Impact of temperature on the dynamics of the COVID-19 outbreak in China

Inactivation of β-coronavirus MHV-A59 by 2.8 GHz microwave

From the severe acute respiratory syndrome coronavirus in 2003 to the severe acute respiratory syndrome coronavirus 2 in 2019, coronavirus has seriously threatened human health. Electromagnetic waves not only own high penetration and low pollution but also can physically resonate with the virus. Several studies have demonstrated that electromagnetic waves can inactivate viruses efficiently. However, there is still a lack of systemic studies to analyze the potential factors closely associated with the effectiveness of inactivation, such as pH, temperature, and so on. In this study, we evaluated the inactivation ability of a 2.8 GHz microwave (MW) on MHV-A59, a substitute virus for coronavirus. Moreover, the influences of environmental pH and temperature on inactivation abilities were also discussed. The results showed that the viral morphology was destroyed, and the infectivity of MHV-A59 was significantly decreased after exposure to a 2.8 GHz MW at a density of 100 mW/cm2. Furthermore, alteration of pH 8 could produce synergistic effects with MW on virus inactivation. And, it was also proved that MWs could inactivate viruses better at room temperature than that under lower environmental temperatures. These results suggested that electromagnetic wave has great promise to become an effective tool to eliminate coronavirus.

Cold snaps lead to a 5-fold increase or a 3-fold decrease in disease proliferation depending on the baseline temperature

BACKGROUND

Climate change is driving increased extreme weather events that can impact ecology by moderating host-pathogen interactions. To date, few studies have explored how cold snaps affect disease prevalence and proliferation. Using the Daphnia magna-Ordospora colligata host-parasite system, a popular model system for environmentally transmitted diseases, the amplitude and duration of cold snaps were manipulated at four baseline temperatures, 10 days post-exposure, with O. colligata fitness recorded at the individual level.

RESULTS

Cold snaps induced a fivefold increase or a threefold decrease in parasite burden relative to baseline temperature, with complex nuances and varied outcomes resulting from different treatment combinations. Both amplitude and duration can interact with the baseline temperature highlighting the complexity and baseline dependence of cold snaps. Furthermore, parasite fitness, i.e., infection prevalence and burden, were simultaneously altered in opposite directions in the same cold snap treatment.

CONCLUSIONS

We found that cold snaps can yield complicated outcomes that are unique from other types of temperature variation (for example, heatwaves). These results underpin the challenges and complexity in understanding and predicting how climate and extreme weather may alter disease under global change.

Identification of environmental, socioeconomic, water, sanitation, and hygiene (WaSH) factors associated with COVID-19 incidence in the Philippines: A nationwide modelling study

Despite the implementation of non-pharmaceutical interventions, the threat of coronavirus disease 2019 (COVID-19) remains significant on a global scale. Identifying external factors contributing to its spread is crucial, especially given the World Health Organization's recommendation emphasizing access to water, sanitation, and hygiene as essential in curbing COVID-19. There is a notable discrepancy in access to sanitation facilities, particularly evident in low- and middle-income countries. However, there is a lack of quantitative assessments regarding these factors. This study examines various environmental, socioeconomic, water, sanitation, and hygiene factors and their associations with COVID-19 incidence. All regions in the Philippines were categorized into clusters based on socioeconomic factors. A conceptual structural equation model (SEM) was developed using domain knowledge. The best-fitting SEM for each cluster was determined, and associations between factors and COVID-19 incidence were estimated. The correlation analysis revealed that rainfall, minimum temperature, and relative humidity were positively correlated with weekly COVID-19 incidence in urban regions. Maximum temperature, mean temperature, wind speed, and wind direction were negatively correlated with weekly COVID-19 incidence in rural regions, with time lags of 0, 3, and 7 weeks. In urban regions (Cluster 1), factors such as urbanization rate (1.00), area (-0.93), and population (0.54) were found to be associated with weekly COVID-19 incidence. Conversely, in rural regions (Cluster 2), factors including area (0.17), basic sanitation (0.84), and wind direction (0.83) showed associations with weekly COVID-19 incidence. These factors were causally associated with a latent variable reflecting the hidden confounders associated with COVID-19 incidence. It is important to note that sanitation factors were associated only in rural regions. Improving access to sanitation facilities in rural regions of the Philippines is imperative to effectively mitigate disease transmission in future pandemics. Identification of the causal effect of unobserved confounders with COVID-19 incidence is recommended for future research.

Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador

The relationship between COVID-19 infections and environmental contaminants provides insight into how environmental factors can influence the spread of infectious diseases. By integrating epidemiological and environmental variables into a mathematical framework, the interaction between virus spread and the environment can be determined. The aim of this study was to evaluate the impact of atmospheric contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. The data on infections and deaths allowed to identify the periods of greatest contagion and their relationship with the contaminants O3, SO2, CO, PM2.5, and PM10. A validated database was used, and statistical analysis was applied through five models based on simple linear regression. The models showed a significant relationship between SO2 and the increase in infections. In addition, a moderate correlation was shown with PM2.5, O3, and CO, and a low relationship was shown for PM10. These findings highlight the importance of having policies that guarantee air quality as a key factor in maintaining people's health and preventing the proliferation of viral and infectious diseases.

Effect of Long-Distance Domestic Travel Ban Policies in Japan on COVID-19 Outbreak Dynamics During Dominance of the Ancestral Strain: Ex Post Facto Retrospective Observation Study

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

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.

Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19

BACKGROUND

The association between weather conditions and the spread of COVID-19 was demonstrated by previous studies but focused on specific countries or investigated shorter periods of duration limiting the interpretation of the results.

AIM

To make an international comprehensive insight into the association between the weather conditions and the spread of COVID-19 by spanning many regions in the Northern and Southern hemispheres over a period of two years for the COVID-19 Outbreak.

METHODS

The data were analyzed by using statistical description, linear and multiple regressions, and the Spearman rank correlation test. Daily and weekly COVID-19 cases, the average temperatures, Wind Speed, the amount of precipitation as well as the relative humidity rates were collected from Irbid, Jordan as the main location of analyses, as well as comparison cities and countries in both hemispheres.

RESULTS

we found that certain climate variables are significant factors in determining the transmission rate of COVID-19 worldwide. Where, The temperature in the northern hemisphere regions was the most important climate factor that affects the increase in the transmission rate of COVID-19 (Northern Hemisphere rs = -0.65; Irbid rs = -0.74995; P < 0.001), While in southern hemisphere, the climate factor that affects the increase in the transmission rate of COVID-19 was the humidity (rs = 0.55; P < 0.01), In addition, we found the negligible and oscillated effect of wind speed on the transmission rate of COVID-19 worldwide. Moreover, we found that in Irbid 82% of COVID-19 cases were in the fall and winter seasons, while in summer the percentage of COVID-19 cases didn't exceed 3% during the total study period.

CONCLUSION

This study can help develop international strategies and policies against COVID-19-related pandemic peaks, especially during the colder seasons in the Northern Hemisphere regions from the first month of fall to the last month of winter.

Analytical tool for COVID-19 using an SIR model equivalent to the chain reaction equation of infection

Insights from data analysis of existing cases are important to prevent future outbreaks of coronavirus disease 2019 (COVID-19). Although mathematical models are expected to be useful for this purpose, the adequacy of reproducibility of these models is difficult to confirm because they are based on hypotheses. For example, using the time variation of the parameter of the basic reproduction number for the time variation of complex data on the number of infected persons is a change of expression and does not capture the substance of the problem. We previously showed that the simplest Susceptible, Infected, Recovered (SIR) model alone, without any complex models, exhibits acceptable reproducibility. By clarifying the rationale for this reproducibility, quantifiable characteristics regarding the infection spread, such as the duration of the pandemic and the mechanism of occurrence of several large waves, can be uncovered and this can contribute to countermeasures. Here, we show this method equals the chain reaction equation for infection, allowing the parameters (infection rate, population) of the mathematical models to be extracted from the data. Once a model that reproduces the actual situation is determined, much of the information becomes apparent. As an example, we present three characteristics of the spread of infection effective in controlling COVID-19: the time of onset of infection, the rapidity of the spread, and the time to acquisition of herd immunity. Acquiring this information is likely to increase the predictive accuracy of the model.

Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

Research problem

Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world.

Study design

The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model.

Findings

The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC.

Conclusion

The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

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.

Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones

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.

A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial Rt through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible-exposure-infection-quarantine-recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

A noncontact modular infectious disease screening clinic aiming to achieve zero cross-contaminations

Screening clinics play a major role in preventing the transmission of infectious diseases. The main problem that should be addressed is the exposure to cross-infection between healthcare workers and individuals intended to be tested. In this study, a noncontact modular screening clinic (NCMSC) was developed that addresses the problems of existing screening clinics and the risks of cross-contamination during the infectious disease sampling process. The space and ventilation system of the NCMSC were designed to effectively remove viral aerosols to avoid cross-contamination. The spatial configurations that enabled noncontact specimen sampling and pressure differential control was achieved. Regarding the measurement method with the use of tracer gas, an experimental field test framework and procedure that can evaluate the cross-contamination between rooms were presented. It is the observation of pollutants (tracer gas) in two different modes (normal breathing and AGP from a patient) in a screening clinic with ventilation, compared to the room next door, where the HCW is located. Additionally, based on onsite experiments using SF₆ tracer gas that mimics the viral aerosol at an actual scale, it was verified that no cross-contamination occurred in the NCMSC; accordingly, it was possible to protect sufficiently the healthcare workers. It will be possible to use the outcomes of this study as basic data for the development of standards for the installation and operation of screening clinics for infectious diseases.

Association of government effectiveness, logistics performance, IT systems and income with COVID-19 mortality

The COVID-19 pandemic has unprecedentedly shaken the public health system worldwide. It has been one of the greatest humanitarian crises faced by all countries, regardless of their economic prosperity. However, some countries have been able to minimize the deaths caused by the coronavirus even in the face of a large number of cases, while others have failed to control the death rate even in a comparatively small number of cases. This study explores possible causes of this disparity using cross-sectional data from 126 countries associated with demography, governance, income level, the extent of ICT maturity and the geographical divide. The results of this study suggest that while government effectiveness is negatively associated with the COVID-19 death rate, the logistics performance of governments is positively linked to the COVID-19 mortality rate. The ICT maturity proxied through online service delivery did not confirm its association with the COVID-19 mortality rate. This study informs that poverty and the location of countries do not necessarily influence COVID-19 deaths. Hence, it behoves governments to focus on improving government effectiveness and putting in place more effective and efficient mobility systems, healthcare supply chains and digital administration to address the global health crisis posed by the COVID-19 pandemic and mitigate its harsh effects, including mortality.

The Effect of Meteorological Factors on the COVID-19 Pandemic in Northeast Turkiye

Introduction Although various studies have been conducted on the relationship between meteorological factors and coronavirus disease 2019 (COVID-19), this issue has not been sufficiently clarified. In particular, there are a limited number of studies on the course of COVID-19 in the warmer-humidity seasons. Methods Patients presenting to the emergency departments of health institutions and to clinics set aside for cases of suspected COVID-19 in the province of Rize between 1 June and 31 August 2021 and who met the case definition based on the Turkish COVID-19 epidemiological guideline were included in this retrospective study. The effect of meteorological factors on case numbers throughout the study was investigated. Results During the study period, 80,490 tests were performed on patients presenting to emergency departments and clinics dedicated to patients with suspected COVID-19. The total case number was 16,270, with a median daily number of 64 (range 43-328). The total number of deaths was 103, with a median daily figure of 1.00 (range 0.00-1.25). According to the Poisson distribution analysis, it is found that the number of cases tended to increase at temperatures between 20.8 and 27.2°C. Conclusion It is predicted that the number of COVID-19 cases will not decrease with the increase in temperature in temperate regions with high rainfall. Therefore, unlike influenza, there may not be seasonal variation in the prevalence of COVID-19. The requisite measures should be adopted in health systems and hospitals to manage increases in case numbers associated with changes in meteorological factors.

Modeling the Climatic Suitability of COVID-19 Cases in Brazil

Studies have shown that climate may affect the distribution of coronavirus disease (COVID-19) and its incidence and fatality rates. Here, we applied an ensemble niche modeling approach to project the climatic suitability of COVID-19 cases in Brazil. We estimated the cumulative incidence, mortality rate, and fatality rate of COVID-19 between 2020 and 2021. Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected to model the climate suitability for COVID-19 cases from diverse climate data, including temperature, precipitation, and humidity. The annual temperature range and precipitation seasonality showed a relatively high contribution to the models, partially explaining the distribution of COVID-19 cases in Brazil based on the climatic suitability of the territory. We observed a high probability of climatic suitability for high incidence in the North and South regions and a high probability of mortality and fatality rates in the Midwest and Southeast regions. Despite the social, viral, and human aspects regulating COVID-19 cases and death distribution, we suggest that climate may play an important role as a co-factor in the spread of cases. In Brazil, there are regions with a high probability that climatic suitability will contribute to the high incidence and fatality rates of COVID-19 in 2020 and 2021.

Improved air quality leads to enhanced vegetation growth during the COVID-19 lockdown in India

The direct effect of pandemic induced lockdown (LD) on environment is widely explored, but its secondary impacts remain largely unexplored. Therefore, we assess the response of surface greenness and photosynthetic activity to the LD-induced improvement of air quality in India. Our analysis reveals a significant improvement in air quality marked by reduced levels of aerosols (AOD, -19.27%) and Particulate Matter (PM 2.5, -23%) during LD (2020)from pre-LD (March-September months for the period 2017-2019). The vegetation exhibits a positive response, reflected by the increase in surface greenness [Enhanced Vegetation Index (EVI, +10.4%)] and photosynthetic activity [Solar Induced Fluorescence (SiF, +11%)], during LD from pre-LD that coincides with two major agricultural seasons of India; Zaid (March-May) and Kharif (June-September). In addition, the croplands show a higher response [two-fold in EVI (14.45%) and four-fold in SiF (17.7%)] than that of forests. The prolonged growing period (phenology) and high rate of photosynthesis (intensification) led to the enhanced greening during LD owing to the reduced atmospheric pollution. This study, therefore, provides new insights into the response of vegetation to the improved air quality, which would give ideas to counter the challenges of food security in the context of climate pollution, and combat global warming by more greening.

The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a 'synthetic Hong Kong' from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10-day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks.

Inequality, public health, and COVID-19: an analysis of the Spanish case by municipalities

The main objective of this work is to analyze whether inequality in income distribution has an effect on COVID-19 incidence and mortality rates during the first wave of the pandemic, and how the public health system mitigates these effects. To this end, the case of 819 Spanish municipalities is used, and a linear cross-sectional model is estimated. The results obtained allow us to conclude that a higher level of income inequality generates a higher rate of infections but not deaths, highlighting the importance of the Spanish National Health Service, which does not distinguish by income level. Likewise, early detection of infection measured by the number of primary care centers per 100,000 inhabitants, access to health care for the treatment of the most severe cases, unemployment as a proxy for job insecurity, climatic conditions, and population density are also important factors that determine how COVID-19 affects the population.

The dynamics of early-stage transmission of COVID-19: A novel quantification of the role of global temperature

The global outbreak of COVID-19 has emerged as one of the most devastating and challenging threats to humanity. As many frontline workers are fighting against this disease, researchers are struggling to obtain a better understanding of the pathways and challenges of this pandemic. This paper evaluates the concept that the transmission of COVID-19 is intrinsically linked to temperature. Some complex nonlinear functional forms, such as the cubic function, are introduced to the empirical models to understand the interaction between temperature and the "growth" in the number of infected cases. An accurate quantitative interaction between temperature and the confirmed COVID-19 cases is obtained as log(Y) = -0.000146(temp_H)3 + 0.007410(temp_H)2 -0.063332 temp_H + 7.793842, where Y is the periodic growth in confirmed COVID-19 cases, and temp_H is the maximum daily temperature. This equation alone may be the first confirmed way to measure the quantitative interaction between temperature and human transmission of COVID-19. In addition, four important regions are identified in terms of maximum daily temperature (in Celsius) to understand the dynamics in the transmission of COVID-19 related to temperature. First, the transmission decreases within the range of -50 °C to 5.02 °C. Second, the transmission accelerates in the range of 5.02 °C to 16.92 °C. Essentially, this is the temperature range for an outbreak. Third, the transmission increases more slowly in the range of 16.92 °C to 28.82 °C. Within this range, the number of infections continues to grow, but at a slower pace. Finally, the transmission decreases in the range of 28.82 °C to 50 °C. Thus, according to this hypothesis, the threshold of 16.92 °C is the most critical, as the point at which the infection rate is the greatest. This result sheds light on the mechanism in the cyclicity of the ongoing COVID-19 pandemic worldwide. The implications of these results on policy issues are also discussed concerning a possible cyclical fluctuation pattern between the Northern and Southern Hemispheres.

High-Speed railways and the spread of Covid-19

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper's findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

Weather indicators and improving air quality in association with COVID-19 pandemic in India

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM₁₀, PM_2.5, CO, NO₂, SO₂, NH₃, and O₃) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

Development of a non-contact mobile screening center for infectious diseases: Effects of ventilation improvement on aerosol transmission prevention

Under the global landscape of the prolonged COVID-19 pandemic, the number of individuals who need to be tested for COVID-19 through screening centers is increasing. However, the risk of viral infection during the screening process remains significant. To limit cross-infection in screening centers, a non-contact mobile screening center (NCMSC) that uses negative pressure booths to improve ventilation and enable safe, fast, and convenient COVID-19 testing is developed. This study investigates aerosol transmission and ventilation control for eliminating cross-infection and for rapid virus removal from the indoor space using numerical analysis and experimental measurements. Computational fluid dynamics (CFD) simulations were used to evaluate the ventilation rate, pressure differential between spaces, and virus particle removal efficiency in NCMSC. We also characterized the airflow dynamics of NCMSC that is currently being piloted using particle image velocimetry (PIV). Moreover, design optimization was performed based on the air change rates and the ratio of supply air (SA) to exhaust air (EA). Three ventilation strategies for preventing viral transmission were tested. Based on the results of this study, standards for the installation and operation of a screening center for infectious diseases are proposed.

Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.

Association Between Air Pollution, Climate Change, and COVID-19 Pandemic: A Review of the Recent Scientific Evidence

Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.

Effect of association of temperature and pollutant levels on COVID-19 spread over Jaipur

The association of temperature and air pollutants is a very prominent factor which significantly affects human health and may cause diseases such as respiratory illness, cardiovascular mortality in spreading of different pathogenic diseases. The pandemic due to covid-19 infection may be affected by temperature and concentration of pollutants. Jaipur is one of the most polluted cities in Rajasthan of India as per World Health Organization, 2016; also, Jaipur city has a hot semi-arid climate with extremely hot summers. This fact tempered us to examine the impact of the association of temperature and pollutants on corona-virus infection in humans over Jaipur. Analysis was conducted by correlating air pollutants (PM10, PM2.5, NO2, SO2, CO) on seasonal variations because the temperature is one of the major factors in changing seasons. Association between the number of Covid cases and temperature in Jaipur was observed during December 2019 to December 2020. Seasonal analysis indicated that the intensity of Covid-19 infection varied according to increase or decrease in temperature.

Impact of COVID-19 pandemic on consumer behavioural intention to purchase green products

This study examined the fear of COVID-19 pandemic and its impact on consumer behavioural intention to purchase green products. The data was collected from consumers of Malaysia in hypermarkets. A total of 491 respondents were analyzed using the partial least square technique. The results indicated that the fear of the COVID-19 epidemic has a significant impact on health concerns, social media information, intolerance of uncertainty, and personal relevance, which in turn affect consumers' behavioural intention to purchase green products. With a serial mediating effect the results identified that fear of COVID-19 epidemic is associated with behavioural intention to purchase the green product. The findings of this study are crucial for understanding the swings in the green product purchase behaviour due to the ongoing uncertainty of COVID-19 crisis.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021

We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m³. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.

The health benefit of physical exercise on COVID-19 pandemic: Evidence from mainland China

Objectives

Our study aims to investigate the health benefit of regular physical exercise participation on a series of COVID-19 outcomes including COVID-19 morbidity, mortality, and cure rate.

Methods

Prefecture-level panel data related to physical exercise and the COVID-19 pandemic in China were collected from January 1 to March 17, 2020, (N = 21379). Multiple linear regression was conducted, and the ordinary least squares technique was used to estimate the coefficient.

Results

It was shown that regular sports participation significantly negatively affected COVID-19 morbidity (estimate = -1.1061, p<0.01) and mortality (estimate = -0.3836, p<0.01), and positively affected cure rate (estimate = 0.0448, p<0.01), implying that engaging in physical exercise regularly does have a significant positive effect on COVID-19 outcomes. Then, we explored the heterogeneity of the effect of physical exercise on areas with different risk levels and it was revealed that the effect of physical exercise was more pronounced in high-risk areas in terms of morbidity (estimate = -1.8776, p<0.01 in high-risk areas; estimate = -0.0037, p<0.01 in low-risk areas), mortality (estimate = -0.3982, p<0.01 in high-risk areas; estimate = -0.3492, p<0.01 in low-risk areas), and cure rate (estimate = 0.0807, p<0.01 in high-risk areas; 0.0193 = -0.0037, p<0.05 in low-risk areas).

Conclusions

Our results suggest that regularly engaging in physical exercise before the pandemic has positive health effects, especially in the case of a more severe epidemic. Therefore, we urge readers to actively engage in physical exercise so that we can reduce the risks in the event of a pandemic.

COVID-19 transmission in Africa: estimating the role of meteorological factors

Climate variables play a critical role in COVID-19’s spread. Therefore, this research aims to analyze the effect of average temperature and relative humidity on the propagation of COVID-19 in Africa's first four affected countries (South Africa, Morocco, Tunisia, and Ethiopia). As a result, policymakers should develop effective COVID-19 spread control strategies. For each country, using daily data of confirmed cases and weather variables from May 1, 2020, to April 30, 2021, generalized linear models (Poisson regression) and general linear models were estimated. According to the findings, the rising average temperature causes COVID-19 daily new cases to increase in South Africa and Ethiopia while decreasing in Morocco and Tunisia. However, in Tunisia, the relative humidity and daily new cases of COVID-19 are positively correlated, while in the other three countries, they are negatively associated.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.