Impact of meteorological variations and preventive policy measures on the spread and mortality of COVID-19 in European countries

The present study examined the impact of meteorological variations and preventive policy measures on the spread and mortality of COVID-19 in ten European countries. The results from negative binomial regression indicated that the average period of high death count is characterised with low average temperature and confirmed that respiratory infections are enhanced during cold and low humidity conditions. The role of suggested and implemented preventive measures by the respective governments showed the effectiveness of all the measures taken to reduce the probability of mortality. Nonetheless, the effect of educational institutes' closure remained significantly higher than other preventive measures. The study suggested less exposure to low temperature and smart lockdowns to avoid deaths during the predicted waves of COVID-19.

The occurrence of COVID-19 is associated with air quality and relative humidity.

The association between meteorological factors and COVID-19 is important for the prevention and control of COVID-19. However, similar studies are relatively rare in China. This study aims to investigate the association between COVID-19 and meteorological factors, such as average temperature, relative humidity, and air quality index (AQI), and average wind speed. We collected the daily confirmed cases of COVID-19 and meteorological factors in Shanghai China from January 10, 2020 to March 31, 2020. A generalized additive model was fitted to quantify the associations between meteorological factors and COVID-19 during the study period. A negative association between average temperature and daily confirmed cases of COVID-19 was found on lag 13 days. In addition, we observed a significant positive correlation between meteorological factors (AQI, relative humidity) and daily confirmed cases of COVID-19. A 10 increase in AQI (lag1/7/8/9/10 days) was correlated with a 4.2%-9.0% increase in the daily confirmed cases of COVID-19. A 1% increase in relative humidity (lag1/4/7/8/9/10 days) was correlated with 1.7%-3.7% increase in the daily confirmed cases of COVID-19. However, the associations between average wind speed and the daily confirmed cases of COVID-19 is complex in different lag days. In summary, meteorological factors could affect the occurrence of COVID-19. Reducing the effects of meteorological factors on COVID-19 may be an important public health action for the prevention and control of COVID-19.

Lag effect of temperature on the incidence of COVID-19 in Hunan province

Objective To explore the lag effect of daily average temperature on the incidence of coronavirus disease 2019 (COVID-19) in Hunan Province and to provide scientific evidences for effective prevention of COVID-19.  Methods  The meteorological factors, the air quality factors and the data conincidence of COVID-19 reported in Hunan Province during January 21, 2020 to March 2, 2020 were collected. Spearman correlation and distributed lag non-linear model analysis were performed.  Results  A total of 1 018 COVID-19 cases were reported in Hunan Province. The distribution lag non-linear model results showed that the influence of daily average temperature on the incidence of COVID-19 presented a nonlinear relationship. The cumulative relative incidence risk of COVID-19 decreased with the increase of daily average temperature, and the lowest temperature risk of the patients was 0 ℃. Both cold temperature and hot temperature increased incidence risk of COVID-19. It was indicated that the hot effects were immediate, however, the cold effects with obvious lag effect persisted up to 12 days. The highest relative risk of COVID-19 incidence was associated with lag 8-day daily average temperature of -5 ℃(RR=2.20, 95% CI=1.16-4.19). The influence of high temperature(10 ℃) was more significant than that of low temperature(6 ℃).  Conclusion  The daily average temperature, especially cold or hot temperature, was an important influencing factor of the incidence of COVID-19 in Hunan Province, which had lag influence on the incidence of COVID-19. We suggested that some related preventive measures should be adopted to protect vulnerable population and severe patients to reduce the incidence risk. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Association between temperature and COVID-19 transmission in 153 countries.

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

Statistical study on the impact of different meteorological changes on the spread of COVID-19 pandemic in Egypt and its latitude.

Abstract: More than 1 million illnesses and 70,000 deaths were reported due to novel COVID-19 by the end of the first quarter of 2020. In April 2020, the World Health Organization declared COVID-19 a pandemic. The striking resemblance between COVID-19 and its forerunners SARS and MERS, as well as earlier findings on the impact of meteorological conditions on the spread of SARS and MERS, prompted researchers to investigate the relationship between meteorological conditions and the spread of COVID-19. In this work, we statistically studied the effect of different meteorological parameters such as average temperature, humidity, dew point, and wind speed on the spread of COVID-19 pandemic in Egypt and its latitude (Algeria, Egypt, Iran, Saudi Arabia, Turkey). Our findings revealed that there is a correlation between several meteorological parameters and the spread of COVID-19, but that, contrary to popular belief, the virus does not disappear when the temperature rises. Our theory is that either the virus became active in Egypt and its latitude as the temperature rose, or the humidity became unstable when the temperature rose during the summer season. A log-linear quasi-Poisson regression model was used to estimate the relationship between the studied metrological parameters and the spread of COVID-19. The findings of the study will have ramifications for future control and prevention efforts in Egypt and its latitude.

Correlations between Meteorological Indicators, Air Quality and the COVID-19 Pandemic in 12 Cities across China.

BACKGROUND: COVID-19 is a global pandemic. The purpose of this study is to explore correlations between the novel coronavirus (COVID-19) and meteorological indicators from cities across China. METHODS: We collected daily data of the cumulative number of infected, recovered and death cases, and the meteorological indicators including average temperature, wind speed, relative humidity, precipitation and air quality index (AQI) from 12 cities in China during the period of Jan 23 to Feb 22, 2020. Correlation tests were chosen for data analysis. RESULTS: The average temperature and AQI showed significant association with the mortality rate of COVID-19. The mortality rate was not correlated with wind speed, relative humidity or precipitation. Meanwhile, higher average temperatures and more precipitation were beneficial for the recovery rate of COVID-19, but the recovery rate was not correlated with wind speed, relative humidity or AQI. CONCLUSIONS: Our study provides a new basis for correlations between COVID-19, meteorological indicators and air quality index, which can help authorities to combat COVID-19.