-2 infection and confirms the importance of implementing pollution reduction policies to improve public health.

Evidence suggests that air pollution, specifically the particulate matters PM2.5 and PM10, plays a key role in exacerbating the risk of prolonged symptoms following COVID-19 infection.

This study endeavors to elucidate the potential interaction between chronic air pollution exposure and the manifestation of long COVID symptoms within a cohort based in Makkah, Saudi Arabia.

Participants included residents from the Makkah region who had recovered from COVID-19 between 2022 and 2023. A comprehensive questionnaire was utilized to gather detailed demographic data and assess the persistent symptoms seen during the post-COVID period. To gauge the environmental exposure to potential risk factors, air sampling for PM10 and PM2.5 was systematically conducted in various locations in Makkah over a year.

Significant positive associations were found between PM2.5 and PM10 exposure and long COVID. Furthermore, specific symptom analysis revealed a significant association between air pollution and shortness of breath (for PM2.5). Only PM2.5 exposure remained statistically significant (RR = 1.32, 95% CI: 1.05, 1.67). In contrast, the association with PM10 remained on the cusp of significance, with an RR of 1.27 (95% CI: 1.00, 1.61).

This study highlights the importance of reducing air pollution levels to mitigate the long-term health consequences of COVID-19.

During the early stage of the COVID-19 pandemic (winter 2020), the northern part of Italy has been significantly affected by viral infection compared to the rest of the country leading the scientific community to hypothesize that airborne particulate matter (PM) could act as a carrier for the SARS-CoV-2. To address this controversial issue, we first verified and demonstrated the presence of SARS-CoV-2 RNA genome on PM2.5 samples, collected in the city of Bologna (Northern Italy) in winter 2021. Then, we employed classical molecular dynamics (MD) simulations to investigate the possible recognition mechanism(s) between a newly modelled PM2.5 fragment and the SARS-CoV-2 Spike protein. The potential molecular interaction highlighted by MD simulations suggests that the glycans covering the upper Spike protein regions would mediate the direct contact with the PM2.5 carbon core surface, while a cloud of organic and inorganic PM2.5 components surround the glycoprotein with a network of non-bonded interactions resulting in up to 4769 total contacts. Moreover, a binding free energy of -207.2 ± 3.9 kcal/mol was calculated for the PM-Spike interface through the MM/GBSA method, and structural analyses also suggested that PM attachment does not alter the protein conformational dynamics. Although the association between the PM and SARS-CoV-2 appears plausible, this simulation does not assess whether these established interactions are sufficiently stable to carry the virus in the atmosphere, or whether the virion retains its infectiousness after the transport. While these key aspects should be verified by further experimental analyses, for the first time, this pioneering study gains insights into the molecular interactions between PM and SARS-CoV-2 Spike protein and will support further research aiming at clarifying the possible relationship between PM abundance and the airborne diffusion of viruses.

Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries' COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure.

Enveloped viruses have been the leading causative agents of viral epidemics in the past decade, including the ongoing coronavirus disease 2019 outbreak. In epidemics caused by enveloped viruses, direct contact is a common route of infection, while indirect transmissions through the environment also contribute to the spread of the disease, although their significance remains controversial. Bridging the knowledge gap regarding the influence of interfacial interactions on the persistence of enveloped viruses in the environment reveals the transmission mechanisms when the virus undergoes mutations and prevents excessive disinfection during viral epidemics. Herein, from the perspective of the driving force, partition efficiency, and viral survivability at interfaces, we summarize the viral and environmental characteristics that affect the environmental transmission of viruses. We expect to provide insights for virus detection, environmental surveillance, and disinfection to limit the spread of severe acute respiratory syndrome coronavirus 2.

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

the different lockdown phases due to COVID-19 may have negatively impacted pregnant women's mental health. Concerning antenatal stress, most studies focused more on the effect of pandemic onset than on the impact of the following phases and restrictions.

the present study aimed to assess levels of anxiety and depression in a group of Italian pregnant women during the second COVID-19 phase and possible risk factors.

We recruited 156 pregnant women through our Perinatal Psychology Outpatient Clinic. We divided the sample into women recruited before the pandemic (N = 88) through a face-to-face antenatal class and pregnant women recruited during the second lockdown (Covid-19 study group) (November 2020-April 2021) through the same antenatal class via Skype (N = 68). To investigate depressive and anxiety symptoms, we administered the Edinburgh Postnatal Depression Scale (EPDS) and the State and Trait Anxiety Inventory (STAI-Y) scales; women's medical history and obstetric data were collected.

women belonging to the Covid-19 study group reported higher depressive and anxious levels than the pre-pandemic group. Moreover, during the second lockdown previous psychopathology and being close to childbirth were related to increased depression; a history of abortion was related to higher levels of trait anxiety instead.

recurrent restrictive measures due to COVID-19 negatively affected the antenatal mental well-being of pregnant women, particularly worsening depressive and anxious feelings. Pregnant women during the pandemic represented a vulnerable population to be monitored more closely and timely to prevent psychological problems in the postpartum period and their consequences on the child.

anxiety, depression, pregnancy, COVID-19, mental health, lockdown.

The first aim of study was to quantify the viral load in the wastewater samples by RT-qPCR testing in Lahore population to estimate the number of patients affected and predict the next resurgence of COVID-19 wave in the city. The second aim of the study was to determine the hotspot areas of Lahore which remained positive more often for virus with high viral load. In this study, n = 420 sewage samples were collected on an average of two weeks intervals from 30 different sewage water disposal stations (14 sampling events) from Sept 2020 to March 2021. RNA was extracted and quantified by RT-qPCR without concentrating the virus in samples. Number of positive disposal sites (7-93%), viral load from sewage samples (100.296 to 103.034), and estimated patients (660-17,030) ranged from low to high according to the surge and restrain of 2nd and 3rd COVID-19 waves in the country. The viral load and estimated patients were reported high in January 2021 and March 2021 which were similar to the peak of 2nd and 3rd waves in Pakistan. Site 18 (Niaz Baig village DS) showed the highest viral load among all sites. Findings of the present study helped to estimate the number of patients and track the resurgence in COVID-19 waves in Lahore particularly, and in Punjab generally. Furthermore, it emphasizes the role of wastewater-based epidemiology to help policymakers strengthen the quarantine measures along with immunization to overcome enteric viral diseases. Local and national stake holders should work in collaboration to improve the environmental hygiene to control the disease.