Evidence map›Paper›PMID 42360570›Full record

ArticleBulletin of mathematical biology2026

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Mohammad Sharif Ullah, Xiunan Wang, Jin Wang

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 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

3 authors.

Mohammad Sharif UllahDepartment of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA. sharifju49@yahoo.com.ORCID http://orcid.org/0000-0001-8910-484X
Xiunan WangDepartment of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA.ORCID http://orcid.org/0000-0003-4070-4101
Jin WangDepartment of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA.ORCID http://orcid.org/0000-0001-9836-1362

Funding

Mathematical Modeling and Scientific Computing for Infectious Disease ResearchR15GM152943 · NIGMS · UNIVERSITY OF TENNESSEE CHATTANOOGA · PI WANG, JIN · 2023 to 2023
$331k
NIGMS NIH HHS R15 GM152943
6 · The paper itself

Abstract

A coupled epidemic-behavior model is proposed to assess how vaccination, testing, and treatment jointly influence early outbreak control, accounting for individual choices based on perceived risk, intervention costs, and policy incentives. In a uniformly mixing population, individuals make strategic decisions regarding vaccination and testing participation based on the trade-offs between personal costs and communal benefits, while the intensity of public health testing adjusts according to infection prevalence; the transmission model includes vaccine efficacy, the probability of false negatives in testing, treatment-related reductions in infectious duration and mortality, as well as nonlinear saturation and awareness feedback mechanisms. Analytical threshold results and simulations (time series and two-dimensional parameter sweeps) indicate that reducing vaccination and testing costs expands the disease-free region, while false negatives consistently diminish the efficacy of increased testing by delaying diagnosis and perpetuating onward transmission; treatment reduces transmission potential but maximizes population benefit when combined with prompt implementation of testing. The approach also delineates parameter regimes in which behavioral feedback provides non-monotonic responses and recurring outbreaks, notwithstanding heightened interventions. The findings suggest that effective epidemic preparedness necessitates synchronized incentives for vaccination and testing, investments to minimize false negatives (such as enhanced test accuracy or repeated testing), and treatment accessibility aligned with rapid case identification to optimize resource distribution and early-response strategies during emerging and re-emerging infectious disease threats.

Indexed as

Communicable Disease ControlCommunicable DiseasesEpidemiological ModelsModels, BiologicalComputer SimulationDisease OutbreaksHumansMathematical ConceptsVaccinationBehavioral dynamicsEarly diagnosisFalse-negative resultSocial benefit

Identifiers

PMID42360570
PMCPMC13309453

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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.