A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China

Abstract

A mechanism-based parameterisation scheme was developed for transmission rate. • Scale of population migration from Wuhan was incorporated. • Population density had a non-linear (linearity index, 0.9) impact on disease spread. • Transmission rate shows a negative and exponential temperature dependence. • COVID-19 mitigation in densely populated and cold regions will be a great challenge.

The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management

“…It should, however, be mentioned that some studies did not find significant relationship between humidity and transmission of the virus. For instance, after controlling for the effect of population density, the effect of humidity was not significant in a study of 20 Chinese municipalities ( Lin et al, 2020 ). Future research may reveal further insights into the relationship between humidity and COVID-19 transmission.…”

“…Some studies from China, Brazil, and Italy argue that lower temperatures are more favorable for the transmission of the virus. For instance, using the number of confirmed cases obtained from the data repository at Johns Hopkins University Center for Systems Science and Engineering (CSSE) and meteorological data obtained from the ground-based monitoring network of the World Meteorological Organization (WMO) global telecommunications system, Lin et al (2020) found a negative and exponential relationship between transmission rate and temperature in a study of 20 provinces/municipalities across China. In areas below 0 °C, rapidly increased rates of transmission were observed ( Lin et al, 2020 ).…”

“…For instance, using the number of confirmed cases obtained from the data repository at Johns Hopkins University Center for Systems Science and Engineering (CSSE) and meteorological data obtained from the ground-based monitoring network of the World Meteorological Organization (WMO) global telecommunications system, Lin et al (2020) found a negative and exponential relationship between transmission rate and temperature in a study of 20 provinces/municipalities across China. In areas below 0 °C, rapidly increased rates of transmission were observed ( Lin et al, 2020 ). Similar results about the higher rates of infection in lower temperatures have been reported in other studies of provincial capital regions and provincial capital cities of China ( Liu et al, 2020 ; Shi et al, 2020 ; Qi et al, 2020 ).…”

“…As for other environmental parameters such as wind speed, existing evidence is limited and inconclusive. While analysis of data from nine Turkish cities, based on data obtained from the Ministry of Health and the Weather Underground website, showed that wind speed is positively associated with COVID-19 infections ( Şahin, 2020 ), in a study of 20 Chinese cities no significant relationship was observed ( Lin et al, 2020 ), and in Italian cities, correlations were negative ( Coccia, 2020a ; Zoran et al, 2020b ). In the latter, low wind speed was often coupled with high levels of air pollution (PM 10 and O 3 ).…”

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

“…In addition, many studies have been conducted to examine the social, psychological and economic effects of the COVID-19 outbreak and the changes it causes [8] , [9] , [10] , [11] , [12] , [13] . Epidemiological, statistical and mathematical models have also been introduced to predict the distribution, to observe the changes depending on meteorological conditions, and to examine the structure of this epidemic which affects all countries globally [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Besides, the performance of machine learning approaches for the diagnosis and treatment of the disease was also studied [22] , [23] , [24] , [25] , [26] , [27] , [28] .…”