Evidence map›Paper›PMID 26576514›Full record

SynthesisTransboundary and emerging diseases2017

Data-Driven Models of Foot-and-Mouth Disease Dynamics: A Review.

L W Pomeroy, S Bansal, M Tildesley, K I Moreno-Torres, M Moritz, N Xiao, T E Carpenter, R B Garabed

Abstract readSystematic Review
In one paragraph

Synthesis in Transboundary and emerging diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Vote-processing rules for combining control recommendations from multiple models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
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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

8 authors.

L W PomeroyDepartment of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA.
S BansalDepartment of Biology, Georgetown University, Washington, DC, USA.
M TildesleyFogarty International Center, National Institutes of Health, Bethesda, MD, USA.
K I Moreno-TorresDepartment of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA.
M MoritzDepartment of Anthropology, The Ohio State University, Columbus, OH, USA.
N XiaoDepartment of Geography, The Ohio State University, Columbus, OH, USA.
T E CarpenterEpicentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.
R B GarabedDepartment of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA.

Funding

Institute for Population ResearchP2CHD058484 · NICHD · OHIO STATE UNIVERSITY · PI HAYFORD, SARAH R · 2014 to 2023
$5.0M
Statistics and Survey Methods CoreR24HD058484 · NICHD · OHIO STATE UNIVERSITY · PI QIAN, ZHENCHAO · 2009 to 2013
$2.1M
NICHD NIH HHS P2C HD058484NICHD NIH HHS R24 HD058484
6 · The paper itself

Abstract

Foot-and-mouth disease virus (FMDV) threatens animal health and leads to considerable economic losses worldwide. Progress towards minimizing both veterinary and financial impact of the disease will be made with targeted disease control policies. To move towards targeted control, specific targets and detailed control strategies must be defined. One approach for identifying targets is to use mathematical and simulation models quantified with accurate and fine-scale data to design and evaluate alternative control policies. Nevertheless, published models of FMDV vary in modelling techniques and resolution of data incorporated. In order to determine which models and data sources contain enough detail to represent realistic control policy alternatives, we performed a systematic literature review of all FMDV dynamical models that use host data, disease data or both data types. For the purpose of evaluating modelling methodology, we classified models by control strategy represented, resolution of models and data, and location modelled. We found that modelling methodology has been well developed to the point where multiple methods are available to represent detailed and contact-specific transmission and targeted control. However, detailed host and disease data needed to quantify these models are only available from a few outbreaks. To address existing challenges in data collection, novel data sources should be considered and integrated into models of FMDV transmission and control. We suggest modelling multiple endemic areas to advance local control and global control and better understand FMDV transmission dynamics. With incorporation of additional data, models can assist with both the design of targeted control and identification of transmission drivers across geographic boundaries.

Indexed as

Foot-and-Mouth Disease VirusModels, BiologicalAnimalsDisease OutbreaksFoot-and-Mouth Diseasedynamical modelsfoot-and-mouth disease virustargeted controltransmission

Identifiers

PMID26576514
PMCPMC5205574

What OpenQuestion holds

Textmetadata
LicenceTDM
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.