The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19

Frequencies of micronuclei in buccal cells and their spatial distribution in a population living in proximity to coal mining areas in southern Brazil

The extraction and burning of coal release genotoxic pollutants, and understanding the relationship between genetic damage and the spatial distribution of residences in coal-using regions is crucial. The study aimed to conduct a spatial analysis of genotoxic damage through the of micronuclei (MNs) number and their proximity to coal mining/burning in the largest coal exploration region in Brazil. In this study, the detection of genotoxic damage was performed using the MN assay in oral cells of residents exposed to coal mining activities. Spatial analysis was conducted using QGIS 3.28.10 based on information obtained from a questionnaire administered to the population. Multiple linear regression analysis was carried out to assess the influence of the distance from residential areas to polluting sources on the number of MNs found. Additionally, Spearman's correlation was performed to identify the strength and direction of the association between the frequency of MNs and each of the polluting sources. A total of 147 MNs were quantified among all participants in the coal mining region. Notably, residents living within 2 km and 10 km of pollution sources exhibited the highest prevalence of MNs. The analysis demonstrated a significant correlation between closer proximity to pollution sources and increased MN frequency, underscoring the spatial relationship between these sources and genotoxic damage. Environmental pollutants from anthropogenic sources present a major health risk, potentially leading to irreversible damage. The spatial analysis in this study highlights the importance of targeted public policies. These policies should aim for a sustainable balance between economic development and public health, promoting effective measures to mitigate environmental impacts and protect community health.

Influence of Environmental Risk Exposure on the Determinants of COVID-19 Booster Vaccination in an Urban Thai Population

This study aimed to identify the influence of environmental risk exposure levels on the predictive factors of COVID-19 booster dose vaccination in an urban Thai population in the post-pandemic era. Six study locations, including the three provinces with the highest environmental risk levels and the three provinces with the lowest environmental risk levels, were selected by calculating the environmental risk exposure indexes. Participants from the capital district of each province were chosen via the simple random sampling technique and interviewed using a structured questionnaire. A total of 1315 individuals were included in a sample in this study, and the best predictors of booster dose vaccination were determined using multiple regression analysis. The results showed that a high level of environmental risk exposure occurred in the provinces with a high number of total days exceeding the limits set for PM10 and high rates of mortality for lung cancer. The number of COVID-19 booster vaccinations given amount to 43.4% of the population during the post-COVID-19 pandemic period. Our multivariate analysis indicated that individuals in the working age group (≥25 years old); those with higher education (diploma degree and above); full-time employment (government and private sectors); those with high monthly incomes (≥USD144.1); and those in areas with the lowest risk level of environmental exposure significantly contributed to the number of booster dose vaccinations given during the post-pandemic period. To summarize, the rate of COVID-19 booster dose vaccination acceptance in Thailand was influenced by socio-economic factors with environmental concerns. These findings improve our understating of both the global pandemic and how environmental exposure affects behavioral change patterns and could improve the effectiveness of post-pandemic management.

The impact of ambient temperature and air pollution on SARS-CoV2 infection and Post COVID-19 condition in Belgium (2021-2022)

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Association Between Nitrogen Dioxide Pollution and Cause-Specific Mortality in China: Cross-Sectional Time Series Study

BACKGROUND

Nitrogen dioxide (NO2) has been frequently linked to a range of diseases and associated with high rates of mortality and morbidity worldwide. However, there is limited evidence regarding the risk of NO2 on a spectrum of causes of mortality. Moreover, adjustment for potential confounders in NO2 analysis has been insufficient, and the spatial resolution of exposure assessment has been limited.

OBJECTIVE

This study aimed to quantitatively assess the relationship between short-term NO2 exposure and death from a range of causes by adjusting for potential confounders in Guangzhou, China, and determine the modifying effect of gender and age.

METHODS

A time series study was conducted on 413,703 deaths that occurred in Guangzhou during the period of 2010 to 2018. The causes of death were classified into 10 categories and 26 subcategories. We utilized a generalized additive model with quasi-Poisson regression analysis using a natural cubic splines function with lag structure of 0 to 4 days to estimate the potential lag effect of NO2 on cause-specific mortality. We estimated the percentage change in cause-specific mortality rates per 10 μg/m3 increase in NO2 levels. We stratified meteorological factors such as temperature, humidity, wind speed, and air pressure into high and low levels with the median as the critical value and analyzed the effects of NO2 on various death-causing diseases at those high and low levels. To further identify potentially vulnerable subpopulations, we analyzed groups stratified by gender and age.

RESULTS

A significant association existed between NO2 exposure and deaths from multiple causes. Each 10 μg/m3 increment in NO2 density at a lag of 0 to 4 days increased the risks of all-cause mortality by 1.73% (95% CI 1.36%-2.09%) and mortality due to nonaccidental causes, cardiovascular disease, respiratory disease, endocrine disease, and neoplasms by 1.75% (95% CI 1.38%-2.12%), 2.06% (95% CI 1.54%-2.59%), 2.32% (95% CI 1.51%-3.13%), 2.40% (95% CI 0.84%-3.98%), and 1.18% (95% CI 0.59%-1.78%), respectively. Among the 26 subcategories, mortality risk was associated with 16, including intentional self-harm, hypertensive disease, and ischemic stroke disease. Relatively higher effect estimates of NO2 on mortality existed for low levels of temperature, relative humidity, wind speed, and air pressure than with high levels, except a relatively higher effect estimate was present for endocrine disease at a high air pressure level. Most of the differences between subgroups were not statistically significant. The effect estimates for NO2 were similar by gender. There were significant differences between the age groups for mortality due to all causes, nonaccidental causes, and cardiovascular disease.

CONCLUSIONS

Short-term NO2 exposure may increase the risk of mortality due to a spectrum of causes, especially in potentially vulnerable populations. These findings may be important for predicting and modifying guidelines for NO2 exposure in China.

Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective

It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.

The effects of meteorological factors on the COVID-19 omicron variant in Bangladesh

The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.

Air pollution exposure, SARS-CoV-2 infection, and immune response in a cohort of healthcare workers of a large university hospital in Milan, Italy

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.

Impact of environmental factors on COVID-19 transmission: spatial variations in the world

The COVID-19 pandemic caused enormous destruction to global health and the economy and has surged worldwide with colossal morbidity and mortality. The pattern of the COVID infection varies in diverse regions of the world based on the variations in the geographic environment. The multivariate generalized linear regression models: zero-inflated negative binomial regression, and the zero-inflated Poisson regression model, have been employed to determine the significant meteorological factors responsible for the spread of the pandemic in different continents. Asia experienced a high COVID-19 infection, and death was extreme in Europe. Relative humidity, air pressure, and wind speed are the salient factors significantly impacting the spread of COVID-19 in Africa. Death due to COVID-19 in Asia is influenced by air pressure, temperature, precipitation, and relative humidity. Air pressure and temperature substantially affect the spread of the pandemic in Europe.

An analysis of atmospheric stability indices and parameters under air pollution conditions

The stability of the atmosphere plays an important role in changes in air pollutant concentrations. Stable atmospheric conditions cause pollutant concentrations to reach high values, which degrades the air quality in a particular region. This study aims to reveal the relationship between atmospheric stability indices/parameters (thermodynamic indices) and changes in air pollutant concentrations. Pollutant concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 were statistically analyzed for a 10-year (2013-2022) period for nine air quality stations located in the megacity Istanbul. Based on national and international air quality standards, 145 episode days were determined for the days when these parameters exceeded the threshold values. Five stability indices (Showalter Index - SI, Lifted Index - LI, Severe Weather Index - SWEAT, K Index - KI, Totals Totals Index - TTI), and three stability parameters (Convective Available Potential Energy - CAPE, Convective Inhibition - CIN, Bulk Richardson Number - BRN) were used to determine the stability of the atmosphere for episode days. It has been found that in cases where air pollutant concentrations are high, the stability parameters reveal the stability of the atmosphere better than the stability indices. It was also found that there was at least one vertical inversion layer on 122 of the 145 episode days, these layers mostly (84%) occurred between the surface and 850 hPa levels, and the layer thicknesses were mostly between 0-250 m (84%).

Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China

The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.

Association of the corona virus (Covid-19) epidemic with environmental risk factors

The current outbreak of the novel coronavirus SARS-CoV-2 (coronavirus disease 2019; previously 2019-nCoV), epicenter in Hubei Province (Wuhan), People's Republic of China, has spread too many other countries. The transmission of the corona virus occurs when people are in the incubation stage and do not have any symptoms. Therefore, the role of environmental factors such as temperature and wind speed becomes very important. The study of Acute Respiratory Syndrome (SARS) indicates that there is a significant relationship between temperature and virus transmission and three important factors, namely temperature, humidity and wind speed, cause SARS transmission. Daily data on the incidence and mortality of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website (WMW) for several major cities in Iran and the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, wind speed, dew point and air quality index (AQI) index are extracted from the website of the World Meteorological Organization (WMO), The National Aeronautics and Space Administration (NASA) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Statistical analysis carried out for significance relationships. The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. The relationship between AQI and number of infected was significant in all cities. In Canberra, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and wind speed. There is a significant positive relationship between the number of infected people in a day and the dew point in the cities of Canberra, Wellington and Washington. The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. There was significant relationship between Dew point and prevalence. Wind speed showed a significant relationship in USA, Madrid and Paris. AQI was strongly associated with the prevalence of covid19. The purpose of this study is to investigate some environmental factors in the transmission of the corona virus.

Animal experiments on respiratory viruses and analogous studies of infection factors for interpersonal transmission

Air pollution caused by SARS-CoV-2 and other viruses in human settlements will have a great impact on human health, but also a great risk of transmission. The transmission power of the virus can be represented by quanta number in the Wells-Riley model. In order to solve the problem of different dynamic transmission scenarios, only a single influencing factor is considered when predicting the infection rate, which leads to large differences in quanta calculated in the same space. In this paper, an analog model is established to define the indoor air cleaning index RL and the space ratio parameter. Based on infection data analysis and rule summary in animal experiments, factors affecting quanta in interpersonal communication were explored. Finally, by analogy, the factors affecting person-to-person transmission mainly include viral load of infected person, distance between individuals, etc., the more severe the symptoms, the closer the number of days of illness to the peak, and the closer the distance to the quanta. In summary, there are many factors that affect the infection rate of susceptible people in the human settlement environment. This study provides reference indicators for environmental governance under the COVID-19 epidemic, provides reference opinions for healthy interpersonal communication and human behavior, and provides some reference for accurately judging the trend of epidemic spread and responding to the epidemic.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

The change in the relationship between temperature and respiratory diseases among children in Guangzhou, China

Temperature is closely associated with respiratory disease (RD) in children, but few studies have examined whether the relationship between ambient temperature and RD in children changed after the COVID-19 epidemic. The aim of this study was to assess the relationship between temperature and RD in children after the COVID-19 epidemic in Guangzhou, China. We used a distributed lag nonlinear model to compare the relationship between temperature and RD among children in Guangzhou from 2018 to 2022. The results showed an S-shaped relationship between temperature and RD in the post-COVID-19 period with a reference minimum risk at a temperature of 21 °C and an increasing relative risk (RR) at extremely low temperature (ELT) and extremely high temperature (EHT). The highest RR associated with EHT was 1.935 (95% confidence interval [CI]: 1.314-2.850) at a lag of 0-14 days. The on-the-day lag effects were found to be strongest at the lag 0 day of EHT with a RR of 1.167 (95% CI: 1.021-1.334). Furthermore, each 1 °C increase in post-COVID-19 temperature increased the risk of RD by 8.2% (95% CI: 1.044-1.121). Our study provides evidence that the relationship between temperature and RD in children in Guangzhou changed after the COVID-19 epidemic, and high temperature is more likely to cause RD in children. Relevant government departments and parents should understand the relationship between temperature and RD in children and develop new preventive measures.

Effects of High Temperature on COVID-19 Deaths in U.S. Counties

The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record-hot or top-10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID-19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID-19 cases and deaths. However, the associations between temperature and COVID-19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID-19 deaths during the summer of 2021 to examine the relationship between COVID-19 deaths and temperature by applying a two-stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID-19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID-19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID-19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from -0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID-19 deaths, which might help us to understand the underlying modulation of the COVID-19 pandemic by meteorological variables and to develop epidemic policy response strategies.

Ambient air pollution exposure linked to long COVID among young adults: a nested survey in a population-based cohort in Sweden

Background

Post COVID-19 conditions, also known as long COVID, are of public health concern, but little is known about their underlying risk factors. We aimed to investigate associations of air pollution exposure with long COVID among Swedish young adults.

Methods

We used data from the BAMSE (Children, Allergy, Environment, Stockholm, Epidemiology [in Swedish]) cohort. From October 2021 to February 2022 participants answered a web-questionnaire focusing on persistent symptoms following acute SARS-CoV-2 infection. Long COVID was defined as symptoms after confirmed infection with SARS-CoV-2 lasting for two months or longer. Ambient air pollution levels (particulate matter ≤2.5 μm [PM_2.5], ≤10 μm [PM₁₀], black carbon [BC] and nitrogen oxides [NO_x]) at individual-level addresses were estimated using dispersion modelling.

Findings

A total of 753 participants with SARS-CoV-2 infection were included of whom 116 (15.4%) reported having long COVID. The most common symptoms were altered smell/taste (n = 80, 10.6%), dyspnea (n = 36, 4.8%) and fatigue (n = 34, 4.5%). Median annual PM_2.5 exposure in 2019 (pre-pandemic) was 6.39 (interquartile range [IQR] 6.06-6.71) μg/m³. Adjusted Odds Ratios (95% confidence intervals) of PM_2.5 per IQR increase were 1.28 (1.02-1.60) for long COVID, 1.65 (1.09-2.50) for dyspnea symptoms and 1.29 (0.97-1.70) for altered smell/taste. Positive associations were found for the other air pollutants and remained consistent across sensitivity analyses. Associations tended to be stronger among participants with asthma, and those having had COVID during 2020 (versus 2021).

Interpretation

Ambient long-term PM_2.5 exposure may affect the risk of long COVID in young adults, supporting efforts for continuously improving air quality.

Funding

The study received funding from the Swedish Research Council (grant no. 2020-01886, 2022-06340), the Swedish Research Council for Health, Working life and Welfare (FORTE grant no. 2017-01146), the Swedish Heart-Lung Foundation, Karolinska Institute (no. 2022-01807) and Region Stockholm (ALF project for cohort and database maintenance).

Combined Impact of Omicron Vaccination and Environmental Risk Exposure: A Thailand Case Study

This research aimed to determine the levels of COVID-19 booster dose vaccinations in Thai populations in areas with environmental risk exposure during the Omicron outbreak. Five of twenty provinces in Thailand were selected by assessing environmental risk exposure for study settings. A total of 1038 people were interviewed by a structured questionnaire. The predicting factors of COVID-19 booster dose vaccinations were analyzed by univariate and multivariate analysis. The results showed that 69.4% (95% CI 66.5-72.1) of the population was vaccinated with COVID-19 booster doses. Multiple logistics regression revealed that the female gender (AOR 1.49, 95% CI 1.11-2.00), all age groups from 38 to 60 years old, all education levels of at least secondary school, high income (AOR 1.16, 95% CI 1.15-2.24), populations having experience with COVID-19 infection (AOR 2.27, 95% CI 2.05-3.76), knowledge of vaccine (AOR 1.78, 95% CI 1.11-2.83), and trusting attitude (AOR 1.76, 95% CI 1.32-2.36) were factors among those more likely to take COVID-19 booster dose vaccinations in high-environmental-risk-exposure areas. Therefore, an effective booster dose campaign with education programs to increase attitudes toward booster vaccinations should be implemented for the resilience of COVID-19 prevention and control.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

The impact of air pollution on COVID-19 incidence, severity, and mortality: A systematic review of studies in Europe and North America

BACKGROUND

Air pollution is speculated to increase the risks of COVID-19 spread, severity, and mortality.

OBJECTIVES

We systematically reviewed studies investigating the relationship between air pollution and COVID-19 cases, non-fatal severity, and mortality in North America and Europe.

METHODS

We searched PubMed, Web of Science, and Scopus for studies investigating the effects of harmful pollutants, including particulate matter with diameter ≤2.5 or 10 μm (PM2.5 or PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO), on COVID-19 cases, severity, and deaths in Europe and North America through to June 19, 2021. Articles were included if they quantitatively measured the relationship between exposure to air pollution and COVID-19 health outcomes.

RESULTS

From 2,482 articles screened, we included 116 studies reporting 355 separate pollutant-COVID-19 estimates. Approximately half of all evaluations on incidence were positive and significant associations (52.7%); for mortality the corresponding figure was similar (48.1%), while for non-fatal severity this figure was lower (41.2%). Longer-term exposure to pollutants appeared more likely to be positively associated with COVID-19 incidence (63.8%). PM2.5, PM10, O3, NO2, and CO were most strongly positively associated with COVID-19 incidence, while PM2.5 and NO2 with COVID-19 deaths. All studies were observational and most exhibited high risk of confounding and outcome measurement bias.

DISCUSSION

Air pollution may be associated with worse COVID-19 outcomes. Future research is needed to better test the air pollution-COVID-19 hypothesis, particularly using more robust study designs and COVID-19 measures that are less prone to measurement error and by considering co-pollutant interactions.

Modeling Key Strategies for Reducing Socio-Economic and Health Crisis: Perspective from COVID-19 Pandemic

The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that "strong leadership and control" and "awareness on social media" play significant roles in reducing pandemic challenges, while "promoting online purchase behavior" and "online education" are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.

The impact of crowd gatherings on the spread of COVID-19

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Does climate help modeling COVID-19 risk and to what extent?

A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

Vitamin D prescribing practices among clinical practitioners during the COVID-19 pandemic

Background and Aims

COVID-19 has caused devastation globally. Low vitamin D status, particularly during the winter months, remains commonplace around the world, and it is thought to be one of the contributing factors toward causation and severity of COVID-19. Many guidelines do not recommend vitamin D for the treatment or prevention of the disease. Hence, we set out to conduct a global survey to understand the use and prescribing habits of vitamin D among clinicians for COVID-19.

Methods

An online anonymous questionnaire was sent to clinicians enquiring about their prescribing habits of vitamin D and personal use of vitamin D. Data of the survey were collected between January 15, 2021, and February 13, 2021.

Results

Four thousand four hundred forty practicing clinicians were included in the analysis, with the majority of those responding from Asia, followed by Europe. 82.9% prescribed vitamin D before COVID-19, more commonly among general practitioners (GPs) in comparison with medical specialists, and Asian clinicians were more likely to prescribe vitamin D in comparison with Caucasian physicians (p < 0.01). GPs were also more likely to prescribe vitamin D prophylactically to prevent COVID-19 in comparison with medical specialists (OR 1.47, p < 0.01). Most GPs (72.8%) would also prescribe vitamin D to treat COVID-19 in comparison with medical specialists (OR 1.81, p < 0.01), as well as more Asian in comparison with Caucasian physicians (OR 4.57, p < 0.01). 80.4% of respondents were taking vitamin D, more so in the 45-54 and 65-74 age groups in comparison with the 18-24 years category (OR 2.15 and 2.40, respectively, both p < 0.05), many of whom did so before COVID-19 (72.1%).

Conclusion

This survey has shown that many clinicians would prescribe vitamin D for the prevention and treatment of COVID-19. The majority would also recommend measuring vitamin D levels, but not so in patients with COVID-19.

Short-term influence of environmental factors and social variables COVID-19 disease in Spain during first wave (Feb-May 2020)

This study aims to identify the combined role of environmental pollutants and atmospheric variables at short term on the rate of incidence (TIC) and on the hospital admission rate (TIHC) due to COVID-19 disease in Spain. This study used information from 41 of the 52 provinces of Spain (from Feb. 1, 2021 to May 31, 2021). Using TIC and TIHC as dependent variables, and average daily concentrations of PM₁₀ and NO₂ as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). Generalized linear models (GLM) with Poisson link were carried out for each provinces The GLM model controlled for trend, seasonalities, and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 μg/m³ in PM₁₀ and NO₂ and by 1 °C in the case of Tmax and 1 g/m³ in the case of HA. Later, a linear regression was carried out that included the social determinants of health. Statistically significant associations were found between PM₁₀, NO₂, and the rate of COVID-19 incidence. NO₂ was the variable that showed greater association, both for TIC as well as for TIHC in the majority of provinces. Temperature and HA do not seem to have played an important role. The geographic distribution of RR in the studied provinces was very much heterogeneous. Some of the health determinants considered, including income per capita, presence of airports, average number of diesel cars per inhabitant, average number of nursing personnel, and homes under 30 m² could explain the differential geographic behavior. As findings indicates, environmental factors only could modulate the incidence and severity of COVID-19. Moreover, the social determinants and public health measures could explain some patterns of geographically distribution founded.

Biological Properties of SARS-CoV-2 Variants: Epidemiological Impact and Clinical Consequences

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that belongs to the coronavirus family and is the cause of coronavirus disease 2019 (COVID-19). As of May 2022, it had caused more than 500 million infections and more than 6 million deaths worldwide. Several vaccines have been produced and tested over the last two years. The SARS-CoV-2 virus, on the other hand, has mutated over time, resulting in genetic variation in the population of circulating variants during the COVID-19 pandemic. It has also shown immune-evading characteristics, suggesting that vaccinations against these variants could be potentially ineffective. The purpose of this review article is to investigate the key variants of concern (VOCs) and mutations of the virus driving the current pandemic, as well as to explore the transmission rates of SARS-CoV-2 VOCs in relation to epidemiological factors and to compare the virus's transmission rate to that of prior coronaviruses. We examined and provided key information on SARS-CoV-2 VOCs in this study, including their transmissibility, infectivity rate, disease severity, affinity for angiotensin-converting enzyme 2 (ACE2) receptors, viral load, reproduction number, vaccination effectiveness, and vaccine breakthrough.

Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters

The study aims to investigate associations between air pollution, climate parameters, and the diffusion of COVID-19-confirmed cases in Turkey using Spearman's correlation test as an empirical methodology by Statgraphics Centurion XVI (version 16.1) and to determine the risk factors accelerating the spread of SARS-CoV-2 virus. The present study demonstrates the strong impacts of air pollutants and weather conditions on the transmission of COVID-19 morbidity. Particularly, O3 and PM10 from air quality parameters exhibited the strongest correlation with the number of daily cases in Kütahya (rs = -0.62; p < 0.05) and Sivas (rs = -0.62; p < 0.05) provinces, respectively. In meteorological parameters, rainfall showed the highest impact (rs = 0.76; p < 0.05) on the number of daily COVID-19 cases in Denizli distinct. Moreover, this study suggested that the diffusion of the novel coronavirus SARS-CoV-2 in regions with high levels of air pollution and low wind speed is dominant. To prevent the negative effects of the future pandemic crisis on public health and economic systems, manifold implications to encourage strategies to reduce air pollution in the polluted region such as being prevalent the usage of renewable energy technologies in particular electricity generation and sustainable policies such as improving the health system should be implemented by decision-makers.

Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging three-dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy-makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.

COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors

How is the dynamics of Coronavirus Disease 2019 (COVID-19) in 2020 with an health policy of full lockdowns and in 2021 with a vast campaign of vaccinations? The present study confronts this question here by developing a comparative analysis of the effects of COVID-19 pandemic between April-September 2020 (based upon strong control measures) and April-September 2021 (focused on health policy of vaccinations) in Italy, which was one of the first European countries to experience in 2020 high numbers of COVID-19 related infected individuals and deaths and in 2021 Italy has a high share of people fully vaccinated against COVID-19 (>89% of population aged over 12 years in January 2022). Results suggest that over the period under study, the arithmetic mean of confirmed cases, hospitalizations of people and admissions to Intensive Care Units (ICUs) in 2020 and 2021 is significantly equal (p-value<0.01), except fatality rate. Results suggest in December 2021 lower hospitalizations, admissions to ICUs, and fatality rate of COVID-19 than December 2020, though confirmed cases and mortality rates are in 2021 higher than 2020, and likely converging trends in the first quarter of 2022. These findings reveal that COVID-19 pandemic is driven by seasonality and environmental factors that reduce the negative effects in summer period, regardless control measures and/or vaccination campaigns. These findings here can be of benefit to design health policy responses of crisis management considering the growth of COVID-19 pandemic in winter months having reduced temperatures and low solar radiations ( COVID-19 has a behaviour of influenza-like illness). Hence, findings here suggest that strategies of prevention and control of infectious diseases similar to COVID-19 should be set up in summer months and fully implemented during low-solar-irradiation periods (autumn and winter period).

Ambient air pollution and COVID-19 incidence during four 2020-2021 case surges

BACKGROUND

Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure.

OBJECTIVE

To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021.

METHODS

The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM_2.5, NO₂, and O₃) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020-5/31/2020, 6/1/202-9/30/2020, 10/1/2020-12/31/2020, and 1/1/2021-2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation.

RESULTS

The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM_2.5, NO₂, and O₃ were 1.11 (1.04, 1.18) per 2.3 μg/m³,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 μg/m³ for PM_2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO₂ and 0.96 (0.85, 1.08) per 12.0 ppb for O₃.

CONCLUSION

Long-term PM_2.5 and NO₂ exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM_2.5 and NO₂ exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

Airborne Transmission of Foot-and-Mouth Disease Virus: A Review of Past and Present Perspectives

The primary transmission route for foot-and-mouth disease (FMD), a contagious viral disease of cloven-hoofed animals, is by direct contact with infected animals. Yet indirect methods of transmission, such as via the airborne route, have been shown to play an important role in the spread of the disease. Airborne transmission of FMD is referred to as a low probability- high consequence event as a specific set of factors need to coincide to facilitate airborne spread. When conditions are favourable, airborne virus may spread rapidly and cause disease beyond the imposed quarantine zones, thus complicating control measures. Therefore, it is important to understand the nature of foot-and-mouth disease virus (FMDV) within aerosols; how aerosols are generated, viral load, how far aerosols could travel and survive under different conditions. Various studies have investigated emissions from infected animals under laboratory conditions, while others have incorporated experimental data in mathematical models to predict and trace outbreaks of FMD. However, much of the existing literature focussing on FMDV in aerosols describe work which was undertaken over 40 years ago. The aim of this review is to revisit existing knowledge and investigate how modern instrumentation and modelling approaches can improve our understanding of airborne transmission of FMD.

Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-022-04190-z.

The global transmission of new coronavirus variants

Coronavirus Disease 2019 (COVID-19) has caused tremendous losses to the world. This study addresses the impact and diffusion of the five major new coronavirus variants namely Alpha, Beta, Gamma, Eta, and Delta lineage. The results of this study indicate that Africa and Europe will be affected by new coronavirus variants the most compared with other continents. The comparative analysis indicates that vaccination can contain the spread of the virus in most of the continent, and non-pharmaceutical interventions (NPIs), such as restriction on gatherings and close public transport, will effectively curb the pandemic, especially in densely populated continents. According to our Global Prediction System of COVID-19 Pandemic, the diffusion of delta lineage in the US shows seasonal oscillation characteristics, and the first wave will occur in October 2021, with the record of 323,360, and followed by a small resurgence in April 2022, with the record of 184,196, while the second wave will reach to 232,622 cases in October 2022. Our study will raise the awareness of new coronavirus variants among the public, and will help the governments make appropriate directives to cope with the new coronavirus variants.

Measuring the effect of fireworks on air quality in Minneapolis, Minnesota

Abstract

Air quality was measured before, during, and after a 4th of July fireworks display in downtown Minneapolis, Minnesota using a mix of low-cost sensors (CO, CO2, and NO) for gases and portable moderate cost instruments for particle measurements (PM2.5, lung deposited surface area, and number weighted particle size distributions). Meteorological conditions—temperature, humidity, and vertical temperature profile were also monitored. Concentrations of particles and most gaseous species peak between 10 pm and midnight on July 4th, decrease in the middle of the night but increase again and by between 6 and 7 am reach concentrations as high or higher than during fireworks. This overnight increase is likely due to a temperature inversion trapping emissions. Between 10 pm and midnight on July 4th the measures of particle concentration increase by 180–600% compared to the same period on July 3rd. Particle size distributions are strongly influenced by fireworks, shifting from traffic-like bimodal distributions before to a nearly unimodal distribution dominated by a large accumulation mode during and after. The shape of the size distribution measured during the early morning peak is nearly identical to that observed during fireworks, suggesting that the early morning peak is mainly due to trapped fireworks emissions not early morning traffic. Gaseous species are less strongly influenced by fireworks than particles. Comparing measurements made between 10 pm and midnight on July 4th and the same period on July 3rd, the concentration of CO increases 32% while the CO2 increases only 2% but increases by another 15% overnight. The NO concentration behaves oddly, decreasing during fireworks, but then recovering the next morning, more than doubling overnight. Our measurements of CO, NO, and PM2.5 are compared with those made at the nearest (~ 2 km away) Minnesota Pollution Control Agency Air Monitoring Station. Their NO results are quite different from ours with much lower concentrations before fireworks, a distinct peak during, followed by a strong overnight increase and an early morning peak somewhat similar in shape and concentration to ours. These differences are likely due mainly to malfunction of our low-cost NO sensor. Concentrations of CO and PM2.5 track ours within 25% but peak shapes are somewhat different, which is not unexpected given the spatial separation of the measurements.

Article highlights

COVID-19 and the irony of military expenditures: non-verbal semiotic discourse study

This article presents a study in the non-verbal semiotic discourse analysis of visual ironies of military expenditures in some selected cartoons amid COVID-19 spread. Visual irony can be expressed through using humorous or sardonic cartoons or posters with written expressions to express the opposite of what is really going on. Global military expenditure reaches $1917 billion in 2019. Such massive spending on military equipment failed to fight an unseen enemy. This article aims to analyse, from a non-verbal semiotic discourse perspective, some selected cartoons related to military spending amid the rise of COVID-19 pandemic. The selected cartoons are analysed according to Peirce's triadic system of the sign. The analysis of the ironic cartoons related to military spending indicates that governments should reduce spending on military. Instead, they should focus on spending on other crucial humane, economic, and health problems.

Influence of meteorological patterns on the 2020 COVID-19 pandemic in the Mexico City region

Meteorology is a critical factor affecting respiratory infectious diseases such as MERS, SARS, and influenza, but its effect on the spread of the COVID-19 disease remains controversial. Nevertheless, since the infected people cough-jets produce plumes of droplets and aerosols that can travel for several meters in the atmosphere, the possible influence of wind circulation and atmospheric turbulence on the infectious plume's fate cannot be ignored. This paper applied cluster analysis for identifying the near surface wind circulation patterns and associated temperature and humidity distributions in the Mexico City Metropolitan Area (MCMA), then their influence on the spread of the COVID-19 disease during the 2020 pandemic was discussed. Meteorology data and daily numbers of confirmed COVID-19 infections were obtained from public sources. An intense infection activity occurred from October to December 2020, and notable spreading of the disease toward the southwest and south MCMA was observed. In the same period, temperature and humidity conditions that could favor the virus stability and replication were detected in the same sectors, besides 60% of the wind observations revealed considerable northerly components. These findings suggested the existence of correlations between both phenomena. For assessing the possible relationship, the Pearson coefficients between the daily confirmed infections and the temperature and inward flux were estimated, and values from -0.32 to -0.55 and 0.62 to 0.70 were obtained. Correlation was negligible for relative humidity. Multilinear regression for the daily infections in response to the meteorological variables produced coefficients of determination from 0.3839 to 0.6138. Because of its implications for public health, this topic deserves a more in-depth investigation.

Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran

The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO₂, and O₃ tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO₂), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO₂. Suitable policy implications are recommended to be performed for improving people's healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.

Comparison Between China and Brazil in the Two Waves of COVID-19 Prevention and Control

Objective

This study analyzes the effectiveness of COVID-19 prevention and control in China and Brazil from the perspectives of policy and meteorological conditions, and provides experience for epidemic prevention and control.

Methods

This study collects data on meteorological conditions, vaccination and mutant strains in the two countries to analyze the reasons for the differences in epidemic status between the two countries and extracts public data on COVID-19 through various official websites, summarizes the prevention and control policies implemented by the two countries, and evaluates their effectiveness.

Results

As of August 12, 2021, the total number of COVID-19 cases and the daily number of new COVID-19 cases in China have been growing steadily, showing remarkable results in epidemic control. The total number of confirmed cases and the daily number of new confirmed cases in Brazil have continued to increase rapidly. The total death case in Brazil has reached 560,000, far exceeding that in China, and the effect of epidemic prevention and control is not satisfactory.

Conclusions

Multiple factors, such as meteorological conditions, policies and strategies, and economic conditions, can influence the spread of COVID-19, and therefore, the situation varies greatly from country to country. China and Brazil have chosen different interventions in the fight against COVID-19. The policy measures taken by China are typical containment measures and Brazil has a mitigation strategy. From the perspective of the current situation of the epidemic development in both countries, the cumulative death rate and daily new confirmed cases in Brazil are much higher than those in China, which indicates that the containment strategy is more effective than mitigation strategy in preventing and controlling COVID-19. Fighting the epidemic is a global long-lasting battle, and the two countries should learn from each other with the premise of respecting their national conditions. Countries should deepen cooperation and not let up prematurely.

Immunotherapy and CRISPR Cas Systems: Potential Cure of COVID-19?

The COVID-19 has plunged the world into a pandemic that affected millions. The continually emerging new variants of concern raise the question as to whether the existing vaccines will continue to provide sufficient protection for individuals from SARS-CoV-2 during natural infection. This narrative review aims to briefly outline various immunotherapeutic options and discuss the potential of clustered regularly interspaced short palindromic repeat (CRISPR Cas system technology against COVID-19 treatment as specific cure. As the development of vaccine, convalescent plasma, neutralizing antibodies are based on the understanding of human immune responses against SARS-CoV-2, boosting human body immune responses in case of SARS-CoV-2 infection, immunotherapeutics seem feasible as specific cure against COVID-19 if the present challenges are overcome. In cell based therapeutics, apart from the high costs, risks and side effects, there are technical problems such as the production of sufficient potent immune cells and antibodies under limited time to treat the COVID-19 patients in mild conditions prior to progression into a more severe case. The CRISPR Cas technology could be utilized to refine the specificity and safety of CAR-T cells, CAR-NK cells and neutralizing antibodies against SARS-CoV-2 during various stages of the COVID-19 disease progression in infected individuals. Moreover, CRISPR Cas technology are proposed in hypotheses to degrade the viral RNA in order to terminate the infection caused by SARS-CoV-2. Thus personalized cocktails of immunotherapeutics and CRISPR Cas systems against COVID-19 as a strategy might prevent further disease progression and circumvent immunity escape.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM₁₀, PM_2.5, and NO₂ contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM_2.5 concentration and fatality (R² = 0.416; p = 0.003), NO₂ concentration and fatality (R² = 0.232; p = 0.005), and NO₂ concentration and mortality (R² = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 μg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Transmission in home environment associated with the second wave of COVID-19 pandemic in India

India has suffered from the second wave of COVID-19 pandemic since March 2021. This wave of the outbreak has been more serious than the first wave pandemic in 2020, which suggests that some new transmission characteristics may exist. COVID-19 is transmitted through droplets, aerosols, and contact with infected surfaces. Air pollutants are also considered to be associated with COVID-19 transmission. However, the roles of indoor transmission in the COVID-19 pandemic and the effects of these factors in indoor environments are still poorly understood. Our study focused on reveal the role of indoor transmission in the second wave of COVID-19 pandemic in India. Our results indicated that human mobility in the home environment had the highest relative influence on COVID-19 daily growth rate in the country. The COVID-19 daily growth rate was significantly positively correlated with the residential percent rate in most state-level areas in India. A significant positive nonlinear relationship was found when the residential percent ratio ranged from 100 to 120%. Further, epidemic dynamics modelling indicated that a higher proportion of indoor transmission in the home environment was able to intensify the severity of the second wave of COVID-19 pandemic in India. Our findings suggested that more attention should be paid to the indoor transmission in home environment. The public health strategies to reduce indoor transmission such as ventilation and centralized isolation will be beneficial to the prevention and control of COVID-19.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate

The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.

Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis

Coronavirus disease 2019 (COVID-19) continues to be a pandemic threat that is generating a constant state of alert in manifold countries. One of the strategies of defense against infectious diseases (e.g., COVID-19) is the vaccinations that decrease the numbers of infected individuals and deaths. In this context, the optimal level of vaccination for COVID-19 is a basic point to control this pandemic crisis in society. The study here,-using data of doses of vaccines administered per 100 inhabitants, confirmed cases and case fatality ratio of COVID-19 between countries (N=192) from March to May 2021,- clarifies the optimal levels of vaccination for reducing the number of infected individuals and, consequently, the numbers of deaths at global level. Findings reveal that the average level of administering about 80 doses of vaccines per 100 inhabitants between countries can sustain a reduction of confirmed cases and number of deaths. In addition, results suggest that an intensive vaccination campaign in the initial phase of pandemic wave leads to a lower optimal level of doses administered per 100 inhabitants (roughly 47 doses of vaccines administered) for reducing infected individuals; however, the growth of pandemic wave (in May, 2021) moves up the optimal level of vaccines to about 90 doses for reducing the numbers of COVID-19 related infected individuals. All these results here could aid policymakers to prepare optimal strategies directed to a rapid COVID-19 vaccination rollout, before the takeoff of pandemic wave, to lessen negative effects of pandemic crisis on environment and socioeconomic systems.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM_2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM₁₀ had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Spatial analysis of COVID-19 hospitalised cases in an entire city: The risk of studying only lattice data

We live in a global pandemic caused by the COVID-19 disease where severe social distancing measures are necessary. Some of these measures have been taken into account by the administrative boundaries within cities (neighborhoods, postal districts, etc.). However, considering only administrative boundaries in decision making can prove imprecise, and could have consequences when it comes to taking effective measures. To solve the described problems, we present an epidemiological study that proposes using spatial point patterns to delimit spatial units of analysis based on the highest local incidence of hospitalisations instead of administrative limits during the first COVID-19 wave. For this purpose, the 579 addresses of the cases hospitalised between March 3 and April 6, 2020, in Albacete (Spain), and the addresses of the random sample of 383 controls from the Inhabitants Register of the city of Albacete, were georeferenced. The risk ratio in those hospitalised for COVID-19 was compatible with the constant risk ratio in Albacete (p = 0.49), but areas with a significantly higher risk were found and coincided with those with greater economic inequality (Gini Index). Moreover, two districts had areas with a significantly high incidence that were masked by others with a significantly low incidence. In conclusion, taking measures conditioned exclusively by administrative limits in a pandemic can cause problems caused by managing entire districts with lax measures despite having interior areas with high significant incidences. In a pandemic context, georeferencing disease cases in real time and spatially comparing them to updated random population controls to automatically and accurately detect areas with significant incidences are suggested. This would facilitate decision making, which must be fast and accurate in these situations.

Association between human coronaviruses' epidemic and environmental factors on a global scale

Environmental factors could influence the epidemic of virus in human; however, the association remains intricate, and the evidence is still not clear in human coronaviruses (HCoVs). We aimed to explore and compare the associations between HCoVs' epidemic and environmental factors globally. Four common HCoVs' data were collected by a systematic literature review, and data of MERS, SARS, and COVID-19 were collected from the World Health Organization's reports. Monthly positive rates of common HCoVs and incidence rates of MERS, SARS, and COVID-19 were calculated. Geographical coordinates were used to link virus data and environmental data. Generalized additive models (GAMs) were used to quantitatively estimate the association of environmental factors with HCoVs' epidemic. We found that there are wide associations between HCoVs and environmental factors on a global scale, and some of the associations were nonlinear. In addition, COVID-19 has the most similarities in associations' direction with common HCoVs, especially for HCoV-HKU1 in four environmental factors including the significantly negative associations with average temperature, precipitation, vegetation coverage (p<0.05), and the U-shaped association with temperature range. This study strengthened the relevant research evidences and provided significant insights into the epidemic rules of HCoVs in general. The similarities between COVID-19 and common HCoVs indicated that it is critically important to strengthen surveillance on common HCoVs and pay more attention to environmental factors' role in surveillance and early warning of HCoVs' epidemic.

Influence of environmental factors on the spread of COVID-19 in Saudi Arabia

BACKGROUND

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide. The infection is mostly spread through the inhalation of infected droplets. Saudi Arabia is a vast country having different climatic conditions.

METHODS

The study evaluated the influence of environmental factors on the spread of COVID-19. Six zones (A to F) were classified depending on the climatic conditions. The study was conducted by retrospective analysis of COVID-19 records from the ministry of health between the months of September 2020 and August 2021. The environmental data such as average temperature (°C), humidity (%), wind speed (m/s) and sun exposure (kwh/m²) were retrieved from official sites. The data was analyzed to determine the effect of these factors on the spread of COVID-19. SPSS IBM 25 software was used to conduct the analysis and p < 0.05 was considered to indicate the significance of the results.

RESULTS

According to the findings, the rate of infection was greater between April and July 2021. Six climatic zones experienced high temperatures, little humidity, consistent wind flow, and intense sun exposure throughout this time. The correlation study revealed a significant (p < 0.05) relationship between the environmental factors and the spread of COVID-19. The data suggested that during summer condition when the weather is hot, less humid, and steady wind flow with lots of sun exposure, the COVID-19 infection rate got augmented in Saudi Arabia. Poor ventilation and closed-door habitats in an air-conditioned atmosphere during this period could have played a role in human transmission. More research on air quality, population mobility and diseased condition is essential, so that precise proactive measures can be designed to limit the spread of infection in specific climatic seasons.

Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats

The Coronavirus Disease 2019 (COVID-19) continues to generate a constant pandemic threat with new mutations of the viral agent (SARS-CoV-2) that create socioeconomic issues. One of the fundamental problems is the evaluation of the preparedness of countries to cope with COVID-19 pandemic crisis to detect and support factors associated with the reduction of mortality and the growth of vaccinations in society. The study here confronts this problem by developing two basic indexes, which measure the performance of countries to face pandemic threats. In particular, the Index r (as resilience) detects the countries having the best performance in the reduction of the negative impact of mortality related to COVID-19 pandemic and the Index p (as preparedness and prevention) assesses best-performer countries to support COVID-19 vaccinations in order to constrain future pandemic threats and support the recovery of socioeconomic systems. Index of resilience is a composite measure based on three indicators associated with COVID-19, given by average mortality, hospital occupancy and Intensive Care Units occupancy per 100 000 people, producing an overall score; Index of preparedness/prevention is a composite measure of two indicators related to COVID-19 vaccinations (i.e., doses of vaccines administered and total vaccinates per 100 000 people), producing also an overall score of performance. The application of these indexes on a case study of European countries, having a homogenous socioeconomic area, shows the strategic positioning of countries to cope with a major pandemic threat. Findings reveal that all countries have some weaknesses and no country has a high preparedness to cope with a major epidemic or pandemic. Moreover, results suggest that best-performer countries to cope with COVID-19 pandemic crisis have a smaller size of population and/or better public governance, associated with high expenditures in health system. These indexes can help policymakers for designing effective strategies to improve preparedness and prevention of countries to face future pandemic threats.

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.