TY - JOUR
T1 - Does climate help modeling COVID-19 risk and to what extent?
AU - Scabbia, Giovanni
AU - Sanfilippo, Antonio
AU - Mazzoni, Annamaria
AU - Bachour, Dunia
AU - Perez-Astudillo, Daniel
AU - Bermudez, Veronica
AU - Wey, Etienne
AU - Marchand-Lasserre, Mathilde
AU - Saboret, Laurent
N1 - Publisher Copyright:
© 2022 Scabbia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/9
Y1 - 2022/9
N2 - A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.
AB - A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.
UR - http://www.scopus.com/inward/record.url?scp=85137745897&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0273078
DO - 10.1371/journal.pone.0273078
M3 - Article
C2 - 36070304
AN - SCOPUS:85137745897
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 9 September
M1 - e0273078
ER -