Evidence map›Paper›PMID 40598170›Full record

ArticleMalaria journal2025

Modelling the effects of precipitation and temperature on malaria incidence in coastal and western Kenya.

Amna Tariq, Donal Bisanzio, Francis Mutuku, Bryson Ndenga, Zainab Jembe, Priscilla Maina, Philip Chebii, Charles Ronga, Victoria Okuta, A Desiree LaBeaud

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

10 authors.

Amna TariqDepartment of Pediatrics, Division of Infectious Diseases, Stanford University, Stanford, CA, USA. atariq1@stanford.edu.
Donal BisanzioRTI International, Washington, DC, USA.
Francis MutukuDepartment of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya.
Bryson NdengaCentre for Global Health Research, Kenya, Medical Research Institute, Kisumu, Kenya.
Zainab JembeVector Borne Disease Control Unit, Msambweni County Referral Hospital, Msambweni, Kenya.
Priscilla MainaVector Borne Disease Control Unit, Msambweni County Referral Hospital, Msambweni, Kenya.
Philip ChebiiVector Borne Disease Control Unit, Msambweni County Referral Hospital, Msambweni, Kenya.
Charles RongaCentre for Global Health Research, Kenya, Medical Research Institute, Kisumu, Kenya.
Victoria OkutaCentre for Global Health Research, Kenya, Medical Research Institute, Kisumu, Kenya.
A Desiree LaBeaudDepartment of Pediatrics, Division of Infectious Diseases, Stanford University, Stanford, CA, USA.

Funding

Research Mentoring and Building Capacity of underrepresented Minority Research Scientists in India.D43TW010540 · FIC · YALE UNIVERSITY · PI MICHELE BARRY, Eva Harris · 2017 to 2026
$15.5M
The child health impacts of overlapping vector-borne diseases in KenyaR01AI102918 · NIAID · STANFORD UNIVERSITY · PI Angelle Desiree LaBeaud · 2013 to 2026
$7.7M
FIC NIH HHS D43 TW010540NIAID NIH HHS R01 AI102918NIH HHS R01AI102918
6 · The paper itself

Abstract

backgroundMalaria continues to plague sub-Saharan Africa despite great efforts geared towards its mitigation. In Kenya alone, 70% of the population remains at risk for malaria every year. Malaria is spread by Anopheles mosquitoes carrying the Plasmodium parasite, and displays a complex ecology with various socio-economic, biophysical factors and meteorological predictors, particularly temperature and precipitation, associated with the occurrence of the disease.

methodsThis study estimated the empirical relationship of temperature and precipitation on the temporal population dynamics of symptomatic malaria cases in Kenyan children living in Ukunda (on Kenyan southern coast), and Kisumu (on Kenyan lake zone) between 2014 and 2022 using daily malaria incidence data collected during a febrile illness surveillance study, merged with daily climatological data collected from ground devices. Generalized additive mixed models (GAMMs) were used to explore the relationship between malaria cases and temperature and precipitation, with Poisson, zero-inflated Poisson and negative binomial distribution and a logarithmic link function. The cross-correlation function assessed the time lags with peak correlations between malaria incidence, precipitation and temperature.

resultsThe data showed 673 positive malaria incident cases amongst children in Kisumu compared to 1209 cases in Ukunda. The results indicate a positive correlation of malaria incidence with rainfall and temperature in Kisumu and a positive correlation between malaria incidence and rainfall and a negative correlation between malaria incidence and temperature in Ukunda. The lags between malaria incidence and rainfall were similar for Kisumu and Ukunda and estimated between 7 and 15 weeks. With a time lag of 15 weeks in Ukunda, GAMM depicted a steady relationship between rainfall and malaria cases until rainfall reaches 150 mm and the relationship between malaria cases and temperature peaks at 26-27 °C. In Kisumu using a time lag of 15 weeks in the GAMM, a steady relationship between rainfall and malaria cases was observed until almost 120 mm of rainfall, peaking at 160 mm of rainfall and the relationship between malaria cases and temperature remained steady between 22 and 30 °C.

conclusionAssessing the changes in malaria case incidence due to changing seasonality and weather patterns provides policymakers with updated information to strategize malaria control policies.

Indexed as

MalariaRainTemperatureChildChild, PreschoolFemaleHumansIncidenceInfantKenyaMaleCross-correlationGeneralized additive modelsLagsMalariaPrecipitationTemperature

Identifiers

PMID40598170
PMCPMC12217918

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Registered trials

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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.