Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 05 2021
28 05 2021
Historique:
received:
18
01
2021
accepted:
16
05
2021
entrez:
29
5
2021
pubmed:
30
5
2021
medline:
16
6
2021
Statut:
epublish
Résumé
COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran's [Formula: see text] and global Geary's [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran's scatter plot, where states' dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.
Identifiants
pubmed: 34050241
doi: 10.1038/s41598-021-90873-5
pii: 10.1038/s41598-021-90873-5
pmc: PMC8163835
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
11302Subventions
Organisme : Kementerian Riset Teknologi Dan Pendidikan Tinggi Republik Indonesia (Ministry of Research, Technology and Higher Education)
ID : PUPT Research Grant Scheme, 2021
Commentaires et corrections
Type : ErratumIn
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