ArticlePreventive veterinary medicine2021
A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study.
Article in Preventive veterinary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Point-of-Care Tests for Rapid Detection of Porcine Epidemic Diarrhea Virus: A Systematic Review and Meta-Analysis.Viruses · 2022Pooled it
- Suidae Coronaviruses: Epidemiology, Transmission, and Molecular Diagnosis.Animals : an open access journal from MDPI · 2026Review
- Landscape-scale analysis of raccoon rabies surveillance reveals different drivers of disease dynamics across latitude.PLoS neglected tropical diseases · 2025Article
- Surveillance Analysis and Sample Size Explorer (SASSE): Learning How to Plan Disease Surveillance in Wildlife.Ecology and evolution · 2025Article
- Mechanism by which porcine transmissible gastroenteritis virus disrupts host innate immunity.Frontiers in immunology · 2025Review
- Landscape-Scale Epidemiological Dynamics of SARS-CoV-2 in White-Tailed Deer.Transboundary and emerging diseases · 2024Article
- Risk of African swine fever virus transmission among wild boar and domestic pigs in Poland.Frontiers in veterinary science · 2023Article
- Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface.Transboundary and emerging diseases · 2022Article
- A pan-coronavirus RT-PCR assay for rapid viral screening of animal, human, and environmental specimens.One health (Amsterdam, Netherlands) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pigs (Sus scrofa) may be important surveillance targets for risk assessment and risk-based control planning against emerging zoonoses. Pigs have high contact rates with humans and other animals, transmit similar pathogens as humans including CoVs, and serve as reservoirs and intermediate hosts for notable human pandemics. Wild and domestic pigs both interface with humans and each other but have unique ecologies that demand different surveillance strategies. Three fundamental questions shape any surveillance program: where, when, and how can surveillance be conducted to optimize the surveillance objective? Using theory of mechanisms of zoonotic spillover and data on risk factors, we propose a framework for determining where surveillance might begin initially to maximize a detection in each host species at their interface. We illustrate the utility of the framework using data from the United States. We then discuss variables to consider in refining when and how to conduct surveillance. Recent advances in accounting for opportunistic sampling designs and in translating serology samples into infection times provide promising directions for extracting spatio-temporal estimates of disease risk from typical surveillance data. Such robust estimates of population-level disease risk allow surveillance plans to be updated in space and time based on new information (adaptive surveillance) thus optimizing allocation of surveillance resources to maximize the quality of risk assessment insight.
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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.