title: Modelling heterogeneity in malaria transmission using large sparse spatio-temporal entomological data.
abstract: BACKGROUND
  Malaria transmission is measured using entomological inoculation rate (EIR), number of infective mosquito bites/person/unit time. Understanding heterogeneity of malaria transmission has been difficult due to a lack of appropriate data. A comprehensive entomological database compiled by the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) project (2001-2004) at several sites is the most suitable dataset for studying malaria transmission-mortality relations. The data are sparse and large, with small-scale spatial-temporal variation.
  
  
  OBJECTIVE
  This work demonstrates a rigorous approach for analysing large and highly variable entomological data for the study of malaria transmission heterogeneity, measured by EIR, within the Rufiji Demographic Surveillance System (DSS), MTIMBA project site in Tanzania.
  
  
  METHODS
  Bayesian geostatistical binomial and negative binomial models with zero inflation were fitted for sporozoite rates (SRs) and mosquito density, respectively. The spatial process was approximated from a subset of locations. The models were adjusted for environmental effects, seasonality and temporal correlations and assessed based on their predictive ability. EIR was calculated using model-based predictions of SR and density.
  
  
  RESULTS
  Malaria transmission was mostly influenced by rain and temperature, which significantly reduces the probability of observing zero mosquitoes. High transmission was observed at the onset of heavy rains. Transmission intensity reduced significantly during Year 2 and 3, contrary to the Year 1, pronouncing high seasonality and spatial variability. The southern part of the DSS showed high transmission throughout the years. A spatial shift of transmission intensity was observed where an increase in households with very low transmission intensity and significant reduction of locations with high transmission were observed over time. Over 68 and 85% of the locations selected for validation for SR and density, respectively, were correctly predicted within 95% credible interval indicating good performance of the models.
  
  
  CONCLUSIONS
  Methodology introduced here has the potential for efficient assessment of the contribution of malaria transmission in mortality and monitoring performance of control and intervention strategies.
authors: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; Department Biozentrum, University of Basel, Basel, Switzerland; Department of Disease Surveillance and Geographical Information Systems, National Institute for Medical Research, Dar es Salaam, Tanzania.
authors: Susan Fred
authors: SF
authors: Rumisha
authors: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; Department Biozentrum, University of Basel, Basel, Switzerland.
authors: Thomas
authors: T
authors: Smith
authors: Ifakara Health Institute, Dar es Salaam, Tanzania.
authors: Salim
authors: S
authors: Abdulla
authors: Ifakara Health Institute, Dar es Salaam, Tanzania.
authors: Honorath
authors: H
authors: Masanja
authors: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; Department Biozentrum, University of Basel, Basel, Switzerland; Penelope.Vounatsou@unibas.ch.
authors: Penelope
authors: P
authors: Vounatsou
mesh_terms: D000818:Animals; D000852:Anopheles / Q000469:parasitology; D001499:Bayes Theorem; D006801:Humans; D008288:Malaria / Q000453:epidemiology / Q000635:transmission*; D008954:Models, Biological; D011156:Population Density; D011891:Rain; D012621:Seasons; D062211:Spatio-Temporal Analysis; D013636:Tanzania / Q000453:epidemiology; D013696:Temperature
keywords: INDEPTH-MTIMBA; MCMC; approximate spatial process; malaria transmission; seasonality