Abstract
This paper models the INGARCHX type with a generalized Poisson distribution relationship for three meteorological variables consisting of relative humidity, maximum temperature, and seasonality. The key properties of the proposed model can describe overdispersion and lagged dependencies. For the model parameters and predictions, we use the Bayesian Markov chain Monte Carlo (MCMC) method. To compare different models, we apply a Bayesian information criterion. As shown by the simulations and the analysis of the sample data of the weekly Necrotizing Fasciitis (NF) cases. Then, we provide a one-week prediction to explain the incidence of weekly NF cases occurring in both Mahasarakham and Roi-Et hospitals, which also leads to a faster treatment plan.
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Funding
Khemmanant Khamthong’s research is funded by Thailand Science Research and Innovation (TSRI).
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(Submitted by A. I. Volodin)
Sujitta Suraphee is a corresponding author
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Khamthong, K., Srisarakham, N. & Suraphee, S. Bayesian Time Series Modeling of Necrotizing Fasciitis Count in Mahasarakham and Roi-Et Hospitals. Lobachevskii J Math 44, 3718–3728 (2023). https://doi.org/10.1134/S1995080223090159
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DOI: https://doi.org/10.1134/S1995080223090159