Evidence map›Paper›PMID 41567818›Full record

ArticleFrontiers in tropical diseases2025

Predicting the ecological niches of

Samuel K Muchiri, Moses M Musau, Paul Mwaniki, Fridah Kirimi, Nathan O Agutu, Emelda A Okiro, Simon Dellicour, Robert W Snow

Abstract read
In one paragraph

Article in Frontiers in tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Post-establishment environmental suitability ofOne health (Amsterdam, Netherlands) · 2026
    Article
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

8 authors.

Samuel K MuchiriDepartment of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
Moses M MusauPopulation and Health Impact Surveillance Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Paul MwanikiData and Statistics, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Fridah KirimiDepartment of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
Nathan O AgutuDepartment of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
Emelda A OkiroPopulation and Health Impact Surveillance Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Simon DellicourSpatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.
Robert W SnowPopulation and Health Impact Surveillance Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.

Funding

Wellcome Trust 203077Wellcome Trust 212176
6 · The paper itself

Abstract

Background: To evaluate the human population at risk of arboviral illnesses and improve vector and disease surveillance, it is crucial to model the probability of occurrence of impactful mosquitoes such as Methods: Occurrence data on Results: A total of 291 unique locations reported positive identification of Conclusion: The predicted distribution of

Indexed as

Aedes aegyptiarbovirusecological niche modellingmaximum entropyprobability of occurrence

Identifiers

PMID41567818
PMCPMC7618658

What OpenQuestion 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 OpenQuestion graph.