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Home / Equine Research Bank / PLoS One / Using Bayes’ rule to define the value of evidence from...
PloS one2014; 9(11); e111335; doi: 10.1371/journal.pone.0111335

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.
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Publication Date: 2014-11-03 PubMed ID: 25364823PubMed Central: PMC4218722DOI: 10.1371/journal.pone.0111335Google Scholar: Lookup
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  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research proposes utilizing a statistical method, often employed in forensic investigations, to assess and present circumstantial evidence of a disease outbreak from syndromic surveillance. Fundamentally, this method leverages calculated ratios between the probability of observing a certain number of cases given an outbreak, and the same given no outbreak, to define the Value of Evidence, and employs Bayes’ rule to estimate the subsequent odds of an outbreak.

Adoption of Statistical Framework

  • The research suggests using a statistical method, commonly used in forensic evaluations, as an instrument for assessing and presenting indirect evidence of a disease outbreak gathered from syndromic surveillance.
  • The primary aim is to utilize the predicted distributions of reported cases to determine a ratio. This is calculated between the likelihood of observing a certain number of cases given a disease outbreak versus the likelihood of observing the same number of cases in the absence of an outbreak.

Defining the Value of Evidence

  • The ratio calculated as explained above defines what researchers call the ‘Value of Evidence’ (V).
  • Bayes’ rule is then applied to this ‘Value of Evidence.’ The pre-existing odds for an ongoing outbreak are multiplied by this value to produce the posterior odds.
  • This technique was tested using time series data on the number of horses exhibiting clinical respiratory or neurological symptoms.

Transparency and Application of the Approach

  • One of the noteworthy aspects of this approach is the clear separation it provides between pre-existing beliefs regarding the probability of an outbreak and the strength of evidence gathered from syndromic surveillance. This clear reasoning process is deemed to be helpful for decision makers.
  • The identified value of evidence can be translated into a verbal statement, similar to common practice within the forensic field, or used to develop risk maps.

Bayesian Approach and Integration of Data

  • Another significant advantage of a Bayesian approach is seamless data integration. This means that data from syndromic surveillance can be effortlessly combined with information from predictive modelling or from other sources such as disease introduction risk assessments.

Cite This Article

APA
Andersson MG, Faverjon C, Vial F, Legrand L, Leblond A. (2014). Using Bayes’ rule to define the value of evidence from syndromic surveillance. PLoS One, 9(11), e111335. https://doi.org/10.1371/journal.pone.0111335

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 9
Issue: 11
Pages: e111335
PII: e111335

Researcher Affiliations

Andersson, Mats Gunnar
  • Department of Chemistry, Environment and Feed Hygiene, The National Veterinary Institute, Uppsala, Sweden.
Faverjon, Céline
  • INRA UR346 Animal Epidemiology, VetagroSup, Marcy L'Etoile, France.
Vial, Flavie
  • Veterinary Public Health Institute, DCR-VPH, Vetsuisse Fakultät, Bern, Switzerland.
Legrand, Loïc
  • LABÉO - Frank Duncombe, Unité Risques Microbiens (U2RM), EA 4655, Normandie Universite, Caen, Normandy, France; Réseau d'EpidémioSurveillance en Pathologie Equine (RESPE), Caen, France.
Leblond, Agnès
  • Réseau d'EpidémioSurveillance en Pathologie Equine (RESPE), Caen, France; INRA UR346 Animal Epidemiology et Département Hippique, VetAgroSup, Marcy L'Etoile, France.

MeSH Terms

  • Algorithms
  • Animal Diseases / epidemiology
  • Animals
  • Bayes Theorem
  • Decision Making
  • Disease Outbreaks
  • Dogs
  • Forensic Medicine / methods
  • France / epidemiology
  • Horses
  • Population Surveillance

Conflict of Interest Statement

LL is an employee of LABÉO. LABÉO was funded by the Conseils Généraux du Calvados, de la Manche et de l\'Orne (County Councils). There are no patents, products in development or marketed products to declare. This does not alter the authors\' adherence to all the PLOS ONE policies on sharing data and materials.

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Citations

This article has been cited 5 times.
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