ArticleScientific reports2018
Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.Journal of the Royal Society, Interface · 2021Pooled it
- Spatial and spatio-temporal methods for mapping malaria risk: a systematic review.BMJ global health · 2020Pooled it
- Exploring the Effects of Climate Variability on Diarrheal Diseases, Malaria, and Malnutrition in Children Under Five Years in Revuè Sub-Basin, Manica Province, Mozambique.Tropical medicine and infectious disease · 2026Article
- Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018.Scientific reports · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Spatio-temporal dynamics of malaria in Rwanda between 2012 and 2022: a demography-specific analysis.Infectious diseases of poverty · 2024Article
- Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia.Malaria journal · 2023Article
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7 authors.
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Abstract
Malaria is a major cause of morbidity and mortality in Mozambique. We present a malaria early warning system (MEWS) for Mozambique informed by seven years of weekly case reports of malaria in children under 5 years of age from 142 districts. A spatio-temporal model was developed based on explanatory climatic variables to map exceedance probabilities, defined as the predictive probability that the relative risk of malaria incidence in a given district for a particular week will exceed a predefined threshold. Unlike most spatially discrete models, our approach accounts for the geographical extent of each district in the derivation of the spatial covariance structure to allow for changes in administrative boundaries over time. The MEWS can thus be used to predict areas that may experience increases in malaria transmission beyond expected levels, early enough so that prevention and response measures can be implemented prior to the onset of outbreaks. The framework we present is also applicable to other climate-sensitive diseases.
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