title: Using Bayes' rule to define the value of evidence from syndromic surveillance.
abstract: In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.
authors.affiliation: Department of Chemistry, Environment and Feed Hygiene, The National Veterinary Institute, Uppsala, Sweden.
authors.forename: Mats Gunnar
authors.initials: MG
authors.lastname: Andersson
authors.affiliation: INRA UR346 Animal Epidemiology, VetagroSup, Marcy L'Etoile, France.
authors.forename: Céline
authors.initials: C
authors.lastname: Faverjon
authors.affiliation: Veterinary Public Health Institute, DCR-VPH, Vetsuisse Fakultät, Bern, Switzerland.
authors.forename: Flavie
authors.initials: F
authors.lastname: Vial
authors.affiliation: LABÉO - Frank Duncombe, Unité Risques Microbiens (U2RM), EA 4655, Normandie Universite, Caen, Normandy, France; Réseau d'EpidémioSurveillance en Pathologie Equine (RESPE), Caen, France.
authors.forename: Loïc
authors.initials: L
authors.lastname: Legrand
authors.affiliation: Réseau d'EpidémioSurveillance en Pathologie Equine (RESPE), Caen, France; INRA UR346 Animal Epidemiology et Département Hippique, VetAgroSup, Marcy L'Etoile, France.
authors.forename: Agnès
authors.initials: A
authors.lastname: Leblond
mesh_terms: D000465:Algorithms; D000820:Animal Diseases / Q000453:epidemiology; D000818:Animals; D001499:Bayes Theorem; D003657:Decision Making; D004196:Disease Outbreaks; D004285:Dogs; D005554:Forensic Medicine / Q000379:methods; D005602:France / Q000453:epidemiology; D006736:Horses; D011159:Population Surveillance