Spat Spatiotemporal Epidemiol - Spatio-temporal modeling of sparse geostatistical malaria sporozoite rate data using a zero inflated binomial model.

Tópicos

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Resumo

The proportion of malaria vectors harboring the infectious stage of the parasite (the sporozoite rates) is an important component of measures of malaria transmission. Variation in time and/or space in sporozoite rates contribute substantially to spatio-temporal variation in transmission. However, because most vectors test negative for sporozoites, sporozoite rate data are sparse with large number of observed zeros across locations or over time in the case of longitudinal data. Rarely are appropriate methods and models used in analyzing such data. In this study, Bayesian zero inflated binomial (ZIB) geostatistical models were developed and compared with standard binomial analogues to analyze sporozoite data obtained from the KEMRI/CDC health and demographic surveillance system (HDSS) site in rural Western Kenya during 2002-2004. ZIB models showed a better predictive ability, identified more significant covariates and obtained narrower credible intervals for all parameters compared to standard geostatistical binomial model.

Resumo Limpo

proport malaria vector harbor infecti stage parasit sporozoit rate import compon measur malaria transmiss variat time andor space sporozoit rate contribut substanti spatiotempor variat transmiss howev vector test negat sporozoit sporozoit rate data spars larg number observ zero across locat time case longitudin data rare appropri method model use analyz data studi bayesian zero inflat binomi zib geostatist model develop compar standard binomi analogu analyz sporozoit data obtain kemricdc health demograph surveil system hdss site rural western kenya zib model show better predict abil identifi signific covari obtain narrow credibl interv paramet compar standard geostatist binomi model

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