A novel adaptation of spatial interpolation methods to map health attitudes related to COVID-19.

Publication date: Jul 17, 2023

The COVID-19 pandemic presented substantial challenges to public health stakeholders working to vaccinate populations against the disease, particularly among vaccine hesitant individuals in low- and middle-income countries. Data on the determinants of vaccine hesitancy are scarce, and often available only at the national level. In this paper, our goal is to inform programmatic decision making in support of local vaccine uptake. Our analytical objectives to support this goal are to (1) reliably estimate attitudinal data at the hyperlocal level, and (2) estimate the loss of data heterogeneity among these attitudinal indicators at higher levels of aggregation. With hyperlocal attitudinal data on the determinants of vaccine hesitancy, public health stakeholders can better tailor interventions aimed at increasing uptake sub-nationally, and even down to the individual vaccination site or neighborhood. We estimated attitudinal data on the determinants of vaccine hesitancy as framed by the WHO’s Confidence, Complacency, and Convenience (“3Cs”) Model of Vaccine Hesitancy using a nationally and regionally representative household survey of 4,922 adults aged 18 and above, collected in February 2022. This custom survey was designed to collect information on attitudes towards COVID-19 and concerns about the COVID-19 vaccine. A machine learning (ML) framework was used to spatially interpolate metrics representative of the 3Cs at a one square kilometer (1km) resolution using approximately 130 spatial covariates from high-resolution satellite imagery, and 24 covariates from the 2018 Nigeria Demographic and Health Survey (DHS). Spatial interpolated hyperlocal estimates of the 3Cs captured significant information on attitudes towards COVID-19 and COVID-19 vaccines. The interpolated estimates held increased heterogeneity within each subsequent level of disaggregation, with most variation at the 1km level. Our findings demonstrate that a) attitudinal data can be successfully estimated at the hyperlocal level, and b) the determinants of COVID-19 vaccine hesitancy have large spatial variance that cannot be captured through national surveys alone. Access to community level attitudes toward vaccine safety and efficacy; vaccination access, time, and financial burden; and COVID-19 beliefs and infection concerns presents novel implications for public health practitioners and policymakers seeking to increase COVID-19 vaccine uptake through more customized community-level interventions.

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Concepts Keywords
1km Behavior Change
Adults Geospatial
Pandemic Immunization
Policymakers Machine Learning
Vaccine Misinformation
Vaccine Hesitancy


Type Source Name
disease MESH COVID-19
disease VO vaccine
drug DRUGBANK Tropicamide
disease VO vaccination site
disease VO COVID-19 vaccine
disease VO vaccination
disease VO time
disease MESH infection
pathway REACTOME Reproduction
disease VO immunization
disease MESH death
disease VO organization
disease VO population
disease VO effective
drug DRUGBANK Ademetionine
disease IDO process
disease VO efficient
disease IDO country
disease VO Gap
drug DRUGBANK Pidolic Acid
drug DRUGBANK Coenzyme M
disease IDO algorithm
disease VO report
disease MESH Malaria
pathway KEGG Malaria
disease VO vaccinated
disease IDO cell
drug DRUGBANK Fenamole
disease IDO facility
drug DRUGBANK Hexocyclium
drug DRUGBANK L-Citrulline
drug DRUGBANK Indoleacetic acid
drug DRUGBANK Spinosad
disease MESH Emergency
disease MESH AIDS
drug DRUGBANK Pentaerythritol tetranitrate
disease VO volume
disease VO company
drug DRUGBANK Esomeprazole
disease VO USA
disease IDO symptom
drug DRUGBANK Gold
disease MESH polio
drug DRUGBANK Trestolone
drug DRUGBANK Huperzine B
disease VO Equity

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