Evidence map›Paper›PMID 40170026›Full record

ArticleMalaria journal2025

Monitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control.

Mauricianot Randriamihaja, Tokiniaina M Randrianjatovo, Michelle V Evans, Felana A Ihantamalala, Vincent Herbreteau, Christophe Révillion, Eric Delaitre, Thibault Catry, Andres Garchitorena

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 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

9 authors.

Mauricianot Randriamihaja *ONG PIVOT, Ranomafana, Madagascar. mauricianothr@gmail.com.
Tokiniaina M Randrianjatovo *ONG PIVOT, Ranomafana, Madagascar.
Michelle V EvansONG PIVOT, Ranomafana, Madagascar.
Felana A IhantamalalaONG PIVOT, Ranomafana, Madagascar.
Vincent HerbreteauEspace Dev, IRD, Univ Montpellier, Univ Antilles, Univ Guyane, Univ Réunion, 5 Preah Monivong Blvd, Phnom Penh, 12201, Cambodia.
Christophe RévillionEspace Dev, IRD, Univ Montpellier, Univ Antilles, Univ Guyane, Univ Réunion, Montpellier, France.
Eric DelaitreEspace Dev, IRD, Univ Montpellier, Univ Antilles, Univ Guyane, Univ Réunion, Montpellier, France.
Thibault CatryEspace Dev, IRD, Univ Montpellier, Univ Antilles, Univ Guyane, Univ Réunion, Montpellier, France.
Andres GarchitorenaONG PIVOT, Ranomafana, Madagascar.

Funding

This work was supported by a grant from Agence Nationale de la Recherche (Project SMALLER ANR-19-CE36-0001), with additional support from a grant from AfriCam project, funded by the French Development Agency (AFD) as part of PREACTS program and internal funding from Pivot. ANR-19-CE36-0001
6 · The paper itself

Abstract

backgroundProgress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. There is, therefore, increasing interest in the use of larval source management (LSM) to supplement current insecticide-based interventions. However, LSM implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. Remotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations.

methodsA method using radar imagery is proposed to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programmes aiming to implement LSM at scale. This is demonstrated for a 3971 km

resultsFrom these time-series, over a dozen indicators useful for LSM implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of LSM intervention (e.g. prioritization of sites, implementation, follow-up).

conclusionsScale-up of these methods could enable wider implementation of evidence-based LSM interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.

Indexed as

AnophelesFloodsMosquito ControlMosquito VectorsOryzaAnimalsLarvaMadagascarMalariaSatellite ImageryDecision-making toolsHealth surveillance systemLarval source managementMalariaOpenStreetMap mappingSynthetic aperture radar

Identifiers

PMID40170026
PMCPMC11963359

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LicenceCC BY-NC-ND
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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.