Evidence map›Paper›PMID 41547981›Full record

ArticleScientific reports2026

Climatic and governance determinants of malaria transmission in Rivers State, Nigeria.

S E Egbom, F O Nduka, S O Nzeako, G O Nwafor, D C Bartholomew, C C Nwaigwe, H C Iwu, C J Ogbonna, C I Nwokike, M C Opara and 3 more

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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

13 authors.

S E EgbomDepartment of Environmental Health Science, Federal University of Technology, Owerri, Imo State, Nigeria.
F O NdukaDepartment of Animal and Environmental Biology, University of Portharcourt, Port Harcourt, Rivers State, Nigeria.
S O NzeakoDepartment of Animal and Environmental Biology, University of Portharcourt, Port Harcourt, Rivers State, Nigeria.
G O NwaforDepartment of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria.
D C BartholomewDepartment of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria. barthold@myumanitoba.ca.
C C NwaigweDepartment of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria.
H C IwuDepartment of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria.
C J OgbonnaDepartment of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria.
C I NwokikeDepartment of Mathematics, Federal University of Technology, Owerri, Imo State, Nigeria.
M C OparaDepartment of Environmental Health Science, Federal University of Technology, Owerri, Imo State, Nigeria.
C O EzenwakaBiology Department, Federal University Otuoke, Otuoke, Bayelsa State, Nigeria.
E O AnyanwuDepartment of Environmental Health Science, Federal University of Technology, Owerri, Imo State, Nigeria.
J I NwachukwuDepartment of Environmental Management, Federal University of Technology, Owerri, Imo State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malaria remains a significant public health challenge in tropical regions where environmental and governance factors influence transmission dynamics. This study examined how meteorological conditions and shifts in government policy affect malaria incidence in Rivers State from 2007 to 2021. Poisson, quasi-Poisson, and negative binomial models were applied to identify significant environmental predictors, followed by comparison with a SARIMAX model to evaluate forecasting performance. The quasi-Poisson model provided the best fit among count models; however, SARIMAX demonstrated superior predictive accuracy. The SARIMAX(1,1,0)(1,1,1)₁₂ model revealed that monthly malaria incidence is significantly influenced by seasonality, rainfall, and changes in government malaria-control policy periods. These findings highlight the value of integrating climatic and governance indicators into malaria early-warning systems. The optimal model identified here provides a practical tool for strengthening malaria surveillance, planning, and response efforts in Rivers State.

Indexed as

ClimateMalariaRiversHumansIncidenceNigeriaSeasonsGovernanceMalariaMeteorological factorsModel validationPredictive modelingRivers stateSARIMAX

Identifiers

PMID41547981
PMCPMC12886937

What OpenQuestion holds

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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.