Evidence map›Paper›PMID 33064750›Full record

ArticlePloS one2020

Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems.

Qihui Yang, Don M Gruenbacher, Jessica L Heier Stamm, David E Amrine, Gary L Brase, Scott A DeLoach, Caterina M Scoglio

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 10% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
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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 at 1 institution in 1 country.

Qihui YangDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America.ORCID 0000-0003-3215-3645
Don M GruenbacherDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America.
Jessica L Heier StammDepartment of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS, United States of America.ORCID 0000-0002-5310-3543
David E AmrineBeef Cattle Institute, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States of America.
Gary L BraseDepartment of Psychological Sciences, Kansas State University, Manhattan, KS, United States of America.
Scott A DeLoachDepartment of Computer Science, Kansas State University, Manhattan, KS, United States of America.
Caterina M ScoglioDepartment of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America.
Kansas State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.

Indexed as

AnimalsCattleCattle DiseasesComputer SimulationDisease OutbreaksFoot-and-Mouth DiseaseInformation DisseminationModels, BiologicalMotor VehiclesSurveys and Questionnaires

Identifiers

PMID33064750
PMCPMC7567383
OpenAlexW3092981469

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.