title: Spatio-temporal modeling of sparse geostatistical malaria sporozoite rate data using a zero inflated binomial model.
abstract: 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.
authors: Kenya Medical Research Institute/Centers for Disease Control and Prevention (CDC), Research and Public Health Collaboration, P.O. Box 1578 Kisumu, Kenya. namek@ke.cdc.gov
authors: Nyaguara
authors: N
authors: Amek
authors: Nabie
authors: N
authors: Bayoh
authors: Mary
authors: M
authors: Hamel
authors: Kim A
authors: KA
authors: Lindblade
authors: John
authors: J
authors: Gimnig
authors: Kayla F
authors: KF
authors: Laserson
authors: Laurence
authors: L
authors: Slutsker
authors: Thomas
authors: T
authors: Smith
authors: Penelope
authors: P
authors: Vounatsou
mesh_terms: D000818:Animals; D000852:Anopheles / Q000469:parasitology*; D001499:Bayes Theorem; D006801:Humans; D007303:Insect Vectors / Q000469:parasitology*; D007630:Kenya / Q000453:epidemiology; D008288:Malaria / Q000453:epidemiology / Q000469:parasitology* / Q000635:transmission; D008432:Mathematical Computing; D015233:Models, Statistical; D010965:Plasmodium malariae / Q000302:isolation & purification*; D016012:Poisson Distribution; D011159:Population Surveillance; D012044:Regression Analysis; D012307:Risk Factors; D012621:Seasons; D062211:Spatio-Temporal Analysis; D034101:Sporozoites / Q000469:parasitology*