Evidence map›Paper›PMID 41161569›Full record

ReviewCurrent opinion in insect science2026

Integrating mosquito genomics into simulation modeling: opportunities for better-informed biocontrol.

Gordana Rašić, John M Marshall

Abstract readReview
In one paragraph

Review in Current opinion in insect science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. EliminatingInsects · 2026
    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

2 authors.

Gordana RašićMosquito Genomics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Australia. Electronic address: gordana.rasic@qimrb.edu.au.
John M MarshallDivisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, CA, USA; Innovative Genomics Institute, University of California, Berkeley, CA, USA. Electronic address: john.marshall@berkeley.edu.

Funding

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
Conditional and female lethal approaches for mosquito controlR01AI190001 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Omar Sultan Akbari · 2025 to 2026
$1.2M
NIAID NIH HHS R01 AI143698NIAID NIH HHS R01 AI190001
6 · The paper itself

Abstract

Mosquito-borne diseases remain a major global health burden, and novel biocontrol tools are quickly advancing from the laboratory to the field. Mathematical models play a central role in evaluating these interventions, yet their predictive accuracy depends on robust parameterization. Population genomics presents a powerful opportunity to address this challenge. Here, we review progress at the interface between mosquito genomics and biocontrol modeling, highlighting how genomic data have informed our understanding of mosquito population structure, standing genetic variation at gene drive target sites, and sources of resurgence for suppressed populations. We also discuss frontiers, including new approaches to quantifying gene flow, mating behaviors, and inbreeding depression, all of which shape intervention outcomes. By tapping this potential to better quantify our understanding of mosquito ecology, modelers can develop context-specific models with better predictive accuracy, supporting efficacy and risk assessment, design of field trials and interventions, and promotion of regulation and public trust.

Indexed as

CulicidaeGenomicsMosquito ControlPest Control, BiologicalAnimalsComputer Simulation

Identifiers

PMID41161569
PMCPMC12638013

What OpenQuestion holds

Textmetadata
LicenceTDM
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.