title: Combining surveillance and expert evidence of viral hemorrhagic septicemia freedom: a decision science approach.
abstract: The ability to combine evidence streams to establish disease freedom or prioritize surveillance is important for the evaluation of emerging diseases, such as viral hemorrhagic septicemia virus (VHSV) IVb in freshwater systems of the United States and Canada. Waterways provide a relatively unconstrained pathway for the spread of VHSV; and structured surveillance for emerging disease in open systems has many challenges. We introduce a decision framework for estimating VHSV infection probability that draws from multiple evidence streams and addresses challenges associated with the assessment of emerging disease. Using this approach, historical and risk-based evidence, whether empirical or expert-derived, supplement surveillance data to estimate disease probability. Surveillance-based estimates of VHSV prevalence were described using beta distributions. Subjective likelihood ratios (LRs), representing contextual risk, were elicited by asking experts to estimate the predicted occurrence of risk factors among VHSV-affected vs. VHSV-unaffected watersheds. We used the odds form of Bayes' theorem to aggregate expert and surveillance evidence to predict the risk-adjusted posterior probability of VHSV-infection for given watersheds. We also used LRs representing contextual risk to quantify the time value of past surveillance data. This evidence aggregation model predicts disease probability from the combined assessment of multiple sources of information. The method also provides a flexible framework for iterative revision of disease freedom status as knowledge and data evolve.
authors.affiliation: USDA Animal and Plant Health Inspection Service, Veterinary Services, Centers for Epidemiology and Animal Health, 2150 Centre Ave, Bldg B, CO 80526, USA. lori.l.gustafson@aphis.usda.gov <lori.l.gustafson@aphis.usda.gov>
authors.forename: L
authors.initials: L
authors.lastname: Gustafson
authors.forename: K
authors.initials: K
authors.lastname: Klotins
authors.forename: S
authors.initials: S
authors.lastname: Tomlinson
authors.forename: G
authors.initials: G
authors.lastname: Karreman
authors.forename: A
authors.initials: A
authors.lastname: Cameron
authors.forename: B
authors.initials: B
authors.lastname: Wagner
authors.forename: M
authors.initials: M
authors.lastname: Remmenga
authors.forename: N
authors.initials: N
authors.lastname: Bruneau
authors.forename: A
authors.initials: A
authors.lastname: Scott
mesh_terms: D000818:Animals; D017756:Aquaculture; D001499:Bayes Theorem; D002170:Canada / Q000453:epidemiology; D003657:Decision Making; D004196:Disease Outbreaks / Q000517:prevention & control / Q000662:veterinary; D004784:Environmental Monitoring; D062665:Epidemiological Monitoring; D019317:Evidence-Based Medicine; D005393:Fish Diseases / Q000453:epidemiology* / Q000517:prevention & control / Q000821:virology; D005399:Fishes; D016013:Likelihood Functions; D029141:Novirhabdovirus / Q000302:isolation & purification*; D018353:Rhabdoviridae Infections / Q000453:epidemiology / Q000517:prevention & control / Q000662:veterinary*; D018570:Risk Assessment; D012307:Risk Factors; D018571:Sentinel Surveillance / Q000662:veterinary*; D014481:United States / Q000453:epidemiology