A Space-Time Point Process Model for Analyzing and Predicting Case Patterns of Diarrheal Disease in Northwestern Ecuador

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 natu- ral environments in the sampled region, after accounting for unmeasured residual heterogeneities. All diarrheal case events are collected from 21 sampled com- munities in Esmeraldes province in Ecuador, during seven sampling cycles from 2003-2008. The region of interest comprises 158 communities along a river basin. Prediction of case counts at unsampled communities at a future time is of inter- est along with estimation of risk-related parameters. We propose a computation- ally 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 accom- modate temporal variation within the sampled communities. Prediction of the number of cases at unsampled communities at a future time is obtained by a dis- ease mapping model conditional on the expected case counts from Stage I.

Ahn J., Johnson T., Bhavnani D., and Eisenberg J. (2014) A Space-Time Point Process Model for Analyzing and Predicting Case Patterns of Diarrheal Disease in Northwestern Ecuador. Spatial and Spatio-temporal Epidemiology, 9, 23-35.