Estimating small area population from health intervention campaign surveys and partially observed settlement data
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 cens...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59862-4 |
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| author | 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 Davis Attila N. Lazar Andrew J. Tatem |
| author_facet | 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 Davis Attila N. Lazar Andrew J. Tatem |
| author_sort | Chibuzor Christopher Nnanatu |
| collection | DOAJ |
| description | 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maps and other spatial datasets have been developed to fill population data gaps in countries where censuses are outdated and incomplete.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. |
| format | Article |
| id | doaj-art-49f1f9ef47814a5b843cec114b5145d2 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-49f1f9ef47814a5b843cec114b5145d22025-08-20T03:22:11ZengNature PortfolioNature Communications2041-17232025-05-0116111310.1038/s41467-025-59862-4Estimating small area population from health intervention campaign surveys and partially observed settlement dataChibuzor Christopher Nnanatu0Amy Bonnie1Josiah Joseph2Ortis Yankey3Duygu Cihan4Assane Gadiaga5Hal Voepel6Thomas Abbott7Heather R. Chamberlain8Mercedita Tia9Marielle Sander10Justin Davis11Attila N. Lazar12Andrew J. Tatem13WorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonNational Statistical OfficeWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonUnited Nations Population FundUnited Nations Population FundPlanet LabsWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonWorldPop Research Group, School of Geography and Environmental Science, University of SouthamptonAbstract 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.https://doi.org/10.1038/s41467-025-59862-4 |
| spellingShingle | 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 Davis Attila N. Lazar Andrew J. Tatem Estimating small area population from health intervention campaign surveys and partially observed settlement data Nature Communications |
| title | Estimating small area population from health intervention campaign surveys and partially observed settlement data |
| title_full | Estimating small area population from health intervention campaign surveys and partially observed settlement data |
| title_fullStr | Estimating small area population from health intervention campaign surveys and partially observed settlement data |
| title_full_unstemmed | Estimating small area population from health intervention campaign surveys and partially observed settlement data |
| title_short | Estimating small area population from health intervention campaign surveys and partially observed settlement data |
| title_sort | estimating small area population from health intervention campaign surveys and partially observed settlement data |
| url | https://doi.org/10.1038/s41467-025-59862-4 |
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