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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. Jennifer C Stevenson & Gillian H Stresman & Caroline W Gitonga & Jonathan Gillig & Chrispin Owaga & Elizabeth Marube & Wycliffe Odongo & Albert Okoth & Pauline China & Robin Oriango & Simon J Brooker , 2013. "Reliability of School Surveys in Estimating Geographic Variation in Malaria Transmission in the Western Kenyan Highlands," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. Peter Diggle & Rana Moyeed & Barry Rowlingson & Madeleine Thomson, 2002. "Childhood malaria in the Gambia: a case‐study in model‐based geostatistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 493-506, October.
    4. Joe, Harry, 2008. "Accuracy of Laplace approximation for discrete response mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5066-5074, August.
    5. Simon I Hay & Carlos A Guerra & Peter W Gething & Anand P Patil & Andrew J Tatem & Abdisalan M Noor & Caroline W Kabaria & Bui H Manh & Iqbal R F Elyazar & Simon Brooker & David L Smith & Rana A Moyee, 2009. "A World Malaria Map: Plasmodium falciparum Endemicity in 2007," PLOS Medicine, Public Library of Science, vol. 6(3), pages 1-17, March.
    6. Rachel L Pullan & Peter W Gething & Jennifer L Smith & Charles S Mwandawiro & Hugh J W Sturrock & Caroline W Gitonga & Simon I Hay & Simon Brooker, 2011. "Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 5(2), pages 1-11, February.
    7. Jen Claridge & Peter Diggle & Catherine M. McCann & Grace Mulcahy & Rob Flynn & Jim McNair & Sam Strain & Michael Welsh & Matthew Baylis & Diana J.L. Williams, 2012. "Fasciola hepatica is associated with the failure to detect bovine tuberculosis in dairy cattle," Nature Communications, Nature, vol. 3(1), pages 1-8, January.
    8. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    9. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    10. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    11. Julian Besag & Debashis Mondal, 2005. "First-order intrinsic autoregressions and the de Wijs process," Biometrika, Biometrika Trust, vol. 92(4), pages 909-920, December.
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    Cited by:

    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. 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.
    3. 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.

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