Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

Abstract

The main objective of this study is to investigate the relationship between the COVID-19 and the weather factors of the most populated and industrialised countries in Europe and propose the best mathematical model to forecast the daily number of COVID-19 cases. To find the relationship between the COVID-19 and the weather factors of absolute humidity and temperature in Spain, France, Italy, Germany, and the United Kingdom, we conducted a Poisson analysis. We also used the General Linear Neural Network (GRNN) model to forecast the trend and number of daily COVID-19 cases in these European countries. The results reveal a statistically significant negative relationship between the number of COVID-19 infections and weather factors of temperature & absolute humidity. Furthermore, the results show a stronger negative relationship between COVID-19 and absolute humidity than temperature. In our proposed GRNN method, we find better compatibility for the COVID-19 cases in Italy relative to the other European countries in this study.

COVID-19 transmission in Africa: estimating the role of meteorological factors

“…Supposing unchanged daily average temperature, a 1% relative humidity increase is associated with 0.006, 0.001, 0.011 and -0.013 reduction in the log of daily new confirmed cases in South Africa, Morocco, Ethiopia and Tunisia, respectively. These findings are similar to those in Spain ( Gharoie Ahangar et al, 2020 ), the world ( Zhang et al, 2021 ), China ( Oliveiros et al, 2020 ), Australia ( Ward et al, 2020 ) and in Italy ( Gharoie Ahangar et al, 2020 ).…”

“…Given that the relative humidity is maintained constant, a 1 °C increase in the average temperature is linked to a 0.110 decrease in the log number of new confirmed cases in Tunisia. These findings are comparable to those in Africa ( Adekunle et al, 2020 ), Saudi Arabia ( Alkhowailed et al, 2020 ), the United States ( Chien and Chen, 2020 ), Spain ( Gharoie Ahangar et al, 2020 ), and China ( Liu et al, 2020 ). As a result, making a specific derivation about the effect of temperature on the COVID-19 spread could be difficult.…”

“…In Ethiopia, a 1 °C increase in the average temperature is correlated with a 0.110 increase in the log number of new confirmed cases, assuming that the relative humidity remains constant. In France ( Gharoie Ahangar et al, 2020 ), Norway ( Menebo, 2020 ), India ( Meraj et al, 2021 ), Singapore ( Pani et al, 2020 ), and China ( Xie and Zhu, 2020 ) a parallel findings were pointed. In Morocco and Tunisia, on the other hand, the daily average temperature has a negative impact on the daily new confirmed cases of COVID-19.…”

“…Thus, the Poisson regression approach is often used in COVID-19 investigations. In some European countries, this approach is used to investigate the association between daily COVID-19 infections and climatic parameters such as temperature and absolute humidity ( Gharoie Ahangar et al, 2020 ).…”

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID‐19) in a hot, arid climate

“…Studies conducted in 2020 investigated the correlation between the weather or climate variables (e.g., temperature, humidity, wind speed, visibility, rain, and solar radiation) and the number of COVID‐19 cases. Several of these studies have concentrated on different cities and countries around the world, for example, the United States and Europe (Bukhari & Jameel, 2020 ; Gharoie Ahangar et al, 2020 ), Africa (Adekunle et al, 2020 ), Russia (Lasisi and Eluwole, 2021 ), Wuhan, China (Ma et al, 2020 ), 122 cities in China (Xie & Zhu, 2020 ), India (Awasthi et al, 2020 ; Borah et al, 2020 ; Gupta, Pradhan, et al, 2020 ), Jakarta, Indonesia (Tosepu et al, 2020 ), Pakistan (Aslam et al, 2020 ), Brazil (Aule et al, 2020 ; Pequeno et al, 2020 ), Iran (Ahmadi et al, 2020 ). Preliminary studies that found no relationship between COVID‐19 transmission and atmospheric variables (e.g., temperature) were performed by Xie and Zhu ( 2020 ), Gupta, Raghuwanshi, et al ( 2020 ), and Yao et al ( 2020 ).…”