tetano
Editor, Senior Moderator
J Expo Sci Environ Epidemiol
. 2021 Jul 1;1-8.
doi: 10.1038/s41370-021-00356-y. Online ahead of print.
Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis
Lung-Chang Chien[SUP] 1 [/SUP], L-W Antony Chen[SUP] 2 [/SUP], Ro-Ting Lin[SUP] 3 [/SUP]
Affiliations
Abstract
Background: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.
Objective: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.
Methods: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.
Results: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.
Significance: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.
Keywords: COVID-19; Lag; Precipitation; Relative humidity; Spatial; Temperature.
. 2021 Jul 1;1-8.
doi: 10.1038/s41370-021-00356-y. Online ahead of print.
Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis
Lung-Chang Chien[SUP] 1 [/SUP], L-W Antony Chen[SUP] 2 [/SUP], Ro-Ting Lin[SUP] 3 [/SUP]
Affiliations
- PMID: 34211113
- PMCID: PMC8247626
- DOI: 10.1038/s41370-021-00356-y
Abstract
Background: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.
Objective: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.
Methods: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.
Results: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.
Significance: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.
Keywords: COVID-19; Lag; Precipitation; Relative humidity; Spatial; Temperature.