Evidence map›Paper›PMID 40235572›Full record

ArticleFrontiers in veterinary science2025

Estimating the risk of zoonotic transmission of swine influenza A variant during agricultural fairs in the United States: a mathematical modeling.

Dana C Pittman Ratterree, Sapna Chitlapilly Dass, Martial L Ndeffo-Mbah

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. 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

3 authors.

Dana C Pittman RatterreeDepartment of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States.
Sapna Chitlapilly DassDepartment of Animal Science, College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States.
Martial L Ndeffo-MbahDepartment of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Agricultural fairs offer a unique interface between humans and swine. We investigate the transmissibility of influenza A variant from pigs to humans using epidemiological data from a 2011 zoonotic outbreak of an influenza H3N2 variant during an agricultural county fair in Pennsylvania. Methods: We developed a mathematical model for the transmission of a swine influenza pathogen among pigs and humans at an agricultural fair. We fitted our model to the outbreak data to estimate zoonotic transmissibility. We considered nine data-driven scenarios of swine-to-swine basic reproductive number (R Results: We estimated the probability of swine-to-human H3N2v transmission per minute of swine contact for which our model best fitted the data. The probability of transmission of H3N2v per minute of contact with swine among club members was estimated to vary from 0.029 (95% confidence interval (CI): 0.028-0.030), when R Discussion: These results show that the transmission risk may vary substantially between club members and attendees and with the underlying disease transmission among pigs. Although fair attendees may have a small transmissibility risk, annual fair attendees represent a large population likely to experience zoonotic events and facilitate the emergence of a potential pandemic influenza variant.

Indexed as

agricultural fairmathematical modelingswine influenzatransmission riskzoonotic events

Identifiers

PMID40235572
PMCPMC11997979

What OpenQuestion holds

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Registered trials

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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.