ReviewMalaria journal2025
An integrative review of the combined use of mathematical and statistical models for estimating malaria transmission parameters.
Review in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCharacterizing malaria burden and its evolution is complicated by the high levels of spatio-temporal heterogeneity and by the complexity of the transmission process. MAIN BODY: This manuscript presents an integrative review of the combined use of mathematical and statistical models to estimate malaria transmission parameters. Therefore, this work aims to provide a solid methodological foundation for the estimation of transmission intensity and other relevant quantities. A perspective covering both mathematical and statistical models to appraise commonly used metrics is adopted and subsequently their inclusion as parameters in compartmental models as well as their estimation from available data is discussed. The current review argues in favour of a more widespread consideration of the Force of Infection (FOI) as a malaria transmission metric. Using the FOI dispenses the analyst from explicitly describing vector dynamics in compartmental modelling, simplifying the system of differential equations describing transmission dynamics. In turn, its estimation can be flexibly performed by solely relying on host data, such as parasitaemia or serology, avoiding the need for entomological data.
conclusionThe present work argues that the interaction between mathematical and statistical models, although previously exemplified by others, is underappreciated when modelling malaria transmission. Orienting the exposition around the FOI provides an illustration of the potential borne by the existing methodology. A connection between the two modelling frameworks warrants better scrutiny, as it leads to the possibility of exploiting the full range of modern statistical methods.
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Registered trials
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