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Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings

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  • Peter J. Diggle
  • Emanuele Giorgi

Abstract

In low-resource settings, prevalence mapping relies on empirical prevalence data from a finite, often spatially sparse, set of surveys of communities within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model with binomial error distribution, logistic link, and a combination of explanatory variables and a Gaussian spatial stochastic process in the linear predictor. In this article, we first review statistical methods and software associated with this standard model, then consider several methodological extensions whose development has been motivated by the requirements of specific applications. These include: methods for combining randomized survey data with data from nonrandomized, and therefore potentially biased, surveys; spatio-temporal extensions; and spatially structured zero-inflation. Throughout, we illustrate the methods with disease mapping applications that have arisen through our involvement with a range of African public health programs.

Suggested Citation

  • Peter J. Diggle & Emanuele Giorgi, 2016. "Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1096-1120, July.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:1096-1120
    DOI: 10.1080/01621459.2015.1123158
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    1. Vahid Tadayon & Mohammad Mehdi Saber, 2023. "A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 59-73, March.
    2. Julius Ssempiira & Betty Nambuusi & John Kissa & Bosco Agaba & Fredrick Makumbi & Simon Kasasa & Penelope Vounatsou, 2017. "Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-20, April.
    3. Benjamin F. Arnold & Francois Rerolle & Christine Tedijanto & Sammy M. Njenga & Mahbubur Rahman & Ayse Ercumen & Andrew Mertens & Amy J. Pickering & Audrie Lin & Charles D. Arnold & Kishor Das & Chris, 2024. "Geographic pair matching in large-scale cluster randomized trials," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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