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

The influence of weather and urban environment characteristics on upper respiratory tract infections: a systematic review

Background

Weather can independently affect the occurrence of respiratory tract infections (RTIs) in urban areas. Built environments of cities could further modify exposure to weather and consequently the risk of RTIs, but their combined effects on infections are not known.

Objectives

Our aim was to synthesize evidence of the influence of weather on RTIs in urban areas and to examine whether urban built environments are associated with both weather and RTIs.

Methods

A systematic search of Scopus, PubMed, and Web of Science databases was conducted on 9th of August 2022 following PRISMA guidelines. Studies were included in the review based on predefined criteria by screening 5,789 articles and reviewing reference lists of relevant studies. The quality of the studies was assessed using the AXIS appraisal tool, and the results analyzed by narrative synthesis.

Results

Twenty-one eligible studies focusing on COVID-19 and influenza transmissions, were included in the review. All studies were register based ecological studies by design. Low temperature (11/19 studies) was most often associated with increased risk of RTI. Humidity showed either negative (5/14 studies), positive (3/14 studies) or no (6/14 studies) relation with RTIs. The association between wind and solar radiation on infections was inconclusive. Population density was positively associated with RTIs (14/15 studies).

Conclusions

Our review shows that exposure to low temperature increases the occurrence of RTIs in urban areas, and where also high population density increases the infection risk. The study highlights the need to further assess the relationship between built environment characteristics, weather, and RTIs.

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.

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.

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.

Analyzing the impact of vaccinations and weather factors on the COVID-19 pandemic

OBJECTIVES

A world-wide immunization project was launched at the peak of COVID-19 pandemic to contain and minimize the adverse effects of SARS-CoV-2 virus. We carried out a series of statistical analyses in this paper to determine, confirm and quantify the impact of the vaccinations on COVID-19 cases and mortalities, amidst critical confounding factors - temperature and solar irradiance.

METHODS

The experiments in this paper were carried out on the world data, data from 21 countries and the five major continents. The significance of the 2020 - 2022 vaccinations on the COVID-19 cases and mortalities response data were evaluated via Hypotheses' tests. Correlation coefficient analyses were carried out to determine the extent of the relationship between vaccination coverage and corresponding COVID-19 mortalities data. The impact of vaccination was quantified. The effects of the weather factors - temperature and solar irradiance, on COVID-19 cases and mortalities data were analyzed.

RESULTS

The series of hypotheses tests carried out reveal that vaccinations did not affect cases; however, vaccinations significantly impacted the mean daily mortalities in all five major continents and globally. The correlation coefficient analysis results show vaccination coverage to be highly and negatively correlated with daily mortalities in the world - the five major continents and most of the countries studied in this work. The percentage reduction in mortalities as a result of wider vaccination coverage was indeed significant. Temperature and solar irradiance impacted daily COVID-19 cases and mortalities data during the vaccination and post vaccination periods.

CONCLUSION

Results show that the world-wide Vaccination against COVID-19 project had a significant impact in reducing mortalities and minimizing the adverse effects due to COVID-19 globally, in all five (5) major continents of the world and the countries studied in this work, however, temperature and solar irradiance still had effects on COVID-19 response in the vaccination eras.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

BACKGROUND AND AIM

The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally.

METHODS

We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone.

RESULTS

We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries.

CONCLUSION

Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Using a novel two-stage strategy to characterize the spatial distribution of associations between temperature and COVID-19: A case study in the continental United States

BACKGROUND

Numerous studies have studied the association between daily average temperature (DAT) and daily COVID-19 confirmed cases, which show considerable heterogeneity, even opposite results, among different regions. Such heterogeneity suggests that characterizing the association on a large area scale would ignore the local variation, even obtain false results in some local regions. So, characterizing the spatial distribution of heterogeneous DAT-COVID-19 associations and exploring the causes plays an important role on making temperature-related region-specific intervention measures and early-warning systems.

METHODS

Aiming to characterize the spatial distribution of associations between DAT and COVID-19 confirmed cases in the continental United States, we proposed a novel two-stage strategy. In the first stage, we used the common stratified distributed lag nonlinear model to obtain the rough state-specific associations. In the second stage, conditional autoregression was used to spatially smooth the rough estimations. Furtherly, based on the idea, two modified strategies were used to investigate the time-varying associations and the modification effects derived from the vaccination campaign.

RESULTS

Around one-third of states exhibit no significant association between DAT and daily confirmed COVID-19 cases. Most of the remaining states present a low risk at low DAT and a high risk at high DAT, but several states present opposite associations. The average association curve presents a 'S' shape with positive association between -8 - 18 °C and keeping flat out of the range. An increased vaccination coverage rate will increase the risk when DAT < 12 °C, but slightly affect the risk when DAT > 12 °C.

CONCLUSION

A considerable spatial heterogeneity of DAT-COVID-19 associations exists in America and the average association curve presents a 'S' shape. The vaccination campaign significantly modifies the association when DAT is low, but only make a slight modification when DAT is high.

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

Objectives

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

Methods

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

Results

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

Conclusions

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

Sports Participation and Anti-Epidemic: Empirical Evidence on the Influence of Regular Physical Activity on the COVID-19 Pandemic in Mainland China

This study aims to investigate the effects and influencing mechanisms of regular physical activity (RPA) on the COVID-19 pandemic. Daily data from 279 prefecture-level cities in mainland China were collected from 1 January to 17 March 2020. A two-way fixed-effects model was used to identify the causal relationship between physical activity and COVID-19, while also considering factors such as patterns of human behavior and socioeconomic conditions. The instrumental variable (IV) approach was applied to address potential endogeneity issues for a more accurate causal identification, and the mediating effect model was applied to examine the mechanisms of the influence of physical activity on the epidemic. We found that regular physical activity significantly improves individual immunity, which, in turn, leads to a reduction in the probability of being infected with COVID-19. Furthermore, we investigated the heterogeneity of the influence, finding that the negative impact of physical activity on the pandemic is more pronounced in the absence of adequate medical resources, strong awareness of prevention among residents, and fully implemented public health measures. Our results provide empirical evidence for the mechanisms of influence of physical activity on the pandemic. We would suggest that not only should physical activity be actively practiced during the pandemic, but also long-term regular exercise habits should be consciously cultivated to improve the ability of the individual immune system to better cope with sudden outbreaks of emerging infectious diseases.

Are Sports Champions Also Anti-Epidemic Heroes? Quantitative Research on the Influence of Sports Champions' Demonstration Effect on the COVID-19 Epidemic in China

What kind of role do sports champions play in the COVID-19 epidemic? Do they contribute to the mitigation of the epidemic by some pathway? In this paper, we empirically explore the influence and mechanism of the demonstration effect of sports champions upon the COVID-19 epidemic using COVID-19-related dataset of prefecture-level cities in China from 1 January 2020 to 17 March 2020. The two-way fixed effect model of econometrics is applied to estimate the result, the instrumental variable approach is adopted to address potential endogeneity issues, and socio-economic factors including public health measures, residents' self-protection awareness, effective distance from Wuhan are also taken into consideration. The results show that the demonstration effect of champions in major sporting events increases the participation in physical exercise, which in turn reduces the possibility of being infected with the epidemic. An increase of one gold medal results in a 0.93% increase in the sports population, then leads to a 3.58% decrease in the cumulative case growth rate (p < 0.01). Further, we find that the effect is greater in regions with developed economies and abundant sports resources. Interestingly, it is greater in regions with less attention to sports, which again confirms the role of the demonstration effect.