Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage

Abstract

Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1x1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.

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Identifiers

ORCID for C. Edson Utazi: orcid.org/0000-0002-0534-5310

ORCID for Ortis Yankey: orcid.org/0000-0002-0808-884X

ORCID for Somnath Chaudhuri: orcid.org/0000-0003-4899-1870

ORCID for Iyanuloluwa D. Olowe: orcid.org/0009-0002-8848-1971

ORCID for Attila N. Lazar: orcid.org/0000-0003-2033-2013

ORCID for Andrew J. Tatem: orcid.org/0000-0002-7270-941X

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