Evidence map›Paper›PMID 33653111›Full record

ArticleJournal of the Royal Society, Interface2021

Causes of delayed outbreak responses and their impacts on epidemic spread.

Yun Tao, William J M Probert, Katriona Shea, Michael C Runge, Kevin Lafferty, Michael Tildesley, Matthew Ferrari

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  4. Landscape fragmentation overturns classical metapopulation thinking.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. 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

7 authors.

Yun TaoIntelligence Community Postdoctoral Research Fellowship Program, Oak Ridge, TN, USA.
William J M ProbertBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Katriona SheaDepartment of Biology, 208 Mueller Laboratory, Pennsylvania State University, University Park, PA, USA.
Michael C RungeUS Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA.
Kevin LaffertyUS Geological Survey, Western Ecological Research Center at Marine Science Institute, University of California, Santa Barbara, CA, USA.
Michael TildesleyThe Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, West Midlands, UK.
Matthew FerrariDepartment of Biology, 208 Mueller Laboratory, Pennsylvania State University, University Park, PA, USA.

Funding

Linking models and policy: Using active adaptive management for optimal control of disease outbreaksR01GM105247 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI FERRARI, MATTHEW · 2012 to 2016
$1.3M
Biotechnology and Biological Sciences Research Council BB/S01750X/1Biotechnology and Biological Sciences Research Council BB/T004312/4NIGMS NIH HHS R01 GM105247
6 · The paper itself

Abstract

Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.

Indexed as

Disease OutbreaksEpidemicsFoot-and-Mouth DiseaseAnimalsLivestockUnited Kingdomepidemicsfoot-and-mouthlivestock diseasesoutbreak managementresponse delay

Identifiers

PMID33653111
PMCPMC8086880

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.