Evidence map›Paper›PMID 41092282›Full record

ArticleJournal of medical entomology2026

The evolution of public health statistical modeling approaches and how to advance their incorporation into modern arboviral surveillance.

Maggie McCarter, Stella C W Self, Alex Ewing, Mufaro Kanyangarara, Sarah M Gunter, Melissa S Nolan

Abstract read
In one paragraph

Article in Journal of medical entomology, 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

6 authors.

Maggie McCarterDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Stella C W SelfDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Alex EwingData Support Core, Prisma Health, Greenville, SC, USA.
Mufaro KanyangararaDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Sarah M GunterDepartment of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Melissa S NolanDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.ORCID 0000-0001-8579-5372

Funding

Next generation mosquito control through technology-driven trap development and artificial intelligence guided detection of mosquito breeding habitatsR01AI165560 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI GUNTER, SARAH MURPHY, NOLAN, MELISSA SUZANNE · 2021 to 2025
$3.9M
National Institute of Allergy and Infectious Diseases of the National Institutes of Health R01AI165560NIAID NIH HHS R01 AI165560
6 · The paper itself

Abstract

Statistical modeling of infectious disease transmission patterns has been in existence since the mid-1700s, evolving in their utility as the scientific and technological revolutions progressed. Despite the expansion of emerging mathematical and statistical methodologies over the past 250 yr, their usage has largely remained restricted to academic settings. This forum article will discuss the evolution of disease modeling techniques, the most common types of models in use today, and recommendations on how key archetypes can be incorporated into future public health practice. With the recent global impetus to predict and forecast novel pathogens, this article raises the question: Why are endemic arboviruses not included in public health modeling efforts, and how can medical entomologists promote their inclusion?

Indexed as

ArbovirusesArbovirus InfectionsModels, StatisticalPublic HealthAnimalsHumansArbovirusBayesian statisticsforecast modelingIntegrated Nested Laplace Approximation techniqueMarkov Chain Monte Carlo method

Identifiers

PMID41092282
PMCPMC12818379

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.