Evidence map›Paper›PMID 38995977›Full record

ArticlePLoS computational biology2024

Epidemiological modeling of SARS-CoV-2 in white-tailed deer (Odocoileus virginianus) reveals conditions for introduction and widespread transmission.

Elias Rosenblatt, Jonathan D Cook, Graziella V DiRenzo, Evan H Campbell Grant, Fernando Arce, Kim M Pepin, F Javiera Rudolph, Michael C Runge, Susan Shriner, Daniel P Walsh and 1 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

11 authors.

Elias RosenblattRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont, United States of America.ORCID 0000-0001-7241-2109
Jonathan D CookU.S. Geological Survey, Eastern Ecological Science Center, Laurel, Maryland, United States of America.ORCID 0000-0001-7000-8727
Graziella V DiRenzoU. S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts, Amherst, Massachusetts, United States of America.ORCID 0000-0001-5264-4762
Evan H Campbell GrantU.S. Geological Survey, Eastern Ecological Science Center, Turner's Falls, Massachusetts, United States of America.ORCID 0000-0003-4401-6496
Fernando ArceDepartment of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, United States of America.ORCID 0000-0002-7622-3791
Kim M PepinNational Wildlife Research Center, USDA, APHIS, Fort Collins, Colorado, United States of America.ORCID 0000-0002-9931-8312
F Javiera RudolphU.S. Geological Survey, Eastern Ecological Science Center, Laurel, Maryland, United States of America.ORCID 0000-0002-7124-4649
Michael C RungeU.S. Geological Survey, Eastern Ecological Science Center, Laurel, Maryland, United States of America.ORCID 0000-0002-8081-536X
Susan ShrinerNational Wildlife Research Center, USDA, APHIS, Fort Collins, Colorado, United States of America.ORCID 0000-0003-0349-7182
Daniel P WalshU. S. Geological Survey, Montana Cooperative Wildlife Research Unit, University of Montana, Missoula, Montana, United States of America.
Brittany A MosherRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont, United States of America.ORCID 0000-0002-8458-9056

Funding

Coronavirus Aid, Relief, and Economic Security Act P.L. 116-136U.S. Department of Agriculture
6 · The paper itself

Abstract

Emerging infectious diseases with zoonotic potential often have complex socioecological dynamics and limited ecological data, requiring integration of epidemiological modeling with surveillance. Although our understanding of SARS-CoV-2 has advanced considerably since its detection in late 2019, the factors influencing its introduction and transmission in wildlife hosts, particularly white-tailed deer (Odocoileus virginianus), remain poorly understood. We use a Susceptible-Infected-Recovered-Susceptible epidemiological model to investigate the spillover risk and transmission dynamics of SARS-CoV-2 in wild and captive white-tailed deer populations across various simulated scenarios. We found that captive scenarios pose a higher risk of SARS-CoV-2 introduction from humans into deer herds and subsequent transmission among deer, compared to wild herds. However, even in wild herds, the transmission risk is often substantial enough to sustain infections. Furthermore, we demonstrate that the strength of introduction from humans influences outbreak characteristics only to a certain extent. Transmission among deer was frequently sufficient for widespread outbreaks in deer populations, regardless of the initial level of introduction. We also explore the potential for fence line interactions between captive and wild deer to elevate outbreak metrics in wild herds that have the lowest risk of introduction and sustained transmission. Our results indicate that SARS-CoV-2 could be introduced and maintained in deer herds across a range of circumstances based on testing a range of introduction and transmission risks in various captive and wild scenarios. Our approach and findings will aid One Health strategies that mitigate persistent SARS-CoV-2 outbreaks in white-tailed deer populations and potential spillback to humans.

Indexed as

COVID-19DeerSARS-CoV-2AnimalsAnimals, WildComputational BiologyDisease OutbreaksEpidemiological ModelsHumansZoonoses

Identifiers

PMID38995977
PMCPMC11268674

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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.