Non-linear link between temperature difference and COVID-19: Excluding the effect of population density

Abstract

Introduction: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density.
 Methodology: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition.
 Results: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust.
 Conclusions: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.

Effect of total population, population density and weighted population density on the spread of Covid-19 in Malaysia

“…However, some studies used non-linear models to deal with the relationship between Covid-19 and some non-demographic variables. For example, [ 52 ] identified a significant non-linear link between temperature difference and Covid-19 in the case of the United States. Another study [ 53 ] focused on 65 countries of the world and examined the nonlinear relationship between Covid-19 cases and carbon damages, managing financial development, renewable energy consumption, and innovative capability.…”

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

“…Other studies have failed to correct for the discrepancies that existed in the data reported from different regions, all of which may lead to uncertainty in the analysis results. Recently, an increasing number of studies have used non-linear time series analysis methods, such as GAM [ 5 , 23 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and DLNM [ 28 , [44] , [45] , [46] ]. However, these studies encompass analyses at the country [ 43 , 44 ], state [ 5 , 26 , 40 ] or city [ 23 , 28 , 38 , 39 , 41 , 45 , 46 ] level.…”

“…Recently, an increasing number of studies have used non-linear time series analysis methods, such as GAM [ 5 , 23 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and DLNM [ 28 , [44] , [45] , [46] ]. However, these studies encompass analyses at the country [ 43 , 44 ], state [ 5 , 26 , 40 ] or city [ 23 , 28 , 38 , 39 , 41 , 45 , 46 ] level. In studies with a country or state as the unit of analysis, the regional scope is relatively large, and acquiring data on meteorological indicators and their representativeness is prone to significant errors.…”

COVID-19’s U.S. Temperature Response Profile

“…Short panel datasets, where the stimulus of interest has a limited range (temperature, humidity, UV light, air pollution in each location), often lack the statistical power to pin down such response functions. Consequently, estimated response functions frequently have conflicting signs on key variables or are fragile in the sense that statistical significance is gained or lost when time trends or demographic variables are added to models (Briz-Redón and Serrano-Aroca 2020 ; Ding et al 2021 ; Jamshidi et al 2020 ; Jüni et al 2020 ; Kerr et al 2021 ; Mecenas et al 2020 ; Pedrosa 2020 ; Xu et al 2020 ).…”