A zero-inflated mixture spatially varying coefficient modeling of cholera incidences

Spatial disease modeling remains an important public health tool. For cholera, the presence of zero counts is common. The Poisson model inadequate to (1) capture over-dispersion, and (2) distinguish between excess zeros arising from non-susceptible susceptible populations. In this study, we develop zero-inflated (ZI) mixture spatially varying coefficient (SVC) models sources uncover effects precipitation temperature (LST) on cholera. We demonstrate potential using cholera data Ghana. A striking observation that outperformed ZI in terms fit. Negative Binomial (ZINB) (ZIP) model. Subject our objectives, make inferences ZINB proportion estimated with 0.41 exceeded what would have been a which 0.35. observed spatial trends LST both increasing decreasing gradients; implying use only global coefficients lead wrong inferences. conclude has epidemiological significance. Therefore, its choice over should be based concept rather than fit and, extension accommodate uncovered remarkable covariates. These findings significant implications for monitoring