Evidence map›Paper›PMID 41507284›Full record

ArticleScientific reports2026

High resolution physically based modelling reveals malaria incidence reduction by vector control measures.

Mame Diarra Bousso Dieng, Stephan Munga, Adrian M Tompkins, Miguel Garrido Zornoza, Cyril Caminade, Benjamin Fersch, Joël Arnault, Sammy Khagayi, Maximilian Schwarz, Simon Kariuki and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Mame Diarra Bousso DiengKarlsruhe Institute of Meteorology, Campus Alpine, Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany. diarra.dieng@kit.edu.
Stephan MungaKenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Adrian M TompkinsInternational Centre for Theoretical Physics (ICTP), Trieste, Italy.
Miguel Garrido ZornozaInternational Centre for Theoretical Physics (ICTP), Trieste, Italy.
Cyril CaminadeInternational Centre for Theoretical Physics (ICTP), Trieste, Italy.
Benjamin FerschKarlsruhe Institute of Meteorology, Campus Alpine, Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany.
Joël ArnaultKarlsruhe Institute of Meteorology, Campus Alpine, Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany.
Sammy KhagayiKenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Maximilian SchwarzRemote Sensing Solutions GmbH (RSS), Munich, Germany.
Simon KariukiKenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Godfrey BigogoKenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Harald KunstmannKarlsruhe Institute of Meteorology, Campus Alpine, Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malaria continues to cause over 600,000 deaths annually in sub-Saharan Africa, disproportionately affecting children under five. Despite sustained control efforts, transmission remains highly sensitive to local environmental and climatic variability, underscoring the need for physically grounded models capable of capturing these dynamics. To address this challenge, we developed a high-resolution hybrid modeling framework linking WRF/WRF-Hydro and VECTRI. The framework integrates atmospheric, hydrological, ecological, and intervention processes at 1 km and 50 m resolutions and includes a new compartment for insecticide-treated net (ITN) coverage. Using data from 2007–2022 in western Kenya, a period of large-scale ITN deployment, the model reproduced observed malaria trends with a mean monthly deviation of ±100–150 cases. Simulations showed that ITN coverage reduced the entomological inoculation rate and malaria incidence by 58% and 41%, respectively, with the highest efficacy under warm ([Formula: see text]C) and moderately wet (150–250 mm) conditions. The findings suggest that integrating environmental process modeling with optimized, targeted control strategies provides a cost-effective and operationally relevant framework for sustainable malaria management under changing climatic conditions.

Indexed as

MalariaMosquito ControlMosquito VectorsAnimalsAnophelesHumansIncidenceInsecticide-Treated BednetsKenyaModels, TheoreticalBet net useHealth and demographic surveillance systemsMalaria transmission dynamicsModel coupling and optimization

Identifiers

PMID41507284
PMCPMC12791136

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.