ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Serologic evidence of exposure to livestock-associated pathogens in free-ranging mountain tapir (Frontiers in veterinary science · 2026Article
- Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species.Frontiers in veterinary science · 2023Article
- Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface.Transboundary and emerging diseases · 2022Article
- The importance of fine-scale predictors of wild boar habitat use in an isolated population.Ecology and evolution · 2022Article
- Dynamic and integrative approaches to understanding pathogen spillover.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- Ecological interventions to prevent and manage zoonotic pathogen spillover.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models.Journal of animal science · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Managing pathogen spillover at the wildlife-livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife-livestock interface. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
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Registered trials
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.