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Estimation of COVID-19 mortality in the United States using Spatio-temporal Conway Maxwell Poisson model.
Li, Xiaomeng; Dey, Dipak K.
  • Li X; Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269-4120, United States of America.
  • Dey DK; Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269-4120, United States of America.
Spat Stat ; 49: 100542, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1458685
ABSTRACT
Spatio-temporal Poisson models are commonly used for disease mapping. However, after incorporating the spatial and temporal variation, the data do not necessarily have equal mean and variance, suggesting either over- or under-dispersion. In this paper, we propose the Spatio-temporal Conway Maxwell Poisson model. The advantage of Conway Maxwell Poisson distribution is its ability to handle both under- and over-dispersion through controlling one special parameter in the distribution, which makes it more flexible than Poisson distribution. We consider data from the pandemic caused by the SARS-CoV-2 virus in 2019 (COVID-19) that has threatened people all over the world. Understanding the spatio-temporal pattern of the disease is of great importance. We apply a spatio-temporal Conway Maxwell Poisson model to data on the COVID-19 deaths and find that this model achieves better performance than commonly used spatio-temporal Poisson model.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2021.100542

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2021.100542