Elsevier

Spatial Statistics

Volume 49, June 2022, 100542
Spatial Statistics

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

https://doi.org/10.1016/j.spasta.2021.100542Get rights and content

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.

Keywords

COM-Poisson model
Spatial temporal effect
Bayesian inference
Model comparison

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