Spat Spatiotemporal Epidemiol - A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador.

Tópicos

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Resumo

We consider modeling case-patterns under a complex spatial and longitudinal sampling design as conducted via a serial case-control study of diarrheal disease in northwestern Ecuador. We build a two-stage space-time model to understand the role of spatially and temporally referenced covariates that reflect social and natural environments in the sampled region, after accounting for unmeasured residual heterogeneities. All diarrheal case events are collected from 21 sampled communities in Esmeraldes province in Ecuador, during seven sampling cycles from 2003 to 2008. The region of interest comprises 158 communities along a river basin. Prediction of case counts at unsampled communities at a future time is of interest along with estimation of risk-related parameters. We propose a computationally feasible two-stage Bayesian approach to estimate the risk-related parameters and conduct predictive inference. We first apply the log Gaussian Cox process (LGCP), commonly used to model spatial clustering of point patterns, to accommodate temporal variation within the sampled communities. Prediction of the number of cases at unsampled communities at a future time is obtained by a disease mapping model conditional on the expected case counts from Stage I.

Resumo Limpo

consid model casepattern complex spatial longitudin sampl design conduct via serial casecontrol studi diarrheal diseas northwestern ecuador build twostag spacetim model understand role spatial tempor referenc covari reflect social natur environ sampl region account unmeasur residu heterogen diarrheal case event collect sampl communiti esmerald provinc ecuador seven sampl cycl region interest compris communiti along river basin predict case count unsampl communiti futur time interest along estim riskrel paramet propos comput feasibl twostag bayesian approach estim riskrel paramet conduct predict infer first appli log gaussian cox process lgcp common use model spatial cluster point pattern accommod tempor variat within sampl communiti predict number case unsampl communiti futur time obtain diseas map model condit expect case count stage

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