Association between ambient temperature and COVID-19 infection in 122 cities from China

TB. Investigation of influential environmental and climatic determinants on COVID-19 spread in India to formulate a sustainable pandemic response

The COVID-19 pandemic has highlighted the need for a Sustainable Pandemic Response Strategy (SPRS), driven by scientific research and engineering principles. This study focuses on Environmental and Climatic Determinants (ECDs) that may influence the occurrence pattern of infectious diseases. The objective of SPRS is to develop a climate-resilient framework for infectious diseases using Earth Observation (EO) data. ECDs were derived from EO data during the COVID-19 study period in India, spanning 1094 days (January 3, 2020, to December 31, 2022). A Convergent Search - Add or Eliminate (CS-AE) algorithm was developed for the investigation of complex association between ECDs and disease occurrence patterns. This algorithm identifies the most influential ECDs in the spread of COVID-19 in India, categorizing them as Determinants of Concern (DOC) or Determinants of Interest (DOI). Shortwave Downward Radiation (SDR) was identified as a DOC, showing a strong correlation (r = 0.9525) with COVID-19 spread. Granger causality analysis was conducted to support the classification of SDR as a Determinant of Concern (DOC). The results confirmed a temporal causal relationship between SDR and disease spread. During the first pandemic wave, significant causality was observed at lags of 2 to 7 days, with the strongest effect at lag 6 (p = 0.001), while in subsequent waves, significance was found across lags of 1 to 6 days. The seasonal effect of SDR and the three pandemic waves in India were observed through a radar chart, illustrating the temporal causal relationship between SDR and COVID-19 spread. The algorithm shows the note of a significant role by SDR in surface and air temperature (r = 0.9525; r = 0.9942) and influences other ECDs which are categorized as DOI. Hence, the proposed CS-AE algorithm provides a robust tool for identifying the most influential ECDs in the spread of infectious diseases, provided the datasets are time-series based.

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.

Discovering the climate dependent disease transmission mechanism through learning-explaining framework

There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy). In particular, we initially learn the incidence rate in an SIRS model based on epidemic data, then use mechanism discovery methods to explore the possible explicit forms of the incidence rate, and consequently explore the possible relationship between transmission rate and meteorological factors. We finally use information criteria and a definition of evaluation score to make model selection, and hence suggest the optimal explicit formula. We illustrate the idea by derive the incidence rate and transmission rate of respiratory infectious diseases based on the case data on influenza-like illness (ILI) in Xi'an, Shaanxi Province of China and meteorological data from 1st January 2010 to 10th November 2016. The finding reveals that the influence of meteorological factors on transmission exhibits very strong nonlinearity, and modeling the effect should be of great care.

Evaluation of the effects of short-term PM2.5 exposure on triglyceride-glucose metrics in a population in eastern China

CONTEXT

The triglyceride-glucose (TyG) index, a novel health indicator, has been widely employed to assess insulin resistance (IR). However, its relationship with fine particulate matter (PM) exposure remains inadequately investigated.

OBJECTIVE

This study endeavors to probe the association between PM2.5 and TyG within the population of eastern China and to determine whether there are disparities in this association among diverse subgroups.

METHODS

We conducted an ecological study on a cohort comprising 39,011 individuals who had undergone at least two physical examinations between 2017 and 2019 at the First Affiliated Hospital of Nanjing Medical University, China. TyG levels concerning short-term PM2.5 exposure were examined using a generalized additive model.

RESULTS

In the overall population, at lags of 0-7 and 0-14 days in the single-pollutant model, it was observed that a 10 µg/m3 rise in PM2.5 corresponded to a 0.0021 elevation in TyG levels. In the multi-pollutant models, at 0-7 and 0-14 days lags, a comparable increase in PM2.5 resulted in an increase in TyG of 0.0073 and 0.0044, respectively. The association remained significant in the subgroup analyses.

CONCLUSION

PM2.5 exposure is related to the TyG index. Controlling air pollution might contribute to maintainin normal lipid metabolism function.

Assessment of environmental factors influencing SARS-CoV-2 in Vietnam's surface water across two years of clinical data

Wastewater-based epidemiology (WBE) is an effective, non-invasive method for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by tracking viral prevalence in water. This study aimed to investigate the presence of SARS-CoV-2 in surface water in Vietnam over two years. One-step quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to quantify SARS-CoV-2 and its variant-specific mutation sites (G339D/E484A) and pepper mild mottle virus (PMMoV) from a total of 315 samples (105 samples per site) to compare with reported Coronavirus disease 2019 (COVID-19) cases and environmental factors. SARS-CoV-2 was detected in 38 % (40/105), 43 % (45/105), and 39 % (41/105) of water samples from Sites A, B, and C, respectively, with concentrations of 3.0-5.6 log10 copies/L. PMMoV concentrations were 5.1-8.9 log10 copies/L. SARS-CoV-2 levels were higher in winter compared with summer. There was a strong positive association between the mutant type and SARS-CoV-2 concentrations (Spearman's rho = 0.77, p < 0.01). The mean concentrations of mutant and nonmutant types were 2.3 and 1.8 log10 copies/L, respectively. Peaks in SARS-CoV-2 concentrations preceded reported COVID-19 cases by 2-4 weeks, with the highest association observed at a 4-week delay (Pearson's correlation coefficient: 0.46-0.53). Environmental factors, including temperature, pH, and electrical conductivity, correlated negatively with SARS-CoV-2 (Spearman's rho = -0.21, -0.28, and -0.21, respectively, p < 0.05), whereas average rainfall, humidity, and dissolved oxygen correlated positively (Spearman's rho = 0.20, 0.27, and 0.51, respectively, p < 0.05). These correlations highlight the significance of environmental variables in understanding viral prevalence in water. Our findings confirmed the utility of WBE as an early warning system for long-term monitoring. Future research should incorporate environmental factors to improve prediction accuracy for clinical cases and other waterborne diseases.

Association between ambient particulate matter and latent tuberculosis infection among 198 275 students

Background

Numerous studies have estimated the impact of outdoor particulate matter (PM) on tuberculosis risk. Nevertheless, whether there is an association between ambient PM and latent tuberculosis infection (LTBI) risk remains uncertain.

Methods

We collected the basic information and LTBI test results of students who underwent freshmen enrolment physical examinations in 68 middle schools from six prefecture-level cities located in eastern China between 2018 and 2021. We also extracted data on air pollutant concentrations and meteorological factors in six cities between 2015 and 2021. We applied the generalised additive model (GAM) to assess the effect of PM on LTBI risk.

Results

We included 198 275 students in the final analysis, of whom 11 721 were diagnosed with LTBI. The LTBI group had higher proportions of males (P < 0.001), individuals of Han nationality (P < 0.001), and body mass index compared to the non-LTBI group (P < 0.001). For each 1-μg/m3 increase in PM10 concentration, the LTBI risk increased by 0.82% (95% confidence interval (CI) = 0.65-1.00), 0.90% (95% CI = 0.73-1.08), and 0.86% (95% CI = 0.69-1.03) when lagged at one, two, and three years, respectively. For PM2.5, the LTBI risk increased by 0.91% (95% CI = 0.63-1.20), 1.05% (95% CI = 0.75-1.36), and 1.32% (95% CI = 0.96-1.69) when lagged at one, two, and three years, respectively.

Conclusions

Outdoor PM concentration was positively correlated with LTBI risk. Considering that many developing countries are facing the dual challenges of high LTBI rates and serious ambient air pollution, reducing outdoor PM concentration would contribute to alleviating their tuberculosis burden.

Environmental predictors of SARS-CoV-2 infection incidence in Catalonia (northwestern Mediterranean)

Numerous studies have explored whether and how the spread of the SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), responds to environmental conditions without reaching consistent answers. Sociodemographic factors, such as variable population density and mobility, as well as the lack of effective epidemiological monitoring, make it difficult to establish robust correlations. Here we carry out a regional cross-correlation study between nine atmospheric variables and an infection index (Ic ) estimated from standardized positive polymerase chain reaction (PCR) test cases. The correlations and associated time-lags are used to build a linear multiple-regression model between weather conditions and the Ic index. Our results show that surface pressure and relative humidity can largely predict COVID-19 outbreaks during periods of relatively minor mobility and meeting restrictions. The occurrence of low-pressure systems, associated with the autumn onset, leads to weather and behavioral changes that intensify the virus transmission. These findings suggest that surface pressure and relative humidity are key environmental factors that may be used to forecast the spread of SARS-CoV-2.

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Ambient air pollution contributed to pulmonary tuberculosis in China

Published studies on outdoor air pollution and tuberculosis risk have shown heterogeneous results. Discrepancies in prior studies may be partially explained by the limited geographic scope, diverse exposure times, and heterogeneous statistical methods. Thus, we conducted a multi-province, multi-city time-series study to comprehensively investigate this issue. We selected 67 districts or counties from all geographic regions of China as study sites. We extracted data on newly diagnosed pulmonary tuberculosis (PTB) cases, outdoor air pollutant concentrations, and meteorological factors in 67 sites from January 1, 2014 to December 31, 2019. We utilized a generalized additive model to evaluate the relationship between ambient air pollutants and PTB risk. Between 2014 and 2019, there were 172,160 newly diagnosed PTB cases reported in 67 sites. With every 10-μg/m3 increase in SO2, NO2, PM10, PM2.5, and 1-mg/m3 in CO, the PTB risk increased by 1.97% [lag 0 week, 95% confidence interval (CI): 1.26, 2.68], 1.30% (lag 0 week, 95% CI: 0.43, 2.19), 0.55% (lag 8 weeks, 95% CI: 0.24, 0.85), 0.59% (lag 10 weeks, 95% CI: 0.16, 1.03), and 5.80% (lag 15 weeks, 95% CI: 2.96, 8.72), respectively. Our results indicated that ambient air pollutants were positively correlated with PTB risk, suggesting that decreasing outdoor air pollutant concentrations may help to reduce the burden of tuberculosis in countries with a high burden of tuberculosis and air pollution.

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

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

Association Between Climatic Factors and Varicella Incidence in Wuxi, East China, 2010-2019: Surveillance Study

BACKGROUND

Varicella is a common infectious disease and a growing public health concern in China, with increasing outbreaks in Wuxi. Analyzing the correlation between climate factors and varicella incidence in Wuxi is crucial for guiding public health prevention efforts.

OBJECTIVE

This study examines the impact of meteorological variables on varicella incidence in Wuxi, eastern China, from 2010 to 2019, offering insights for public health interventions.

METHODS

We collected daily meteorological data and varicella case records from January 1, 2010, to December 31, 2019, in Wuxi, China. Generalized cross-validation identified optimal lag days by selecting those with the lowest score. The relationship between meteorological factors and varicella incidence was analyzed using Poisson generalized additive models and segmented linear regression. Subgroup analyses were conducted by gender and age.

RESULTS

The study encompassed 64,086 varicella cases. Varicella incidence in Wuxi city displayed a bimodal annual pattern, with peak occurrences from November to January of the following year and lower peaks from May to June. Several meteorological factors influencing varicella risk were identified. A decrease of 1°C when temperatures were ≤20°C corresponded to a 1.99% increase in varicella risk (95% CI 1.57-2.42, P<.001). Additionally, a decrease of 1°C below 22.38°C in ground temperature was associated with a 1.36% increase in varicella risk (95% CI 0.96-1.75, P<.001). Each 1 mm increase in precipitation above 4.88 mm was associated with a 1.62% increase in varicella incidence (95% CI 0.93-2.30, P<.001). A 1% rise in relative humidity above 57.18% increased varicella risk by 2.05% (95% CI 1.26-2.84, P<.001). An increase in air pressure of 1 hPa below 1011.277 hPa was associated with a 1.75% rise in varicella risk (95% CI 0.75-2.77, P<.001). As wind speed and evaporation increased, varicella risk decreased linearly with a 16-day lag. Varicella risk was higher with sunshine durations exceeding 1.825 hours, with a 14-day lag, increasing by 1.30% for each additional hour of sunshine (95% CI 0.62-2.00, P=.006). Subgroup analyses revealed that teenagers and children under 17 years of age faced higher varicella risks associated with temperature, average ground temperature, precipitation, relative humidity, and air pressure. Adults aged 18-64 years experienced increased risk with longer sunshine durations. Additionally, males showed higher varicella risks related to ground temperature and air pressure compared with females. However, no significant gender differences were observed regarding varicella risks associated with temperature (male: P<.001; female P<.001), precipitation (male: P=.001; female: P=.06), and sunshine duration (male: P=.53; female: P=.04).

CONCLUSIONS

Our preliminary findings highlight the interplay between varicella outbreaks in Wuxi city and meteorological factors. These insights provide valuable support for developing policies aimed at reducing varicella risks through informed public health measures.

Impact of short-term exposure to ambient air pollutants and meteorological factors on COVID-19 incidence and mortality: A retrospective study from Dammam, Saudi Arabia

The symptoms of COVID-19 included fever with or without respiratory syndrome, but patients subsequently developed pulmonary abnormalities. Exposure to air pollution, meanwhile, is associated with complications such as acute respiratory inflammations, asthma attack, and deaths from cardiorespiratory disease. To analyze the association of the air quality index (AQI), ambient air pollutants (PM10, SO2 and O3) and meteorological parameters (temperature and relative humidity [RH]) with COVID-19 incidence and mortality, a retrospective study was conducted to examine COVID-19 infection, meteorological parameters, ambient air quality and ambient air pollutants in Dammam from 1 January to 30 April 2021. Data of COVID-19 incidence and mortality for Dammam were retrieved from Saudi Arabia Ministry of Health's publicly accessible database. Meteorological data, AQI and average PM10, SO2 and O3 values were extracted from the publicly available website of Ministry of Environment, Water and Agriculture. The correlation of COVID-19 incidence and mortality with the independent variables was analysed by Pearson's correlation test or Spearman's rho test as applicable, and a p-value less than 0.05 was considered significant. COVID-19 incidence exhibited a positive correlation with temperature (r = 0.537, p = .0001) and a negative correlation with RH (r=-0.487, p=.0001). No correlation was observed between the meteorological variables and COVID-19 mortality. COVID-19 incidence showed a positive correlation with AQI (r=0.269, p=.015) and with the ambient air pollutants SO2 and O3 (r=0.258, p=.018), and COVID-19 mortality showed a positive correlation with PM10 (r s  = 0.344, p=.002). Short-term exposure to O3, SO2 and higher temperature had direct relationship with COVID-19 incidence, while RH had inverse relationship. PM10 is positively associated with COVID-19 mortality.

Residential greenspace and COVID-19 Severity: A cohort study of 313,657 individuals in Greater Manchester, United Kingdom

BACKGROUND

Greenspaces contribute positively to mental and physical well-being, promote social cohesion, and alleviate environmental stressors, such as air pollution. Ecological studies suggest that greenspace may affect incidence and severity of Coronavirus Disease 2019 (COVID-19).

OBJECTIVE

This study examines the association between residential greenspace and COVID-19 related hospitalization and death.

METHOD

In this retrospective cohort based on patient records from the Greater Manchester Care Records, all first COVID-19 cases diagnosed between March 1, 2020, and May 31, 2022 were followed until COVID-19 related hospitalization or death within 28 days. Residential greenspace availability was assessed using the Normalized Difference Vegetation Index per lower super output area in Greater Manchester. The association of greenspace with COVID-19 hospitalization and mortality were estimated using multivariate logistic regression models after adjusting for potential individual, temporal, and spatial confounders. We explored potential effect modifications of the associations with greenspace and COVID-19 severity by age, sex, body mass index, smoking, deprivation, and certain comorbidities. Combined effects of greenspace and air pollution (NO2 and PM2.5) were investigated by mutually adjusting pairs with correlation coefficients ≤ 0·7.

RESULTS

Significant negative associations were observed between greenspace availability and COVID-19 hospitalization and mortality with odds ratios [OR] (95 % Confidence Intervals [CI]) of 0·96 (0·94-0·97) and 0·84 (0·80-0·88) (per interquartile range [IQR]), respectively. These were significantly modified by deprivation (P-value for interaction < 0.05), showing that those most deprived obtained largest benefits from greenspace. Inclusion of NO2 and PM2.5 diminished associations to null for COVID-19 hospitalization, but only reduced them slightly for mortality, where inverse associations remained.

CONCLUSION

In the Greater Manchester area, residential greenspace is associated with reduced risk of hospitalization or death in individuals with COVID-19, with deprived groups obtaining the greatest benefits. Associations were strongest for COVID-19 mortality, which were robust to inclusion of air pollutants in the models.

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.

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

Background

In the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate.

Methods

To mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021.

Results

The linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively.

Conclusion

The influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

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.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

A tale of lockdown policies on the transmission of COVID-19 within and between Chinese cities: A study based on heterogeneous treatment effect

During the early outbreak phase of COVID-19 in China, lockdowns prevailed as the only available policy tools to mitigate the spread of infection. To evaluate the impact of lockdown policies in the context of the first phase of COVID-19 pandemic, we leverage data on daily confirmed cases per million people and related characteristics of a large set of cities. The study analyzed 369 Chinese cities, among which 188 implemented lockdowns of varying severity levels from January 23 to March 31, 2020. We use nationwide Baidu Mobility data to estimate the impact of lockdown policies on mitigating COVID-19 cases through reducing human mobility. We adopt a heterogeneous treatment effect model to quantify the effect of lockdown policies on containing confirmed case counts. Our results suggest that lockdowns substantially reduced human mobility, and larger reduction in mobility occurred within-city compared to between-city. The COVID-19 daily confirmed cases per million people decreased by 9% - 9.2% for every ten-percentage point fall in within-city travel intensity in t+7 timeframe. We also find that one city's lockdowns can effectively reduce the spillover cases of the traveler's destination cities. We find no evidence that stricter lockdowns are more effective at mitigating COVID-19 risks. Our findings provide practical insights about the effectiveness of NPI during the early outbreak phase of the unprecedented pandemic.

Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi

Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014-2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure-response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of - 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction.

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

BACKGROUND

The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date.

OBJECTIVE

The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms.

METHODS

We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above.

RESULTS

Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours.

CONCLUSIONS

By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.

The impact of heat waves and cold spells on pneumonia risk: A nationwide study

Climate change affects human health and has been linked to several infectious diseases in recent year. However, there is limited assessment on the impact of heat waves and cold spells on pneumonia risk. This study aims to examine the association of heat waves and cold spells with daily pneumonia hospitalizations in 168 cities in China. Data on pneumonia hospitalizations between 2014 and 2017 were extracted from a national claim database of 280 million beneficiaries. We consider combining temperature intensity and duration to define heat waves and cold spells.This association was quantified using a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model. Exposure-response curves and potential effect modifiers were also estimated. We found that the peak relative risk (RR) of cold spells on daily hospitalizations for pneumonia was observed in relatively mild cold spells with a threshold below the 3 days at the 2nd percentile (RR = 1.69, 95% CI: 1.46-1.92). The risk of heat waves increased with the thresholds, and the greatest risk was found for extremely heatwave period of 4 days at the 98th percentile (RR = 1.69, 95% CI: 1.46-1.92). Heat waves and cold spells are more likely to adversely affect women. In conclusion, our study provided novel and strong evidence that exposure to heat waves and cold spells was associate with increased hospital visits for pneumonia, especially in females. This is the first national study in China to comprehensively evaluate the influence of heat waves and cold spells on pneumonia risk, and the findings may offer valuable insights into the impact of climate change on public health.

Causal relationship between particulate matter and COVID-19 risk: A mendelian randomization study

Background

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

Method

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

Results

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

Conclusions

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

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

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

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

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

Analysis of associations between the TLR3 SNPs rs3775291 and rs3775290 and COVID-19 in a cohort of professionals of Belém-PA, Brazil

The objective of this article was to verify associations between the SNPs rs3775291 (Cytosine [C]>Thymine [T]) and rs3775290 (C>T) of TLR3 in professionals from Health Institutions (HI) who worked during the first pandemic wave and COVID-19. A case-control study was carried out with workers from HI in Belém-PA, Brazil, divided into symptomatology groups (Asymptomatic-AS, n=91; and Symptomatic-SI, n=121), and severity groups, classified by Chest CT scan (symptomatic with lung involvement - SCP, n=34; symptomatic without lung involvement - SSP, n=8). Genotyping was performed by Sanger sequencing and statistical analysis was performed using the SPSS program. In the analysis of SNP rs3775291, the homozygous recessive genotype (T/T) was not found and the frequency of the mutant allele (T) was less than 2% in the cohort. For the rs3775290 SNP, the frequency of the mutant allele (T) was greater than 42% in the cohort. No significant associations were found for these SNPs in this cohort (N= 212 individuals). The scientific community and physicians can use these facts to find new methods of managing COVID-19.

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases

Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

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.

Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model

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

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.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study

BACKGROUND

Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs).

OBJECTIVE

To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs.

METHODS

We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model.

RESULTS

There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites.

CONCLUSION

Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

An overview of SARS-CoV-2 transmission and engineering strategies to mitigate risk

The spread of the COVID-19 pandemic has profoundly affected every aspect of our lives. To date, experts have acknowledged that airborne transmission is a key piece of the SARS-CoV-2 puzzle. Nevertheless, the exact mechanism of airborne transmission of SARS-CoV-2 remains unclear. Recent works have shown the spreading of SARS-CoV-2 through numerical modeling and experimental works, but the successful applications of engineering approaches in reducing the spread of SARS-CoV-2 are lacking. In this review, the environmental factors that influence the transmission risk of SARS-CoV-2, such as ventilation flow rates, humidity, and temperature, are discussed. Besides, additional macro and micro weather factors, regional and global transmission, and the variants of the spread of SARS-CoV-2 are also reviewed. Engineering approaches that practically reduce the risks of SARS-CoV-2 transmissions are reported. Given the complex human behavior, environmental properties, and dynamic nature of the SARS-CoV-2 virus, it is reasonable to summarize that SARS-CoV-2 may not be eradicated even with the timely implementation of interventions. Therefore, more research exploring the potential cost-effective ways to control the transmission rate of SARS-CoV-2 may be a worthwhile pursuit to moderate the current crisis.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

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

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

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

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

Differential COVID-19 preventive behaviors among Asian subgroups in the United States

BACKGROUND

Given the observed within-Asian disparity in COVID-19 incidence, we aimed to explore the differential preventive behaviors among Asian subgroups in the United States.

METHODS

Based on data from the Asian subsample (N = 982) of the 2020 Health, Ethnicity, and Pandemic survey, we estimated the weighted proportion of noncompliance with Centers for Disease Control and Prevention (CDC) guidelines on preventive behaviors and COVID-19 testing by Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other Asian). We examined these subgroup differences after adjusting for demographic factors and state-level clustering.

RESULTS

Filipinos demonstrated the lowest rate of noncompliance for mask-wearing, social distancing, and handwashing. As compared with the Filipinos, our logistic models showed that the Chinese and the 'other Asians' subgroup had significantly higher risk of noncompliance with mask-wearing, while the Japanese, the Vietnamese, and other Asians were significantly more likely to report noncompliance with social distancing.

CONCLUSIONS

The significant variation of preventive behavior across Asian subgroups signals the necessity of data disaggregation when it comes to understanding the health behavior of Asian Americans, which is critical for future pandemic preparedness. The excess behavioral risk among certain Asian subgroups (especially those 'other Asians') warrants further investigation and interventions about the driving forces behind these disparities.

Effects of fine particulate matter (PM2.5) and meteorological factors on the daily confirmed cases of COVID-19 in Bangkok during 2020-2021, Thailand

The ongoing global pandemic caused by the SARS-CoV-2 virus, known as COVID-19, has disrupted public health, businesses, and economies worldwide due to its widespread transmission. While previous research has suggested a possible link between environmental factors and increased COVID-19 cases, the evidence regarding this connection remains inconclusive. The purpose of this research is to determine whether or not there is a connection between the presence of fine particulate matter (PM2.5) and meteorological conditions and COVID-19 infection rates in Bangkok, Thailand. The study employs a statistical method called Generalized Additive Model (GAM) to find a positive and non-linear association between RH, AH, and R and the number of verified COVID-19 cases. The impacts of the seasons (especially summer) and rainfall on the trajectory of COVID-19 cases were also highlighted, with an adjusted R-square of 0.852 and a deviance explained of 85.60%, both of which were statistically significant (p < 0.05). The study results assist in preventing the future seasonal spread of COVID-19, and public health authorities may use these findings to make informed decisions and assess their policies.

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

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

Multivariate time series short term forecasting using cumulative data of coronavirus

Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model's effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

Work accidents, climate change and COVID-19

The effects brought by climate change and the pandemic upon worker health and wellbeing are varied and necessitate the identification and implementation of improved strategic interventions. This review aims, firstly, to assess how climate change affects occupational accidents, focusing on the impacts of extreme air temperatures and natural disasters; and, secondly, to analyze the role of the pandemic in this context. Our results show that the manifestations of climate change affect workers physically while on the job, psychologically, and by modifying the work environment and conditions; all these factors can cause stress, in turn increasing the risk of suffering a work accident. There is no consensus on the impact of the COVID-19 pandemic on work accidents; however, an increase in adverse mental effects on workers in contact with the public (specifically in healthcare) has been described. It has also been shown that this strain affects the risk of suffering an accident. During the pandemic, many people began to work remotely, and what initially appeared to be a provisional situation has been made permanent or semi-permanent in some positions and companies. However, we found no studies evaluating the working conditions of those who telework. In relation to the combined impact of climate change and the pandemic on occupational health, only publications focusing on the synergistic effect of heat due to the obligation to wear COVID-19-specific PPE, either outdoors or in poorly acclimatized indoor environments, were found. It is essential that preventive services establish new measures, train workers, and determine new priorities for adapting working conditions to these altered circumstances.

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.

Macro-analysis of climatic factors for COVID-19 pandemic based on Köppen-Geiger climate classification

This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors' non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere's third-grade and fourth-grade risk regions are more sensitive to precipitation.

Worldwide Correlations Support COVID-19 Seasonal Behavior and Impact of Global Change

Many viral diseases exhibit seasonal behavior and can be affected by environmental stressors. Using time-series correlation charts extrapolated from worldwide data, we provide strong support for the seasonal development of COVID-19 regardless of the immunity of the population, behavioral changes, and the periodic appearance of new variants with higher rates of infectivity and transmissibility. Statistically significant latitudinal gradients were also observed with indicators of global change. Using the Environmental Protection Index (EPI) and State of Global Air (SoGA) metrics, a bilateral analysis of environmental health and ecosystem vitality effects showed associations with COVID-19 transmission. Air quality, pollution emissions, and other indicators showed strong correlations with COVID-19 incidence and mortality. Remarkably, EPI category and performance indicators also correlated with latitude, suggesting cultural and psychological diversity in human populations not only impact wealth and happiness but also planetary health at latitudinal level. Looking forward, we conclude there will be a need to disentangle the seasonal and global change effects of COVID-19 noting that countries that go against the health of the planet affect health in general.

Does climate change affect the transmission of COVID-19? A Bayesian regression analysis

Aim

Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis.

Methods

Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software.

Results

The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus's survival and transmission.

Conclusions

Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.