Evidence map›Paper›PMID 40181373›Full record

ArticleMalaria journal2025

Projection of future malaria prevalence in the upper river region of The Gambia.

Ugochinyere Agatha Okafor, Pierre-Claver Konin Kakou, Umberto D'Alessandro, Vincent Nduka Ojeh, Sidat Yaffa

Abstract read
In one paragraph

Article in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

5 authors.

Ugochinyere Agatha OkaforSchool of Agriculture and Environmental Sciences, Department of Climate Change and Education, University of The Gambia, Serekunda, The Gambia. ugochinyereagatha@gmail.com.
Pierre-Claver Konin KakouClimate Change and Energy, WASCAL Doctoral Program Scholar, Université Abdou Moumouni de Niamey, Niamey, Niger.
Umberto D'AlessandroMedical Research Council Unit the Gambia at the London, School of Hygiene and Tropical, Banjul, The Gambia.
Vincent Nduka OjehTaraba State University, Jalingo, Nigeria.
Sidat YaffaSchool of Agriculture and Environmental Sciences, Department of Climate Change and Education, University of The Gambia, Serekunda, The Gambia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis work investigated the future (2021-2050) impact of Climate Change on Malaria Prevalence in the Upper River Region of The Gambia under two representative concentration pathways, RCP4.5 and RCP8.5, comparing it with the observed evaluation period of 2011-2022.

methodsThe observed climatic variable data used was obtained from the Department of Water Resources and the corresponding malaria cases from the archive of the primary Health database, Banjul, The Gambia. Projected monthly temperature, precipitation, and relative humidity were downloaded from the coordinated Regional downscaling experiment (CORDEX) stimulation of the Rossby Centre Regional Atmospheric regional climate (RCA4). The dataset spans the decades from 2021 to 2050, providing insight into future climatic and epidemiological trends. Gradient Boost Machine Learning algorithm was utilized for the malaria projection both in the population below 5 and above five years.

resultsThe result revealed an increase in malaria incidence under RCP4.5 and RCP8.5 climatic scenarios for both age categories with a clear indication in the population above five years. DISCUSSION AND

conclusionThe result pictures how climate change will impact malaria under RCP4.5 and RCP8.5 emission scenarios in the region and also clearly reveals that the upper river region of the Gambia population is at risk of malaria infection, thus, a strategic and robust intervention scheme is highly solicited.

Indexed as

Climate ChangeMalariaAdolescentAdultChildChild, PreschoolGambiaHumansIncidenceInfantMiddle AgedPrevalenceRiversYoung AdultClimate changeGambiaMachine learningMalaria prevalenceRCP scenarios

Identifiers

PMID40181373
PMCPMC11967031

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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.