Evidence map›Paper›PMID 41737904›Full record

ArticleInfectious Disease Modelling2026

Modelling the effect of long-lasting insecticidal nets on malaria transmission dynamics in Kebbi State, Nigeria.

Emmanuel A Bakare, Idowu I Olasupo, Micheal Imoudu, Afeez Abidemi, Deborah O Daniel, Samuel A Osikoya, Oluwaseun A Mogbojuri, Aaron O Nwana, Dolapo O Oniyelu, Ronke D Olorunfemi and 17 more

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2026. 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. 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

27 authors.

Emmanuel A BakareDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Idowu I OlasupoDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Micheal ImouduInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Afeez AbidemiInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Deborah O DanielInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Samuel A OsikoyaDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Oluwaseun A MogbojuriDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Aaron O NwanaInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Dolapo O OniyeluInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Ronke D OlorunfemiInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Oluwafemi Samson OlagbamiDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Dorcas O AgboolaInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Steven I IkediashiInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Ayomide AdedejiInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Oluwaponmile IkusekaInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Micheal FadairoInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Odunayo OdewaleInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Ifeoma Goodness OnwukaInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Aurel HansinonInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Dolapo A BakareInternational Centre for Applied Mathematical Modelling and Data Analytics, Federal University Oye-Ekiti, Nigeria.
Lisa J WhiteDepartment of Biology, University of Oxford, UK.
Nakul ChitnisSwiss Tropical and Public Health Institute, Basel, Switzerland.
Olusola OresanyaMalaria Consortium Nigeria, Maitama, Abuja, FCT, Nigeria.
Chukwu OkoronkwoSurveillance, Monitoring, Evaluation and Operations Research Unit, National Malaria Elimination Programme, Abuja, Nigeria.
Eze NelsonSurveillance, Monitoring, Evaluation and Operations Research Unit, National Malaria Elimination Programme, Abuja, Nigeria.
Segun KosokoDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.
Gabriel I OgbanDepartment of Mathematics, Federal University Oye-Ekiti, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/aim: Malaria, in Nigeria, is a disease of public health concern that has caused both morbidity and mortality, with the highest prevalence in Kebbi State. Long-lasting insecticidal nets (LLINs) have been instrumental in controlling the burden of the disease. This study aims to assess the effect of LLINs on malaria transmission dynamics in Kebbi State, Nigeria. Methods: Routine data for the confirmed uncomplicated malaria cases in Kebbi State, Nigeria, from January 2015 to May 2024 were used to understand the transmission dynamics within the population. A deterministic compartmental model was developed to capture the malaria transmission dynamics in Kebbi State. Qualitative analysis was carried out, establishing the positivity and boundedness of solutions to ensure the biological feasibility of the model. The disease-free equilibrium was analyzed for stability, revealing under which condition the disease is eradicated. Effective reproduction number, Results: The result showed that if there had been 80% sustained level LLINs usage as proposed by NMEP since 2015, there would have been about 5.2 million malaria cases averted, which corresponds to 97.98% reduction. However, moving forward, if 80% usage can be achieved and sustained, about 3 million malaria cases would be averted by May 2029, signifying an impressive reduction of 78.93% in incidence. Conclusion: We conclude that in order to significantly reduce malaria incidence in Kebbi State to its bearest minimum, health policymakers and decision makers in Nigeria should prioritise scaling up LLINs distribution and usage in the state.

Indexed as

Insecticide-treated netsLong lasting insecticidal-treated netsMalariaTransmission dynamics

Identifiers

PMID41737904
PMCPMC12927317

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.