Evidence map›Paper›PMID 38709820›Full record

ArticlePLoS computational biology2024

MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations.

Héctor M Sánchez C, David L Smith, John M Marshall

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Héctor M Sánchez CDivisions of Epidemiology and Biostatistics, University of California Berkeley, Berkeley, California, United States of America.ORCID 0000-0001-7378-8853
David L SmithInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0003-4367-3849
John M MarshallDivisions of Epidemiology and Biostatistics, University of California Berkeley, Berkeley, California, United States of America.

Funding

Spatial Targeting and Adaptive Vector Control for Residual Transmission and Malaria Elimination in Urban African SettingsR01AI163398 · NIAID · UNIVERSITY OF WASHINGTON · PI SMITH, DAVID L. · 2021 to 2025
$3.2M
Understanding mosquito movement and its relevance to control through genetic analysisR01AI143698 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI MARSHALL, JOHN MACKY · 2020 to 2024
$1.7M
NIAID NIH HHS R01 AI143698NIAID NIH HHS R01 AI163398
6 · The paper itself

Abstract

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Indexed as

Mosquito ControlAedesAnimalsComputational BiologyCulicidaeFemaleGene Drive TechnologyInsecticide ResistanceMosquito Vectors

Identifiers

PMID38709820
PMCPMC11098508

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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.