An initial investigation of the association between the SARS outbreak and weather: with the view of the environmental temperature and its variation

Short-term effect of meteorological factors on COVID-19 mortality in Qom, Iran

The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=-33.64% (95% CI (-42.44, -23.49)), and relative humidity (percentage change in lag 0=-1.87% (95% CI (-2.52, -1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.

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.

Role of meteorological parameters with the spread of Covid-19 in Pakistan: application of autoregressive distributed lag approach

This research focuses on the impacts of different meteorological parameters (temperature, humidity, rainfall, and evapotranspiration) on the transmission of Covid-19 in the administrative regions and provinces of Pakistan, i.e., Azad Jammu and Kashmir, Gilgit Baltistan, Khyber Pakhtunkhwa, Islamabad, Punjab, Sindh, and Balochistan from June 10, 2020, to August 31, 2021. This study analyzes the relation between Covid-19-confirmed cases and the meteorological parameters with the help of the autoregressive distributed lag model. In this research, additional tools (t-statistics, f-statistics, and time series analysis) are used for the motive of examining the linear relationship, the productivity of the model, and for the significant association between dependent and independent variables, lnccc and lnevp, lnhum, lnrain, lntemp, respectively. Values of t-statistics and f-statistics reveal that variables have a connection and individual significance for the model exist. Time series display that the Covid-19 spread increased from June 10, 2020, to August 31, 2021, in Pakistan. Temperature positively influenced the Covid-19-confirmed cases in all provinces of Pakistan in the long run. Evapotranspiration and rainfall influenced positively, while specific humidity influenced negatively on the confirmed Covid-19 cases in Azad Jammu Kashmir, Khyber Pakhtunkhwa, and Punjab. Specific humidity had a positive impact, while evapotranspiration and rainfall had the negative impact on the Covid-19-confirmed cases in Sindh and Balochistan. Evapotranspiration and specific humidity influenced positively, while rainfall influenced the Covid-19-confirmed cases negatively in Gilgit Baltistan. Evapotranspiration influenced positively, while specific humidity and rainfall influenced negatively on the Covid-19-confirmed cases in Islamabad.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-023-04997-4.

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.

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

The impact of meteorological factors on the spread of COVID-19

Background

Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity.

Aim

This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19).

Patients and Methods

This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1^st Regional Directorate.

Results

The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality.

Conclusion

Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

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.

The long-term impact of coronavirus disease 2019 on environmental health: a review study of the bi-directional effect

Background

When health systems worldwide grapple with the coronavirus disease 2019 (COVID-19) pandemic, its effect on the global environment is also a significant consideration factor. It is a two-way process where the pre-COVID climate factors influenced the landscape in which the disease proliferates globally and the consequences of the pandemic on our surroundings. The environmental health disparities will also have a long-lasting effect on public health response.

Main body

The ongoing research on the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and COVID-19 must also include the role of environmental factors in the process of infection and the differential severity of the disease. Studies have shown that the virus has created positive and negative ramifications on the world environment, especially in countries most critically affected by the pandemic. Contingency measures to slow down the virus, such as self-distancing and lockdowns have shown improvements in air, water, and noise quality with a concomitant decrease in greenhouse gas emissions. On the other hand, biohazard waste management is a cause for concern that can result in negative effects on planetary health. At the peak of the infection, most attention has been diverted to the medical aspects of the pandemic. Gradually, policymakers must shift their focus to social and economic avenues, environmental development, and sustainability.

Conclusion

The COVID-19 pandemic has profoundly impacted the environment, both directly and indirectly. On the one hand, the sudden halt in economic and industrial activities led to a decrease in air and water pollution, as well as a reduction in greenhouse gas emissions. On the other hand, the increased use of single-use plastics and a surge in e-commerce activities have had negative effects on the environment. As we move forward, we must consider the pandemic's long-term impacts on the environment and work toward a more sustainable future that balances economic growth and environmental protection. The study shall update the readers on the various facets of the interaction between this pandemic and environmental health with model development for long-term sustainability.

Graphic Abstract

Association of Meteorological Factors With COVID-19 During Harmattan in Nigeria

Harmattan is a season of dry, cold, dusty wind, and haze that is peculiar to West Africa. This season and COVID-19 share common conditions such as malaise and respiratory issues like as runny nose, cough and sneezing, and raise a question of a possible relationship that begs to be answered. This study investigated whether the meteorological factors of humidity and wind speed during harmattan have association with COVID-19 incidence and mortality in the 2 major COVID-19 epicenters of Lagos state and the Federal Capital Territory (FCT) in southern and northern geopolitical regions of Nigeria respectively. Data used were from March, 2020 to February, 2022, which corresponded to the period of 2 years after the first case of COVID-19 was detected in Nigeria. Correlation analysis was performed using incidence or mortality data on COVID-19 over the duration of 2 years and during the harmattan periods, as well as the humidity and wind speed data for the corresponding periods. Our results showed that there was no significant correlation between the humidity or wind speed and COVID-19 daily incidence or mortality during the harmattan and non-harmattan periods in Lagos state. In the FCT however, there was a significant positive correlation between humidity and COVID-19 incidence, as well as a negative correlation between wind speed and COVID-19 incidence. No significant correlation existed between humidity or wind speed and daily mortality. Taken together, the findings of this study show that weather components of the harmattan season have association with COVID-19 incidence but not mortality, and the association could vary depending on location.

Impact of meteorological factors and population density on COVID-19 pandemic in Saudi Arabia

Transmission and increase in cases and fatalities of coronavirus disease-2019 (COVID-19) are significantly influenced by the parameters of weather, human activities and population factors. However, study gap on the seasonality of COVID-19 and impact of environmental factors on the pandemic in Saudi Arabia is present. The main aim of the study is to evaluate the impact of environment on the COVID-19 pandemic. Data were analyzed from January 2020 to July 2021. The generalized estimating equation (GEE) was used to determine the effect of environmental variables on longitudinal outcomes. Spearman's rank correlation coefficient (r_s ) was used to analyze the impact of different parameters on the outcome of the pandemic. Multiple sequence alignment was performed by using ClustalW. Vaccination and fatalities (r _s = -0.85) had the highest association followed by vaccination with cases (r _s = -0.81) and population density with the fatalities (r_s  = 0.71). The growth rate had the highest correlation with sun hours (r _s = -0.63). Isolates from variant of concern alpha and beta were detected. Most of the reference sequences in Saudi Arabia were closely related with B.1.427/429 variant. Clade GH (54%) was the most prevalent followed by O (27%), GR (9%), G (6%), and S (4%), respectively. Male to female patient ratio was 1.4:1. About 95% fatality and hospitalization were reported in patients aged >60 years. This study will create a comprehensive insight of the interaction of environmental factors and the pandemic and add knowledge on seasonality of COVID-19 in Saudi Arabia.

The outbreak of COVID-19 in Taiwan in late spring 2021: combinations of specific weather conditions and related factors

This study aimed to investigate the impact of weather conditions on the daily incidence of the COVID-19 pandemic in late spring 2021 in Taiwan, which is unlike the weather conditions of the COVID-19 outbreak in 2020. Meteorological parameters such as maximum daily temperature, relative humidity, and wind speed were included. The Spearman rank correlation test was used to evaluate the relationship between weather and daily domestic COVID-19 cases. The maximum daily temperature had a positively significant correlation with daily new COVID-19 cases within a 14-day lag period, while the relative humidity and wind speed has a fairly high correlation with the number of daily cases within a 13- and 14-day lag, respectively. In addition, the weather characteristics during this period were an increasingly high temperature, with steady high relative humidity and slightly decreasing wind speed. Our study revealed the weather conditions at the time of the domestic outbreak of COVID-19 in Taiwan in May 2021 and the possible association between weather factors and the COVID-19 pandemic. Further large-scale analysis of weather factors is essential for understanding the impact of weather on the spread of infectious diseases.

The SARS-CoV-2 differential genomic adaptation in response to varying UVindex reveals potential genomic resources for better COVID-19 diagnosis and prevention

Coronavirus disease 2019 (COVID-19) has been a pandemic disease reported in almost every country and causes life-threatening, severe respiratory symptoms. Recent studies showed that various environmental selection pressures challenge the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infectivity and, in response, the virus engenders new mutations, leading to the emergence of more virulent strains of WHO concern. Advance prediction of the forthcoming virulent SARS-CoV-2 strains in response to the principal environmental selection pressures like temperature and solar UV radiation is indispensable to overcome COVID-19. To discover the UV-solar radiation-driven genomic adaption of SARS-CoV-2, a curated dataset of 2,500 full-grade genomes from five different UVindex regions (25 countries) was subjected to in-depth downstream genome-wide analysis. The recurrent variants that best respond to UV-solar radiations were extracted and extensively annotated to determine their possible effects and impacts on gene functions. This study revealed 515 recurrent single nucleotide variants (rcntSNVs) as SARS-CoV-2 genomic responses to UV-solar radiation, of which 380 were found to be distinct. For all discovered rcntSNVs, 596 functional effects (rcntEffs) were detected, containing 290 missense, 194 synonymous, 81 regulatory, and 31 in the intergenic region. The highest counts of missense rcntSNVs in spike (27) and nucleocapsid (26) genes explain the SARS-CoV-2 genomic adjustment to escape immunity and prevent UV-induced DNA damage, respectively. Among all, the most commonly observed rcntEffs were four missenses (RdRp-Pro327Leu, N-Arg203Lys, N-Gly204Arg, and Spike-Asp614Gly) and one synonymous (ORF1ab-Phe924Phe) functional effects. The highest number of rcntSNVs found distinct and were uniquely attributed to the specific UVindex regions, proposing solar-UV radiation as one of the driving forces for SARS-CoV-2 differential genomic adaptation. The phylogenetic relationship indicated the high UVindex region populating SARS-CoV-2 as the recent progenitor of all included samples. Altogether, these results provide baseline genomic data that may need to be included for preparing UVindex region-specific future diagnostic and vaccine formulations.

Assessment of Antimicrobial Potential of Plagiochasma rupestre Coupled with Healing Clay Bentonite and AGNPS

The impact of individual component, i.e., plant extract (Plagiochasma rupestre), biosynthesized silver nanoparticles (AgNPs), and healing clay (bentonite) as antimicrobial agent is reported but their combined effect as a ternary system is a new approach. This study is aimed at investigating the impact of the proposed ternary system against selected human pathogens. AgNPs were synthesized by using Plagiochasma rupestre extract (aqueous) as reducing agent and neutral polymer (PVP) as stabilizer. The morphology, size, and structural properties of synthesized AgNPs were determined with XRD and SEM analysis which showed spherical monomodal particles with an average particle size of 25.5 nm. The antibacterial and antifungal activities of the individual and nanoternary system were investigated. The phytochemical screening of plant extract showed the presence of alkaloids, flavonoids, phenol, and glycosides in methanol extract as compare to aqueous and acetone extract. The antimicrobial activities of crude extracts of Plagiochasma rupestre with AgNPs and bentonite clay were studied as an appropriate candidate for treatment of microbial infections, especially bacterial and fungal diseases. The antioxidant activity of Plagiochasma rupestre aqueous extract and nanoparticles was assessed by (DPPH) free radical, and absorbance was checked at 517 nm. Crude extract has inhibitory effect towards bacteria and fungi, and bentonite clay also showed some degree of antimicrobial resistance. Strategy can be efficiently applied for future engineering and medical. The nanoternary systems showed 3 and 3.5 times higher antibacterial and antifungal activity, respectively, in comparison to Plagiochasma rupestre and bentonite clay, individually.

Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations

Background

Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.

Methods

In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.

Results

Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.

Conclusion

Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Ambient temperature and Covid-19 transmission: An evidence from a region of Iran based on weather station and satellite data

BACKGROUND

The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran.

METHOD

Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models' parameters, autocorrelation model and function of associations.

RESULTS

The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed.

CONCLUSION

However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.

Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.

Mortality due to COVID-19 in Spain and its association with environmental factors and determinants of health

BACKGROUND

The objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 (CM) per 1,000,000 inhabitants 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). 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 g/m³ in PM₁₀ and NO₂ and by 1 ℃ 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.

RESULTS

Statistically significant associations were found between PM₁₀, NO₂ and the CM. These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM₁₀ being the variable that showed greater association, with the CM followed of NO₂ in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior.

CONCLUSIONS

The role of PM₁₀ is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with CM. The role of the meteorological variables-temperature and HA-was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00617-z.

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

Effect of meteorological factors on the COVID-19 cases: a case study related to three major cities of the Kingdom of Saudi Arabia

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.

Association between temperature and COVID-19 transmission in 153 countries

The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R₀ and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.

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.

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.

Air quality during COVID-19 lockdown and its implication toward sustainable development goals

Air pollution is directly as well as indirectly linked with several of the United Nations Sustainable Development Goals (SDGs). Hence, focused efforts and strategies toward improving the air quality can lead to direct reduction in the adverse impacts on human health and our cities and setting climate mitigation targets. The worldwide outbreak of the novel coronavirus (COVID-19) has forced various governments around the world to suspend nonessential activities due to the unavailability of the vaccine. This unprecedented lockdown led to significant decline in major criteria air pollutants—PM_2.5, PM₁₀, CO, and NO₂—with more than 50% decline in several cities across the world. However, SO₂ did not change much over some regions, while O₃ has shown some increase. The majority of these changes are well supported by the reduced pollutant emissions, primarily from vehicular sources in urban areas. A slight decline has also been observed in global greenhouse gas (GHG) emissions during the lockdowns. The lockdown illustrates the need for a potential shift of anthropogenic activities toward a more sustainable lifestyle for ameliorating air quality and thus paving the pathway to achieve SDGs. The COVID-19-induced lockdown scenario should be exploited to understand future measures to improve air quality and mitigate the adverse health and climate effects. This chapter explores the impact of the national lockdowns on urban air quality across the globe. Learnings from this natural intervention and future policy implications toward improving air quality are further discussed.

Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM_2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

The impact of meteorological factors on COVID‐19 of California and its lag effect

As of March 30, 2021, COVID‐19 has been circulating globally for more than 1 year, posing a huge threat to the safety of human life and property. Understanding the relationship between meteorological factors and the COVID‐19 can provide positive help for the prevention and control of the global epidemic. We take California as the research object, use Geodetector to screen out the meteorological factors with the strongest explanatory power for the epidemic, then use partial correlation analysis to study the correlation between the two, and finally construct a distributed lag non‐linear model (DLNM) to further explore the relationship between the dominant factor and COVID‐19 and its lag effect. It turns out that temperature has a greater impact on COVID‐19 and the two have a significant negative correlation. When the temperature is lower than 50°F, it has a significant promotion effect on the epidemic, and the relative risk (RR) increases approximately exponentially as the temperature decreases. The delayed effect of the cold effect on the epidemic can be as long as 15 days. This study has shown that more attention should be paid to epidemic prevention and control when the temperature is low, and the delay effect of temperature on the spread of the epidemic cannot be ignored.

Flight attendant occupational nutrition and lifestyle factors associated with COVID-19 incidence

In the era of COVID-19, essential workers are plagued with unforeseen and obfuscated challenges. Flight attendants are a unique subgroup of essential workers who face a multitude of health risks attributed to occupational exposures that are accentuated by the COVID-19 pandemic. Such risks can be ameliorated with strategies that target factors which enhance COVID-19 risk, including modifiable factors of diet and lifestyle. The aim of this cross-sectional study is to detect occupational dietary and lifestyle factors which could increase COVID-19 incidence amongst flight attendants. To identify potential risk factors, a questionnaire was administered to eighty-four flight attendants and examined the participants’ diet and lifestyle, and COVID-19 incidence. Descriptive statistics and logistic regression indicated that the participants’ perceived dietary quality at work (p = 0.003), sleep disruptions which impacted their consumption of a healthy diet (p = 0.013), job tenure (OR: 0.67, 95% CI: 0.46:0.98) and frequency of reported cold/flu (OR: 1.49, 95% CI: 1.014–2.189) were all factors associated with confirmed/suspected COVID-19 incidence. This study also revealed that a lack of infrastructure for food storage and time limitations are considerable occupational barriers for flight attendants to consume healthy foods. Additional investigation can further elucidate these relationships and related solutions to mitigate COVID-19 risk in the future.

A Review of SARS-CoV2: Compared With SARS-CoV and MERS-CoV

The outbreak of coronavirus disease 2019 (COVID-19) has been spreading rapidly in China and the Chinese government took a series of policies to control the epidemic. Studies found that severe COVID-19 is characterized by pneumonia, lymphopenia, exhausted lymphocytes and a cytokine storm. Studies have showen that SARS-CoV2 has significant genomic similarity to the severe acute respiratory syndrome (SARS-CoV), which was a pandemic in 2002. More importantly, some diligent measures were used to limit its spread according to the evidence of hospital spread. Therefore, the Public Health Emergency of International Concern (PHEIC) has been established by the World Health Organization (WHO) with strategic objectives for public health to curtail its impact on global health and economy. The purpose of this paper is to review the transmission patterns of the three pneumonia: SARS-CoV2, SARS-CoV, and MERS-CoV. We compare the new characteristics of COVID-19 with those of SARS-CoV and MERS-CoV.

A study on the effects of meteorological and climatic factors on the COVID-19 spread in Canada during 2020

The Coronavirus (COVID-19) pandemic has infected more than three million people, with thousands of deaths and millions of people into quarantine. In this research, the authors focus on meteorological and climatic factors on the COVID-19 spread, the main parameters including daily new cases of COVID-19, carbon dioxide (CO2) emission, nitrogen dioxide (NO2), Sulfur dioxide (SO2), PM2.5, Ozone (O3), average temperature, and humidity are examined to understand how different meteorological parameters affect the COVID-19 spread in Canada? The graphical quantitative analysis results indicate that CO2 emissions, air quality, temperature, and humidity have a direct negative relationship with COVID-19 infections. Quantile regression analysis revealed that air quality, Nitrogen, and Ozone significantly induce the COVID-19 spread across Canadian provinces. The findings of this study are contrary to the earlier studies, which argued that weather and climate change significantly increase COVID-19 infections. We suggested that meteorological and climatic factors might be critical to reducing the COVID-19 new cases in Canada based on the findings. This work's empirical conclusions can provide a guideline for future research and policymaking to stop the COVID-19 spread across Canadian provinces.

How Good is the Science That Informs Government Policy? A Lesson From the U.K.'s Response to 2020 CoV-2 Outbreak

In an era when public faith in politicians is dwindling, yet trust in scientists remains relatively high, governments are increasingly emphasizing the role of science based policy-making in response to challenges such as climate change and global pandemics. In this paper we question the quality of some scientific advice given to governments and the robustness and transparency of the entire framework which envelopes such advice, all of which raise serious ethical concerns. In particular we focus on the so-called Imperial Model which heavily influenced the government of the United Kingdom in devising its response to the COVID-19 crisis. We focus on and highlight several fundamental methodological flaws of the model, raise concerns as to the robustness of the system which permitted these to remain unchallenged, and discuss the relevant ethical consequences.

Spatio-temporal variation in fine particulate matter and effect on air quality during the COVID-19 in New Delhi, India

Novel Coronavirus disease has affected almost all the countries; which leads to the pandemic, impacting adversely on environment. The impact on environment during pre-and during lockdowns needs an attention to correlate the pollutants from industrial emissions and other factors. Therefore, the current study demonstrates the changes in fine particulate matter PM_2.5, PM₁₀ and effect on air quality during lockdown. The highest reduction was observed in lockdown I (25 March - 14 April) as compared to others lockdowns (between 15 April and 31st May 2020) due to the complete shutdown of industrial, transport, and construction activities. A significant reduction in PM_2.5 and PM₁₀ from 114.27 μg/m³ and 194.48 μg/m³ for pre-lockdown period to 41.41 μg/m³ and 86.81 μg/m³ for lockdown I was observed. The levels of air quality index fall under satisfactory category for lockdown I whereas satisfactory to moderate category for other lockdowns. The present study revealed a strong correlation between PM_2.5 and PM₁₀ levels during the pre-lockdown period (0.71) and through lockdown IV (0.76), which indicate that change in the PM₁₀ level influences the PM_2.5 level greatly. The findings of the present study could be scaled up nationwide and might be useful in formulating air pollution reduction policies in the future.

Functional variation (Q63R) in the cannabinoid CB2 receptor may affect the severity of COVID-19: a human study and molecular docking

Evidence supports a role of host genetic diversity in the clinical course of coronavirus disease 2019 (COVID-19). Variation in the cannabinoid CB2 receptor gene (CNR2) could affect the regulatory action of endocannabinoids on the immune system, resulting in an increased risk of various inflammatory diseases. The present study investigated the relationship between the CNR2-Q63R variant and COVID-19 severity. A total of 200 Iranian COVID-19 patients were enrolled in the study and genotyped using a TaqMan assay. The co-dominant, dominant, recessive, over-dominant, and additive inheritance models were analyzed using SNPStats software. In silico molecular docking was also performed to simulate the effects of the Q63R variation on CB2 binding with a ligand and with the G-protein. A significant difference in the Q63R allele and genotype distribution was found between expired and discharged COVID-19 patients in co-dominant, recessive, and additive inheritance models. The molecular docking results showed that the predicted structure of mutant CB2 (63R type) could not bind to the G-protein in the correct position. The data indicated that the Q63R variation in the CNR2 gene may affect the severity of COVID-19. Identification of genes related to susceptibility and severity of COVID-19 may lead to specific targets for drug repurposing or development.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Is compulsory home quarantine less effective than centralized quarantine in controlling the COVID-19 outbreak? Evidence from Hong Kong

Faced with the global spread of COVID-19, the Hong Kong government imposed compulsory home quarantine on all overseas arrivals, while cities in mainland China and Macau adopted a more stringent centralized quarantine approach. This study evaluates the effectiveness of compulsory home quarantine as a means of pandemic control. Combining epidemiological data with traditional socioeconomic and meteorological data from over 250 cities, we employ the Synthetic Control Method (SCM) to construct a counterfactual "synthetic Hong Kong". This model simulates the infection trends for a hypothetical situation in which HK adopts centralized quarantine measures, and compares them to actual infection numbers. Results suggest that home quarantine would have been less effective than centralized quarantine initially. However, the infection rate under home quarantine later converges with the counterfactual estimate under centralized quarantine (0.136% vs. 0.174%), suggesting similar efficacy in the later phase of implementation. Considering its minimal reliance on public resources, home quarantine with heightened enforcement may therefore be preferable to centralized quarantine in countries with limited public health resources. Home quarantine as a quarantine alternative balances public protection and individual freedom, while conserving resources, making it a more sustainable option for many cities.

Impact of PM2.5 concentration, weather and population on COVID-19 morbidity and mortality in Baghdad and Kuwait cities

The coronavirus (COVID-19) pandemic is a global health crisis and biggest challenge facing the world. Station measurements of fine particulate matter (PM2.5) concentration in Baghdad and Kuwait during the period January–July 2020 are analyzed as well as assessment of correlation between PM2.5, weather conditions (air temperature, relative humidity, wind speed), population density and COVID-19 morbidity and mortality. A significant improvement (decrease) has observed during total and partial curfew in PM2.5 at Baghdad by 35%, 12.4%, respectively, from PM2.5 mean during the study period that is less than the WHO recommended PM2.5 level especially in total curfew. This decrease in PM2.5 pollution and people’s mobility in Baghdad at total and partial curfew contributed to decrease injuries and mortality. PM2.5 during total and partial curfew in Kuwait country witnessed increasing by 38.4% and decreasing by 22.3% from the PM2.5 mean, respectively, but still higher than WHO standard level. This increase in PM2.5 at total curfew was related to burning accidents in the oil wells which caused increasing in PM2.5 pollutant and then an increase in number of injuries and mortality during that time. In general during all study period our research found that PM2.5 and wind speed exhibit weak relation with COVID-19 morbidity and mortality but strong relation with increasing temperature and decreasing humidity. The high population density had a good association with increasing daily new cases, mortality due to COVID-19 pandemic. Thus, these factors may be taken into consideration in policy development for the control and prevention of new chains of the Coronavirus pandemic.

Effect of meteorological factors and Air Quality Index on the COVID-19 epidemiological characteristics: an ecological study among 210 countries

The survival of COVID-19 in different environments may be affected by a variety of weather, pollution, and seasonal parameters. Therefore, the present study aims to conduct an ecological investigation on COVID-19 average growth rate of daily cases and deaths influenced by environmental factors (temperature, humidity, and air pollution) using a sample size of adjusted cumulative incidence of daily cases and deaths based on five 60-day periods. Research data was gathered on official websites, including information on COVID-19, meteorological data, and air pollution indicators from December 31, 2019, to October 12, 2020, from 210 countries. Spearman correlation and generalized additive model (GAM) were used to analyze the data. During the observed period, the COVID-19 average growth rate of daily cases (r = -0.08, P =0.151) and deaths (r= -0.09, P = 0.207) were not correlated with humidity. Also, there was a negative relationship between the COVID-19 average growth rate of new cases and deaths with the Air Quality Index (AQI) and wind (new cases and wind: r=-0.25, P= 0.04). Furthermore, the data related to the first and second 60 day of the adjusted cumulative incidence of COVID-19 daily cases and deaths were not associated with humidity and Air Quality Index (AQI). The result of GAM showed the effect of AQI on the average growth rate of COVID-19 new cases and deaths. This study provides evidence for a positive relationship between COVID-19 daily cases, deaths, and AQI.

Physical distancing implementation, ambient temperature and Covid-19 containment: An observational study in the United States

Governments may relax physical distancing interventions for coronavirus disease 2019 (Covid-19) containment in warm seasons/areas to prevent economic contractions. However, it is not clear whether higher temperature may offset the transmission risk posed by this relaxation. This study aims to investigate the associations of the effective reproductive number (R_t) of Covid-19 with ambient temperature and the implementation of physical distancing interventions in the United States (US). This study included 50 states and one territory of the US with 4,532,650 confirmed cases between 29 January and 31 July 2020. We used an interrupted time-series model with a state-level random intercept for data analysis. An interaction term of 'physical distancing×temperature' was included to examine their interactions. Stratified analyses by temperature and physical distancing implementation were also performed to analyse the modifying effects. The overall median (interquartile range) R_t was 1.2 (1.0-2.3). The implementation of physical distancing was associated with a 12% decrease in the risk of R_t (relative risk [RR]: 0.88, 95% confident interval [CI]: 0.86-0.89), and each 5 °C increase in temperature was associated with a 2% decrease (RR: 0.98, 95%CI: 0.97-0.98). We observed a statistically significant interaction between temperature and physical distancing implementation, but all the RRs were small (close to one). The containing effects of high temperature were attenuated by 5.1% when physical distancing was implemented. The association of COVID-19 R_t with physical distancing implementation was more stable (0.88 vs. 0.89 in days when temperature was low and high, respectively). Increased temperature did not offset the risk of Covid-19 R_t posed by the relaxation of physical distancing implementation. Our study does not recommend relaxing the implementation of physical distancing interventions in warm seasons/areas.

Monitoring of new coronavirus (SARS-CoV-2): Origin, transmission, and food preservation methods

Unfortunately, there is limited research on coronavirus survival of food products and also food processing. The knowledge of the physical and chemical characteristics of coronaviruses mostly comes from the study of SARS-CoV and MERS-CoV physical (i.e., thermal processing, chilling and freezing, microwave irradiation, ultraviolet light, gamma irradiation, high hydrostatic pressure) and chemical (acidification and use of common disinfectants in the food industry like chlorinated derivatives and ozone) are means which could be used to inactive the coronaviruses or reduce the infection. These methods can be applied individually or in combination to act better performance. Thermal processing is one of the most effective methods for inactive coronavirus. Heating at 75°C (15-60 min) and 65°C (1 min) was the best temperature for inactive SARS-CoV and MERS virus, respectively. Among irradiation methods (microwave, UV, and gamma), the most effective one is UVC rays. Moreover, the use of disinfectant like chlorinated derivatives is appropriate way to disinfect food product surfaces.

Novelty impact statement

This review provided updated information on effective strategies for inactive coronavirus that can be used in the food industry. SARS-CoV-2 as a new pandemic coronavirus was initiated from contaminated foods and can be transmitted by close contact, aerosols, and food surfaces. Food preservation (physical and chemical) methods could decrease SARS-CoV-2. Probably, heating and UVC are the most effective approach to inactive SARS-CoV-2. Despite the findings of coronavirus inactivation which were here discussed, much research is still needed for the development of new approaches to overcome the coronavirus.

Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

Learning from COVID-19: Infectious Disease Vulnerability Promotes Pro-Environmental Behaviors

Environmental problems, such as climate change, pollution, and environmental degradation, are important contributors to the spread of infectious diseases, such as COVID-19 and SARS. For instance, a greater concentration of ambient NO₂ was associated with faster transmission of the SARS-CoV-2 virus, which causes COVID-19. However, it remains unclear whether outbreaks of infectious diseases arouse individuals' concern on the need to protect the environment and therefore promote more pro-environmental behaviors. To this end, we examined the relationship between infectious disease vulnerability and pro-environmental behaviors using data from a cross-societal survey (N = 53 societies) and an experiment (N = 214 individuals). At both the societal and the individual levels, infectious disease vulnerability increased pro-environmental behaviors. At the societal level, this relationship was mediated by citizens' level of environmental concern. At the individual level, the relationship was mediated by empathy. The findings show that infectious disease vulnerability is conducive to pro-environmental behaviors.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

The transmission of SARS-CoV-2 is likely comodulated by temperature and by relative humidity

Inferring the impact of climate upon the transmission of SARS-CoV-2 has been confounded by variability in testing, unknown disease introduction rates, and changing weather. Here we present a data model that accounts for dynamic testing rates and variations in disease introduction rates. We apply this model to data from Colombia, whose varied and seasonless climate, central port of entry, and swift, centralized response to the COVID-19 pandemic present an opportune environment for assessing the impact of climate factors on the spread of COVID-19. We observe strong attenuation of transmission in climates with sustained daily temperatures above 30 degrees Celsius and simultaneous mean relative humidity below 78%, with outbreaks occurring at high humidity even where the temperature is high. We hypothesize that temperature and relative humidity comodulate the infectivity of SARS-CoV-2 within respiratory droplets.

Do Temperature and Humidity Affect the Transmission of SARS-CoV-2?-A Flexible Regression Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus that causes Coronavirus disease 2019 (COVID-19). Temperature and humidity are two essential factors in the transmission of SARS-CoV-2 affect the respiratory system of human. This study aimed to investigate the effects of temperature and humidity on the transmission of SARS-CoV-2 and the Spread Covid-19. The daily number of SARS-CoV-2 infected new cases, and the number of death due to Covid-19 are considered the response variables. Data are collected from March 08, 2020 to January 31, 2021. A flexible regression model under the Generalized Additive Models for Location Scale and Shape framework is used to analyze data. The temperature and humidity have a significant impact on the transmission of SARS-CoV-2. The temperature is highly significant in the number of SARS-CoV-2 infected new cases and number of death due to COVID-19. In contrast, the humidity is significant on the number of SARS-CoV-2 infected new cases, but it is insignificant on the number of death due to COVID-19 at a 5% level of significance. The analysis revealed that both the temperature and humidity inversely affected the daily number of deaths and new cases of COVID-19.

The Association Between COVID-19, Air Pollution, and Climate Change

This mini-review aims to highlight both the positive and negative relationship between COVID-19 and air pollution and climate change based on current studies. Since, COVID-19 opened a bibliographic door to scientific production, so there was a limit to research at the moment. There were two sides to the relationship between COVID-19 and both air pollution and climate change. The associated with climate change, in particular, defines the relationship very loosely. Many studies have revealed a positive correlation between COVID-19 and each air pollutants, while some studies shown a negative correlation. There were a few studies that focused on the relationship between COVID-19 in terms of climate. Meanwhile, there were many studies explained the relationship with meteorological factors instead.

Meteorological conditions are heterogeneous factors for COVID-19 risk in China

Whether meteorological factors influence COVID-19 transmission is an issue of major public health concern, but available evidence remains unclear and limited for several reasons, including the use of report date which can lag date of symptom onset by a considerable period. We aimed to generate reliable and robust evidence of this relationship based on date of onset of symptoms. We evaluated important meteorological factors associated with daily COVID-19 counts and effective reproduction number (R_t) in China using a two-stage approach with overdispersed generalized additive models and random-effects meta-analysis. Spatial heterogeneity and stratified analyses by sex and age groups were quantified and potential effect modification was analyzed. Nationwide, there was no evidence that temperature and relative humidity affected COVID-19 incidence and R_t. However, there were heterogeneous impacts on COVID-19 risk across different regions. Importantly, there was a negative association between relative humidity and COVID-19 incidence in Central China: a 1% increase in relative humidity was associated with a 3.92% (95% CI, 1.98%-5.82%) decrease in daily counts. Older population appeared to be more sensitive to meteorological conditions, but there was no obvious difference between sexes. Linear relationships were found between meteorological variables and COVID-19 incidence. Sensitivity analysis confirmed the robustness of the association and the results based on report date were biased. Meteorological factors play heterogenous roles on COVID-19 transmission, increasing the possibility of seasonality and suggesting the epidemic is far from over. Considering potential climatic associations, we should maintain, not ease, current control measures and surveillance.

Assessment of weather and atmospheric pollution as a co-factor in the spread of SARS-CoV-2

BACKGROUND AND AIM

COVID-19 is a persistent and ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Non-anthropogenic factors, such as weather conditions and air quality are possible predictors of respiratory diseases, such as COVID-19. Weather conditions may also be a direct cause of biological interactions between SARS-CoV-2 and humans and vary widely between regions. The course of an epidemic is determined by several factors, including demographic and environmental parameters, many of which have an unknown correlation with COVID-19. The goal of this study is to access the influence of ground surface particulate matter and weather parameters on the dissemination of COVID-19 in Ljubljana, Slovenia.

METHODS

Spearman rank correlation was used to investigate the association between new daily COVID-19 cases and weather data.

RESULTS

The current study has found correlations between weather variables and PM particles with new cases of COVID-19.

CONCLUSIONS

The correlations observed are highly dependent on the local policies that were in force during the period under study. The interaction between weather conditions and human behaviour may also be an important factor in understanding the relationship between weather and the spread of COVID -19.

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.

The Effect of Ambient Environmental Conditions on COVID-19 Mortality: A Systematic Review

Weather conditions may have an impact on SARS-CoV-2 virus transmission, as has been shown for seasonal influenza. Virus transmission most likely favors low temperature and low humidity conditions. This systematic review aimed to collect evidence on the impact of temperature and humidity on COVID-19 mortality. This review was registered with PROSPERO (registration no. CRD42020196055). We searched the Pubmed, Embase, and Cochrane COVID-19 databases for observational epidemiological studies. Two independent reviewers screened the title/abstracts and full texts of the studies. Two reviewers also performed data extraction and quality assessment. From 5051 identified studies, 11 were included in the review. Although the results were inconsistent, most studies imply that a decrease in temperature and humidity contributes to an increase in mortality. To establish the association with greater certainty, future studies should consider accurate exposure measurements and important covariates, such as government lockdowns and population density, sufficient lag times, and non-linear associations.