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.
REFERENCES
B. D. Bilton, G. B. Zibari, R. W. McMillan, D. F. Aultman, G. Dunn, and J. C. McDonald, ‘‘Aggressive surgical management of necrotizing fasciitis serves to decrease mortality: A retrospective study,’’ Am. Surg. 64, 397–401 (1998).
C. W. S. Chen and S. Lee, ‘‘Generalized Poisson autoregressive models for time series of counts,’’ Comput. Stat. Data Anal. 99, 51–67 (2016).
C. W. S. Chen and S. Lee, ‘‘Bayesian causality test for integer-valued time series models with applications to climate and crime data,’’ J. R. Stat. Soc., Ser. C 66, 797–814 (2017).
D. Spiegelhalter, N. G. Best, B. P. Carlin, and A. van der Linde, ‘‘Bayesian measures of model complexity and fit (with discussion),’’ J. R. Stat. Soc., Ser. B 64, 583–616 (2002).
F. Zhu, ‘‘Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models,’’ J. Math. Anal. Appl. 389, 58–71 (2012).
K. Fokianos, ‘‘Count time series models,’’ in Handbook of Statistics 30: Time Series Analysis: Methods and Applications (2012), pp. 315–347.
L. Subissi, A. Sousa, I. Ba, and A. Perrocheau, ‘‘A large epidemic of a necrotic skin infection in the Democratic Republic of São Tomé and Principe: An epidemiological study,’’ Int. J. Infect. Dis. 110, S69–S76 (2021).
P. Tantirat, T. Rattanathumsakul, H. Praekunatham, K. Pachanee, and R. Suphanchaimat, ‘‘Epidemiological situation of necrotizing fasciitis and factors in Thailand and factors associated with its morbidity and mortality, 2014–2018,’’ Risk Manag. Healthc. Policy 13, 1613–1624 (2020).
P. C. Consul and G. C. Jain, ‘‘A generalization of the Poisson distribution,’’ Technometrics 15, 791–799 (1973).
R. Ferland, A. Latour, and D. Oraichi, ‘‘Integer-valued GARCH processes,’’ J. Time Ser. Anal. 27, 923–942 (2006).
R. Puvanendran, J. C. Huey, and S. Pasupathy, ‘‘Necrotizing fasciitis,’’ Can. Fam. Physician 55, 981–987 (2009).
R. C. Jung, M. Kukuk, and R. Liesenfeld, ‘‘Time series of count data: Modelling and estimation and diagnostics,’’ Comput. Stat. Data Anal. 51, 2350–2364 (2006).
S. Hasham, P. Matteucci, P. R. Stanley, and N. B. Hart, ‘‘Necrotising fasciitis,’’ Brit. Med. J. 330, 830–833 (2005).
Funding
Khemmanant Khamthong’s research is funded by Thailand Science Research and Innovation (TSRI).
Author information
Authors and Affiliations
Corresponding authors
Additional information
(Submitted by A. I. Volodin)
Sujitta Suraphee is a corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1995080223090159