Evidence map›Paper›PMID 39830899›Full record

ArticleSIAM journal on applied mathematics2024

APPLICATIONS OF MATHEMATICAL PROGRAMMING TO GENETIC BIOCONTROL.

Váleri N Vásquez, John M Marshall

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in SIAM journal on applied mathematics, 2024. 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

2 authors.

Váleri N VásquezEnergy and Resources Group, Rausser College of Natural Resources, University of California Berkeley, Berkeley, CA 94705 USA.
John M MarshallDivision of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94704 USA.

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
NIAID NIH HHS R01 AI143698
6 · The paper itself

Abstract

We review existing approaches to optimizing the deployment of genetic biocontrol technologies-tools used to prevent vector-borne diseases such as malaria and dengue-and formulate a mathematical program that enables the incorporation of crucial ecological and logistical details. The model is comprised of equality constraints grounded in discretized dynamic population equations, inequality constraints representative of operational limitations including resource restrictions, and an objective function that jointly minimizes the count of competent mosquito vectors and the number of transgenic organisms released to mitigate them over a specified time period. We explore how nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) can advance the state of the art in designing the operational implementation of three distinct transgenic public health interventions, two of which are presently in active use around the world.

Indexed as

90-10biomathematicsdynamic population modelgenetic modification technologymixed integer programmingnonlinear programmingpublic healthvector-borne disease

Identifiers

PMID39830899
PMCPMC11741675

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.