Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model.
Coronavirus
Count regression models
Epidemiology
Generalized waring regression
Journal
Physica A
ISSN: 0378-4371
Titre abrégé: Physica A
Pays: Netherlands
ID NLM: 9890571
Informations de publication
Date de publication:
01 Jul 2022
01 Jul 2022
Historique:
received:
29
11
2021
revised:
08
03
2022
pubmed:
23
3
2022
medline:
23
3
2022
entrez:
22
3
2022
Statut:
ppublish
Résumé
The rapid spread of the COVID-19 pandemic has triggered substantial economic and social disruptions worldwide. The number of infection-induced deaths in Senegal in particular and West Africa in general are minimal when compared with the rest of the world. We use count regression (statistical) models such as the generalized Waring regression model to forecast the daily confirmed COVID-19 cases in Senegal. The generalized Waring regression model has an advantage over other models such as the negative binomial regression model because it considers factors that cannot be observed or measured, but that are known to affect the number of daily COVID-19 cases. Results from this study reveal that the generalized Waring regression model fits the data better than most of the usual count regression models, and could better explain some of the intrinsic characteristics of the disease dynamics.
Identifiants
pubmed: 35313718
doi: 10.1016/j.physa.2022.127245
pii: S0378-4371(22)00221-7
pmc: PMC8928709
doi:
Types de publication
Journal Article
Langues
eng
Pagination
127245Informations de copyright
© 2022 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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