Abstract:
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Many diseases arise due to exposure to one of multiple pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on the probabilities of disease incidence given pathogen type, and on severity given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological characteristics.
The responses of interest are not directly observed, we obtain estimates of these quantities, with an associated variance. We then model these summary statistics using a Bayesian spline model with a fast implementation over time. We analyze data on hand, foot and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 and Coxackie A16. We find that disease incidence for the two main pathogens follow similar associations with both time and meteorological variables.
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