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Estimating small area population from health intervention campaign surveys and partially observed settlement data

Author

Listed:
  • Chibuzor Christopher Nnanatu

    (University of Southampton
    Department of Statistics, Nnamdi Azikiwe University)

  • Amy Bonnie

    (University of Southampton)

  • Josiah Joseph

    (National Statistical Office)

  • Ortis Yankey

    (University of Southampton)

  • Duygu Cihan

    (University of Southampton)

  • Assane Gadiaga

    (University of Southampton)

  • Hal Voepel

    (University of Southampton)

  • Thomas Abbott

    (University of Southampton)

  • Heather R. Chamberlain

    (University of Southampton)

  • Mercedita Tia

    (United Nations Population Fund)

  • Marielle Sander

    (United Nations Population Fund)

  • Justin Davis

    (Planet Labs)

  • Attila N. Lazar

    (University of Southampton)

  • Andrew J. Tatem

    (University of Southampton)

Abstract

Effective governance requires timely and reliable small area population counts. Geospatial modelling approaches which utilise bespoke microcensus surveys and satellite-derived settlement maps and other spatial datasets have been developed to fill population data gaps in countries where censuses are outdated and incomplete. However, logistics and costs of microcensus surveys and tree canopy or cloud cover obscuring settlements in satellite images limit its wider applications in tropical rural settings. Here, we present a two-step Bayesian hierarchical modelling approach that can integrate routinely collected health intervention campaign data and partially observed settlement data to produce reliable small area population estimates. Reductions in relative error rates were 32–73% in a simulation study, and ~32% when applied to malaria survey data in Papua New Guinea. The results highlight the value of demographic data routinely collected through health intervention campaigns or household surveys for improving small area population estimates, and how biases introduced by satellite data limitations can be overcome.

Suggested Citation

  • Chibuzor Christopher Nnanatu & Amy Bonnie & Josiah Joseph & Ortis Yankey & Duygu Cihan & Assane Gadiaga & Hal Voepel & Thomas Abbott & Heather R. Chamberlain & Mercedita Tia & Marielle Sander & Justin, 2025. "Estimating small area population from health intervention campaign surveys and partially observed settlement data," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59862-4
    DOI: 10.1038/s41467-025-59862-4
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    References listed on IDEAS

    as
    1. 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.
    2. Edith Darin & Mathias Kuépié & Hervé Bassinga & Gianluca Boo & Andrew J. Tatem, 2022. "La population vue du ciel : quand l’imagerie satellite vient au secours du recensement," Population (french edition), Institut National d'Études Démographiques (INED), vol. 77(3), pages 467-494.
    3. Gianluca Boo & Edith Darin & Douglas R. Leasure & Claire A. Dooley & Heather R. Chamberlain & Attila N. Lázár & Kevin Tschirhart & Cyrus Sinai & Nicole A. Hoff & Trevon Fuller & Kamy Musene & Arly Bat, 2022. "High-resolution population estimation using household survey data and building footprints," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
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