Estimation of COVID-19 mortality in the United States using Spatio-temporal Conway Maxwell Poisson model

• Published in: Spatial statistics Vol. 49; p. 100542
• Main Authors: Li, Xiaomeng, Dey, Dipak K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2022; Published by Elsevier B.V
• Subjects: Bayesian inference; COM-Poisson model; Model comparison; Spatial temporal effect; COM-Poisson model; Model comparison; Bayesian inference; Spatial temporal effect
• ISSN: 2211-6753; 2211-6753
• DOI: 10.1016/j.spasta.2021.100542

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.