Evidence map›Paper›PMID 42154746›Full record

ArticlePloS one2026

Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study.

GloriaSalome Shirima, Emma L Fairbanks, Tegemeo Gavana, Gerald Kiwelu, Ismail Nambunga, Yeromin P Mlacha, Silas Mirau, Prosper Chaki, Nakul Chitnis, Samson Kiware

Abstract read
In one paragraph

Article in PloS one, 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

10 authors.

GloriaSalome ShirimaEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.ORCID https://orcid.org/0009-0007-5769-855X
Emma L FairbanksDisease Modelling, Swiss Tropical and Public Health Institute, Allschwil, Switzerland.ORCID https://orcid.org/0000-0002-1598-962X
Tegemeo GavanaEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.
Gerald KiweluEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.
Ismail NambungaEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.
Yeromin P MlachaEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.
Silas MirauComputational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, Tengeru, Arusha, Tanzania.
Prosper ChakiEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.
Nakul ChitnisDisease Modelling, Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
Samson KiwareEnvironmental Health and Ecological Science Department, Ifakara Health Institute, Ifakara, Tanzania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combining larviciding with insecticide treated nets (ITNs) can reduce malaria transmission. However, most modelling analyses use generalized scenarios rather than incorporating local epidemiological and ecological contexts. In Tanzania and other countries, larviciding is increasingly being prioritized in national strategies, with growing advocacy for its broader implementation, to achieving sustained malaria reduction. District-specific modelling is therefore essential to capture variation in transmission ecology, seasonality, and varying coverage levels, providing evidence that is both rigorous and actionable for malaria control programs. The Vector Control Optimization Model (VCOM) was adapted and extended to incorporate local seasonality, simulating the impact of larviciding across a range of coverage levels combined with ITNs. The model was parameterized using district-level field-data on mosquito mortality collected before (2016-2017) and after (2019-2021) larviciding implementation. Mosquito mortality rates were estimated using Bayesian inference. Outcomes were evaluated specifically for Anopheles gambiae s.l. including annual entomological inoculation rates (EIR) and mosquito density. Sensitivity analysis explored the influence of key parameters driving transmission in this scenario study. The immature mosquito mortality rate due to larviciding was 61% based on field data. VCOM simulation showed that, at 80%, ITNs coverage, larviciding substantially reduced mosquito densities and EIR. Specifically, combining ITNs at 80% and larviciding coverage ≥ 60% lowered EIR below 1 ib/p/year, the threshold required to interrupt malaria transmission. Sensitivity analyses highlighted the high impact of targeting immature mosquitoes, suggesting larviciding can effectively complement ITNs to control vectors, including invasive species like An. stephensi, regardless of feeding preference, resting, and biting behaviors, which hinder the effectiveness of most vector control tools. This study provides local evidence that larviciding is an effective complement to ITNs for interrupting malaria transmission. Implementation should leverage innovative approaches, such as drones for precise mapping and targeted application of biological larvicides, to maximize coverage, and scalability for district-level malaria control and elimination.

Indexed as

Computer SimulationInsecticidesMalariaMosquito ControlMosquito VectorsAnophelesBayes TheoremHumansInsecticide-Treated BednetsLarvaRural PopulationTanzaniaInsecticides

Identifiers

PMID42154746
PMCPMC13186345

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LicenceCC BY
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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.