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Bayesian Zero-Inflated Generalized Poisson (t) Spatio-Temporal Modeling for Analyzing the DHF Endemic Area

Mukhsar1, Bahriddin Apabihi1, Sitti Wirdhana Ahmad Bakkareng2, Asrul Sani1*, Edi Cahyono1
Bayesian modeling for count data is to analyze an epidemiological risk as spatially and temporally varying, e.g. Dengue Hemorrhagic Fever (DHF). The DHF data with too many zero are commonly an over dispersion. Bayesian zero-inflated generalized Poisson or BZIGP () spatio-temporal. This model was developed from our previous work, called BZIP S-T. Both models is verified by using the DHF monthly data in 10 districts of Kendari city during periode 2013-2015. MCMC method is used to estimate the parameters of both models. Both models indicate the rainfall and population density are statistically significant to influence the fluctuations of DHF cases. But the BZIGP () S-T is smallest deviance and the best performance model. Puwatu and Kadia districts are an endemic area. Keywords: Bayesian spatio-temporal; DHF; Generalized Poisson; over dispersion; zero inflated

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Diterbitkan pada: • Jurnal: Global Journal of Pure and Applied Mathematics (GJPAM) • ISSN : 0973-1768 (print), 0973-9750 (online) • Vol. 13, Number 3 (2017), pp. 1069-1081 • Terindeks pada: Scimago SJR (Q4, H-Index 5), the Mathematical Reviews, MathSciNet, Zentralblatt MATH and EBSCO Databases, Index Copernicus

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