Estimating the potential impact of surveillance test-and-treat posts to reduce malaria in border regions in sub-Saharan Africa: a modelling study
This article has been Reviewed by the following groups
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- Evaluated articles (Rapid Reviews Infectious Diseases)
Abstract
The last malaria cases in near-elimination settings are often found in international border regions due to the presence of hard-to-reach populations, conflict, uneven intervention coverage, and human migration. Test-and-treat border posts are an under-researched form of active case detection used to interrupt transmission chains between countries. We used an individual-based, mathematical metapopulation model of P. falciparum to estimate the effectiveness of border posts on total cases in malaria-endemic sub-Saharan Africa. We estimated that implementation of international border posts across 401 sub-national administrative units would avert a median of 7,173 (IQR: 1,075 to 23,550) cases per unit over a 10-year period and reduce Pf PR 2-10 by a median of 0.21% (IQR: 0.04% to 0.44%). Border posts were most effective in low-transmission settings with high-transmission neighbors. Border posts alone will not allow a country to reach elimination, particularly when considering feasibility and acceptability, but could contribute to broader control packages to targeted populations.
Article activity feed
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David Guararie
Review 2: "Estimating the Potential Impact of Surveillance Test-and-Treat Posts to Reduce Malaria in Border Regions in Sub-Saharan Africa: A Modelling Study"
Reviewers highlighted the study's significant contribution to understanding the role of human mobility in malaria control and its detailed simulations across 401 sub-national units
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Lin Zhu
Review 1: "Estimating the Potential Impact of Surveillance Test-and-Treat Posts to Reduce Malaria in Border Regions in Sub-Saharan Africa: A Modelling Study"
Reviewers highlighted the study's significant contribution to understanding the role of human mobility in malaria control and its detailed simulations across 401 sub-national units
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Strength of evidence
Reviewers: L Zhu (Stanford) | 📗📗📗📗◻️
D Guararie (Case Western Reserve University) | 📘📘📘📘📘 -
