Impact of weather on COVID-19 pandemic in Turkey

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: A Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximation (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the [Formula: see text] of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Acute Respiratory Viral Infections Among Adult Patients in Edirne, Turkey

Background/Objectives: This study aimed to evaluate the prevalence of viral agents identified by Multiplex PCR in acute respiratory viral infection (ARVI) patients at Edirne Sultan 1, Murat State Hospital, from April 2023 to April 2024, and to investigate the relationship between monthly average humidity and viral positivity rates. Methods: The study included 764 adult patients (aged 18 and older) diagnosed with influenza symptoms. Respiratory viral samples were collected and analyzed for COVID-19, influenza A and B, and RSV using Multiplex PCR, with results evaluated retrospectively. Continuous variables in the study were compared using a t-test, and categorical variables were compared with a chi-square test. A logistic regression analysis was performed for the analysis of COVID-19. In this analysis, PCR positivity was the dependent variable, while age, gender, and humidity level served as independent variables. Results: COVID-19 PCR positivity was detected in 142 patients (18.6%), with INF-A (influenza A) in 13 (3.7%), INF-B (influenza B) in 15 (4.2%), and RSV in 2 (0.6%). Higher humidity (over 60%) was associated with reduced viral PCR positivity rates for COVID-19 and influenza B, while low (up to 40%)/normal (40-60%) humidity correlated with positivity rate (p < 0.05 for both). Logistic regression analysis indicated that high humidity levels offer protection against COVID-19 (OR: 0.356; 95% CI: 0.245-0.518). Conclusions: Our study provides essential epidemiological data by summarizing monthly virus distribution in Edirne.

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 μg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases.

Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey

During pandemic periods, there is an intense flow of patients to hospitals. Depending on the disease, many patients may require hospitalization. In some cases, these patients must be taken to intensive care units and emergency interventions must be performed. However, finding a sufficient number of hospital beds or intensive care units during pandemic periods poses a big problem. In these periods, fast and effective planning is more important than ever. Another problem experienced during pandemic periods is the burial of the dead in case the number of deaths increases. This is also a situation that requires due planning. We can learn some lessons from Covid 19 pandemic and be prepared for the future ones. In this paper, statistical properties of the daily cases and daily deaths in Turkey, which is one of the most affected countries by the pandemic in the World, are studied. It is found that the characteristics are nonstationary. Then, random forest regression is applied to predict Covid-19 daily cases and deaths. In addition, seven other machine learning models, namely bagging, AdaBoost, gradient boosting, XGBoost, decision tree, LSTM and ARIMA regressors are built for comparison. The performance of the models are measured using accuracy, coefficient of variation, root-mean-square score and relative error metrics. When random forest regressors are employed, test data related to daily cases are predicted with an accuracy of 92.30% and with an r2 score of 0.9893. Besides, daily deaths are predicted with an accuracy of 91.39% and with an r2 score of 0.9834. The closest rival in predictions is the bagging regressor. Nevertheless, the results provided by this algoritm changed in different runs and this fact is shown in the study, as well. Comparisons are based on test data. Comparisons with the earlier works are also provided.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

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.

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.

The complex interplay between weather, social activity, and COVID-19 in the US

Empirical studies on the impact of weather and policy interventions on Covid-19 infections have dedicated little attention to the mediation role of social activity. In this study, we combine mobile locations, weather, and COVID-19 data in a two-way fixed effects mediation model to estimate the impact of weather and policy interventions on the COVID-19 infection rate in the US before the availability of vaccines, disentangling their direct impact from the part of the effect that is mediated by the endogenous response of social activity. We show that, while temperature reduces viral infectiousness, it also increases the amount of time individuals spend out of home, which instead favours the spread of the virus. This second channel substantially attenuates the beneficial effect of temperature in curbing the spread of the virus, offsetting one-third of the potential seasonal fluctuations in the reproduction rate. The mediation role of social activity is particularly pronounced when viral incidence is low, and completely offsets the beneficial effect of temperature. Despite being significant predictors of social activity, wind speed and precipitation do not induce sufficient variation to affect infections. Our estimates also suggest that school closures and lockdowns are effective in reducing infections. We employ our estimates to quantify the seasonal variation in the reproduction rate stemming from weather seasonality in the US.

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.

The Variability of Vitamin D Concentrations in Short Children with Short Stature from Central Poland-The Effects of Insolation, Supplementation, and COVID-19 Pandemic Isolation

The aim of the study was to investigate the effects of seasonal variability of insolation, the implementation of new recommendations for vitamin D supplementation (2018), and the SARS-CoV-2 pandemic lockdown (2020) on 25(OH)D concentrations in children from central Poland. The retrospective analysis of variability of 25(OH)D concentrations during the last 8 years was performed in a group of 1440 children with short stature, aged 3.0-18.0 years. Significant differences in 25(OH)D concentrations were found between the periods from mid-2014 to mid-2018, from mid-2018 to mid-2020, and from mid-2020 to mid-2022 (medians: 22.9, 26.0, and 29.9 ng/mL, respectively). Time series models created on the grounds of data from 6 years of the pre-pandemic period and used for prediction for the pandemic period explained over 80% of the seasonal variability of 25(OH)D concentrations, with overprediction for the first year of the pandemic and underprediction for the second year. A significant increase in 25(OH)D concentrations was observed both after the introduction of new vitamin D supplementation guidelines and during the SARS-CoV-2 pandemic; however, the scale of vitamin D deficiency and insufficiency was still too high. Time series models are useful in analyzing the impact of health policy interventions and pandemic restrictions on the seasonal variability of vitamin D concentrations.

Seasonal correlation of meteorological parameters and PM2.5 with the COVID-19 confirmed cases and deaths in Baghdad, Iraq

The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020-2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60-69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.

Association of air pollution and COVID-19 in India

The COVID-19 pandemic has affected the world, leading to significant morbidity and mortality. Various meteorological parameters are considered essential for the viability and transmission of the virus. Multiple reports from various parts of the world suggest a correlation between the disease spread and air pollution severity. This study was carried out to identify the relationship between meteorological parameters, air pollution, and COVID-19 in New Delhi, one of the worst-affected states in India. We studied air pollution and meteorological parameters in New Delhi, India. We obtained data about COVID-19 occurrence, meteorological parameters, and air pollution indicators from various sources from April 1, 2020, until November 12, 2020. We performed correlational analysis and employed autoregressive distributed lag models to identify the relationship between COVID-19 cases, air pollution and meteorological parameters. We found a significant impact of particulate matter (PM) 2.5, PM10, and meteorological parameters on COVID-19. There was a significant positive correlation between daily COVID-19 cases and COVID-19-related deaths with PM2.5 and PM10 levels. Increasing temperature and wind speed were associated with a reduction in the number of cases, while increasing humidity was associated with increased cases. This study demonstrated a significant association between PM2.5 and PM10 and daily COVID-19 cases and COVID-19-related mortality. This knowledge will likely help us prepare well for the future and implement air pollution control measures for other airborne disease epidemics.

Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models

Objectives

The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM_2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic.

Methods

Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5).

Results

The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM_2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM_2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM_2.5 in 2019-2021 decreased.

Conclusion

Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

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

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

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.

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.

Environment and COVID-19 incidence: A critical review

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented worldwide health crisis. Many previous research studies have found and investigated its links with one or some natural or human environmental factors. However, a review on the relationship between COVID-19 incidence and both the natural and human environment is still lacking. This review summarizes the inter-correlation between COVID-19 incidence and environmental factors. Based on keyword searching, we reviewed 100 relevant peer-reviewed articles and other research literature published since January 2020. This review is focused on three main findings. One, we found that individual environmental factors have impacts on COVID-19 incidence, but with spatial heterogeneity and uncertainty. Two, environmental factors exert interactive effects on COVID-19 incidence. In particular, the interactions of natural factors can affect COVID-19 transmission in micro- and macro- ways by impacting SARS-CoV-2 survival, as well as human mobility and behaviors. Three, the impact of COVID-19 incidence on the environment lies in the fact that COVID-19-induced lockdowns caused air quality improvement, wildlife shifts and socio-economic depression. The additional value of this review is that we recommend future research perspectives and adaptation strategies regarding the interactions of the environment and COVID-19. Future research should be extended to cover both the effects of the environment on the COVID-19 pandemic and COVID-19-induced impacts on the environment. Future adaptation strategies should focus on sustainable environmental and public policy responses.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO₂ ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

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

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

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

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.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

An overview and thematic analysis of research on cities and the COVID-19 pandemic: Toward just, resilient, and sustainable urban planning and design

Since early 2020, researchers have made efforts to study various issues related to cities and the pandemic. Despite the wealth of research on this topic, there are only a few review articles that explore multiple issues related to it. This is partly because of the rapid pace of publications that makes systematic literature review challenging. To address this issue, in the present study, we rely on bibliometric analysis techniques to gain an overview of the knowledge structure and map key themes and trends of research on cities and the pandemic. Results of the analysis of 2,799 articles show that research mainly focuses on six broad themes: air quality, meteorological factors, built environment factors, transportation, socio-economic disparities, and smart cities, with the first three being dominant. Based on the findings, we discuss major lessons that can be learned from the pandemic and highlight key areas that need further research.

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97-0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97-0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26-2.74) and 1.078 (95%CI 1.04-1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93-1.31) and 1.007 (95%CI 0.99-1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

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.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers

COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (β = 11.91, 95% CI: 4.77, 19.05) and India (β = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (β = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (β = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models

Coronavirus (COVID-19) is a highly contagious virus (SARS-CoV-2) that has caused a global pandemic since January 2020. Scientists around the world are doing extensive research to control this disease. They are working tirelessly to find out the origin and causes of the disease. Several studies and experiments mentioned that there are some meteorological parameters which are highly correlated with COVID-19 transmission. In this work, we studied the effects of 11 meteorological parameters on the transmission of COVID-19 in Bangladesh. We first applied statistical analysis and observed that there is no significant effect of these parameters. Therefore, we proposed a novel technique to analyze the insight effects of these parameters by using a combination of Random Forest, CART, and Lasso feature selection techniques. We observed that 4 parameters are highly influential for COVID-19 where [Formula: see text] and Cloud have positive association whereas WS and AQ have negative impact. Among them, Cloud has the highest positive impact which is 0.063 and WS has the highest negative association which is [Formula: see text]. Moreover, we have validated our performance using DLNM technique. The result of this investigation can be used to develop an alert system that will assist the policymakers to know the characteristics of COVID-19 against meteorological parameters and can impose different policies based on the weather conditions.

Mediation effect of diversity and availability of high transit service on transit oriented development and spread of COVID-19

The impact of the novel coronavirus disease (COVID-19) continues unabated. Still, it seems that apart from contact and respiratory transmission, the design and development pattern of an area does echoes to be a contributing factor in virus spreadability. The present study considers land use and transportation system parameters under TOD mode of 16 BRT station provinces in Bhopal, India, and COVID-19 cases data were collected from April 2020 to August 2020. Further, the Pearson correlation and mediational analysis were employed to determine the relationship between TODness and COVID-19 spread cases. The bootstrapping method was used to evaluate the mediation effect and describe why and under what conditions they are related. The study shows that TODness and COVID-19 spread cases are positively correlated. The results show a considerable correlation at (p < 0.05) is 0.405 of the dispersed along with TODness of an area in the analysed 16 BRT station areas. In particular, dispersed demonstrated a high-level correlation of 0.681 with TOD areas, whereas a moderate correlation of 0.322 with non-TOD areas was mediated by diversity and the number of available transit service indicators. Diversity and availability of high-quality transit services effectively spread the virus, whereas population density and public transport mediation effects are insignificant. Outcomes from this study may help government authorities and policymakers devise a strategy and adopt preventive measures in subsequent waves of the pandemic.

Graphical abstract

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

SARS-CoV-2 pre-exposure prophylaxis: A potential COVID-19 preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders

The unprecedented worldwide spread of SARS-CoV-2 has imposed severe challenges on global health care systems. The roll-out and widespread administration of COVID-19 vaccines has been deemed a major milestone in the race to restrict the severity of the infection. Vaccines have as yet not entirely suppressed the relentless progression of the pandemic, due mainly to the emergence of new virus variants, and also secondary to the waning of protective antibody titers over time. Encouragingly, an increasing number of antiviral drugs, such as remdesivir and the newly developed drug combination, Paxlovid® (nirmatrelvir/ritonavir), as well as molnupiravir, have shown significant benefits for COVID-19 patient outcomes. Pre-exposure prophylaxis (PrEP) has been proven to be an effective preventive strategy in high-risk uninfected people exposed to HIV. Building on knowledge from what is already known about the use of PrEP for HIV disease, and from recently gleaned knowledge of antivirals used against COVID-19, we propose that SARS-CoV-2 PrEP, using specific antiviral and adjuvant drugs against SARS-CoV-2, may represent a novel preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders. Herein, we critically review the risk factors for severe COVID-19 and discuss PrEP strategies against SARS-CoV-2. In addition, we outline details of candidate anti-SARS-CoV-2 PrEP drugs, thus creating a framework with respect to the development of alternative and/or complementary strategies to prevent COVID-19, and contributing to the global armamentarium that has been developed to limit SARS-CoV-2 infection, severity, and transmission.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

The impact of environmental and climate parameters on the incidence and mortality of COVID-19 in the six Gulf Cooperation Council countries: A cross-country comparison study

Background

Environmental factors can influence the epidemiological dynamics of COVID-19. To estimate the true impact of these factors on COVID-19, climate and disease data should be monitored and analyzed over an extended period of time. The Gulf Cooperation Council (GCC) countries are particularly lacking in such studies. This ecological study investigates the association between climate parameters and COVID-19 cases and deaths in the GCC.

Methods

Data on temperature, wind-speed and humidity and COVID-19 cases and deaths from the six countries of the GCC were collected between 29/1/2020 and 30/3/2021. Using Spearman’s correlation coefficient, we examined associations between climate parameters and COVID-19 cases and deaths by month, over four different time periods. A two-step cluster analysis was conducted to identify distinct clusters of data using climate parameters and linear regression analysis to determine which climate parameters predicted COVID-19 new cases and deaths.

Results

The United Arab Emirates (UAE) had the highest cumulative number of COVID-19 cases while Bahrain had the highest prevalence rate per 100,000. The Kingdom of Saudi Arabia (KSA) reported the highest cumulative number of deaths while Oman recorded the highest death rate per 100,000. All GCC countries, except the UAE, reported a positive correlation between temperature and cases and deaths. Wind speed was positively correlated with cases in Qatar, but negatively correlated with cases in the UAE and deaths in KSA. Humidity was positively correlated with cases and deaths in Oman, negatively correlated in Bahrain, Kuwait, Qatar and KSA but there was no correlation in the UAE. The most significant predictors in cluster analysis were temperature and humidity, while in the regression analysis, temperature, humidity and wind speed predicted new COVID-19 cases and deaths.

Conclusion

This study provides comprehensive epidemiological information on COVID-19 and climate parameters and preliminary evidence that climate may play a key role in the transmission of the COVID-19 virus. This study will assist decision makers in translating findings into specific guidelines and policies for the prevention and elimination of COVID-19 transmission and infection.

Effect of environmental factors on SARS-CoV-2 infectivity in northern hemisphere countries: a 2-year data analysis

OBJECTIVE

The COVID-19 pandemic that emerged in December 2019 brought human life to a standstill. With over 2-year since the pandemic originated from Wuhan, SARS-CoV-2 has caused more than 6 million deaths worldwide. With the emergence of mutant strains and COVID-19 surge waves, it becomes critically important to conduct epidemiological studies that allow us to understand the role of various environmental factors on SARS-CoV-2 infectivity. Our earlier study reported a strong negative correlation between temperature and COVID-19 incidence. This research is an extension of our previous study with an attempt to understand the global analysis of COVID-19 in northern hemisphere countries.

STUDY DESIGN

This research aims at achieving a better understanding of the correlation of environmental factors such as temperature, sunlight, and humidity with new cases of COVID-19 in northern hemisphere from March 2020 to February 2022.

METHODS

To understand the relationship between the different environmental variants and COVID-19, a statistical approach was employed using Pearson, Spearman and Kendall analysis.

RESULTS

Month-wise univariate analysis indicated a strong negative correlation of temperature and sunlight with SARS-CoV-2 infectivity, whereas inconsistencies were observed in correlation analysis in the case of humidity in winter months. Moreover, a strong negative correlation between average temperature of winter months and COVID-19 cases exists as evidenced by Pearson, Spearman, and Kendall analyses. In addition, correlation pattern between monthly temperature and COVID-19 cases of a country mimics to that of sunlight of a country.

CONCLUSION

This pilot study proposes that low temperatures and low sunlight might be additional risk factors for SARS-CoV-2 infectivity, mostly in northern hemisphere countries.

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

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

The Built Environment Assessment of Residential Areas in Wuhan during the Coronavirus Disease (COVID-19) Outbreak

The COVID-19 epidemic has emerged as one of the biggest challenges, and the world is focused on preventing and controlling COVID-19. Although there is still insufficient understanding of how environmental conditions may impact the COVID-19 pandemic, airborne transmission is regarded as an important environmental factor that influences the spread of COVID-19. The natural ventilation potential (NVP) is critical for airborne infection control in the micro-built environment, where infectious and susceptible people share air spaces. Taking Wuhan as the research area, we evaluated the NVP in residential areas to combat COVID-19 during the outbreak. We determined four fundamental residential area layouts (point layout, parallel layout, center-around layout, and mixed layout) based on the semantic similarity model for point of interest (POI) picking. Our analyses indicated that the center-around and point layout had a higher NVP, while the mixed and parallel layouts had a lower NVP in winter and spring. Further analysis showed that the proportion of the worst NVP has been rising, while the proportion of the poor NVP remains very high in Wuhan. This study suggested the need to efficiently improve the residential area layout in Wuhan for better urban ventilation to combat COVID-19 without losing other benefits.

A review of the impact of environmental factors and pollutants on covid-19 transmission

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O₃ concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

The Moderator Role of Spirituality on the Relationship between Fear of COVID-19 and Psychological Well-Being

Collapse

Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran

This study aimed at detecting space-time clusters of COVID-19 cases in Fars Province, Iran and at investigating their potential association with meteorological factors, such as temperature, precipitation and wind velocity. Time-series data including 53,554 infected people recorded in 26 cities from 18 February to 30 September 2020 together with 5876 meteorological records were subjected to the analysis. Applying a significance level of P<0.05, the analysis of space-time distribution of COVID-19 resulted in nine significant outbreaks within the study period. The most likely cluster occurred from 27 March to 13 July 2020 and contained 11% of the total cases with eight additional, secondary clusters. We found that the COVID-19 incidence rate was affected by high temperature (OR=1.64; 95% CI: 1.44-1.87), while precipitation and wind velocity had less effect (OR=0.84; 95% CI: 0.75-0.89 and OR=0.27; 95% CI: 0.14-0.51), respectively.

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

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

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

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

Predictors and time to recovery from COVID-19 among patients attended at the treatment centers in Ekiti State, South West, Nigeria

Introduction

time to clinical recovery from COVID-19 infection and associated factors has not been explored in Nigeria. This study was conducted to assess the predictors and time to recovery from COVID-19 among patients attended to at the treatment centers in Ekiti State, South West.

Methods

a facility-based retrospective cohort study was conducted between March 2020 to October 2021. Laboratory confirmed COVID-19 positive test result of 586 patients receiving treatment at the treatment centres in Ekiti were included. Data were extracted from COVID-19 intake forms and medical records of patients. Data were analysed using descriptive statistics and survival analysis methods including Cox proportional hazards regression model. Level of significance was set at 5%.

Results

the mean age of the patients was 43.46 (SD 0.74) years. Forty-seven percent (47%) of the patients were aged 25-44 years, fifty-one percent (51%) were males. The median recovery time of COVID-19 patients was 21 days (IQR: 14-23). Being a male-patient (95% CI 20.46-21.54), older age (95% CI 20.14-21.86), not admitted in the hospital (95% CI 22.74-23.26), and associated multiple co-morbidities (95% CI 17.65-28.35) were associated with delayed recovery time. Predictors of recovery time of patients from COVID-19 infection were admission status (aHR: 0.71, 95%CI 0.56-0.88; p=0.002) and symptoms on admission (aHR: 0.81, 95%CI 0.66-0.99; p=0.020).

Conclusion

patients with comorbidities, older and those not admitted were more likely to have a delayed clinical recovery from COVID-19. Knowledge of the predictors might help health professionals in risk stratification and better management of patients with COVID-19.

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

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

Supplementary Information

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