ArticleSpatial and spatio-temporal epidemiology2022
Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.
Article in Spatial and spatio-temporal epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Where and why malaria persists in heterogeneous transmission settings: insights from routine health facility data.BMJ global health · 2026Article
- Methods for estimating the burden of acute tropical infectious diseases: A scoping review.PLoS neglected tropical diseases · 2026Article
- Integrating vulnerability and hazard in malaria risk mapping: the elimination context of Senegal.BMC infectious diseases · 2025Article
- Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales.Communications medicine · 2024Article
- An Analysis of Global Orthopedic Organizations Through the Lens of the Four Pillars of Global Surgery.Cureus · 2023Article
- Assessment of health staff's proficiency and quality of key malaria indicators in rural district of Ghana.PloS one · 2022Article
- Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data.Scientific reports · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. However, the aggregation of incidence over large, heterogeneous areas means that these data are underpowered for estimating complex, non-linear models. In contrast, prevalence point-surveys are directly linked to local environmental conditions but are not common in many areas of the world. Here, we train multiple non-linear, machine learning models on Plasmodium falciparum prevalence point-surveys. We then ensemble the predictions from these machine learning models with a disaggregation regression model that uses aggregated malaria incidences as response data. We find that using a disaggregation regression model to combine predictions from machine learning models improves model accuracy relative to a baseline model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.