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A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study.
Pepin, Kim M; Miller, Ryan S; Wilber, Mark Q.
  • Pepin KM; National Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, 4101 Laporte Ave., Fort Collins, CO, 80526, United States. Electronic address: kim.m.pepin@usda.gov.
  • Miller RS; Centers for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, 2150 Center Ave., Fort Collins, CO, 80526, United States.
  • Wilber MQ; Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, 93106, United States.
Prev Vet Med ; 188: 105281, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1051106
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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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Swine Diseases / Zoonoses / Coronavirus Infections / Communicable Diseases, Emerging / Public Health Surveillance Type of study: Case report / Observational study / Prognostic study Limits: Animals / Humans Language: English Journal: Prev Vet Med Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Swine Diseases / Zoonoses / Coronavirus Infections / Communicable Diseases, Emerging / Public Health Surveillance Type of study: Case report / Observational study / Prognostic study Limits: Animals / Humans Language: English Journal: Prev Vet Med Year: 2021 Document Type: Article