Ganegoda N.C., Wijaya K.P., Amadi M., Erandi K.K.W.H., Aldila D.
Department of Mathematics, University of Sri Jayewardenepura, Nugegoda, 10250, Sri Lanka; Mathematical Institute, University of Koblenz, Koblenz, 56070, Germany; Department of Mathematics and Physics, Lappeenranta University of Technology, Lappeenranta, 53851, Finland; Department of Mathematics, University of Colombo, Colombo, 00300, Sri Lanka; Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
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 I and global Geary’s C 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. © 2021, The Author(s).
Publisher: Nature Research
Volume 11, Issue 1, Art No 11302, Page – , Page Count
Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106977820&doi=10.1038%2fs41598-021-90873-5&partnerID=40&md5=f7501da8186b72b57bd7c0c8a4a35f10
Type: All Open Access, Gold, Green
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