The overall objective of this project is to use Bayesian approaches (Bayesian Networks and Naïve Bayes Classifiers) to develop tools for combining and interpreting the results of many SyS event detection algorithms from many diverse data. The goals of the project will be to develop and evaluate methods for:
· Estimating the likelihoods that specific diseases could be causing an epidemic in a population and ranking them according to the likelihood. Note: in this project the target population that we will be working with is the Swiss cattle population.
· Estimating the probability that an epidemic is ongoing within the livestock population under surveillance
· Estimating whether there may be an emerging (previously unknown) pathogen causing the epidemic and also providing some information about the characteristics of the unknown pathogen.
The methods that will be developed are planned to augment the information that is generated by a SyS system and to fit within the architecture of a livestock SyS system. Figure 1 is a schematic showing where the tools proposed for this project will fit within a generalized SyS architecture. Data from several databases (DB) are transported electronically to a preprocessing application that subdivides and formats the data into time series. The times series are then analyzed with event detection algorithms that evaluate whether there is a greater than expected number of cases (red dot = signal when greater than expected number of cases is detected) or not, in the most recent data. The results from all of the algorithms will be then analyzed by the tools developed by this project to produce a ranking of the most likely diseases that could be causing an epidemic, if an epidemic were occurring within the population.
The methods developed will incorporate expert knowledge and knowledge from the literature about the diseases under surveillance. Knowledge will be incorporated using scenario tree models (Martin, Cameron, & Greiner, 2007), expert opinion, prevalence estimates for endemic diseases, and combinations of these. Methods will be developed using a small number of cattle diseases as examples and a small number of time series covering the Swiss cattle population. Diseases proposed at this time include: Schmallenberg virus, Infectious Bovine Rhinotracheitis, Bovine Virus Diarrhea and Bovine Tuberculosis. It is expected that at the time the project starts there will be two databases available for the project, the ALIS and the TDV databases. Time series from the TDV database are currently being evaluated for cattle SyS under a current VPHI project (1.12.12) and time series form the ALIS database will be evaluated as they become available. It is the goal of the project to develop tools that will be scalable and will be able to handle large numbers of diseases and time series as needs change and new time series become available.
The methods will be developed in R(R Core Team, 2014). The R code and accompanying documentation will be transferred to the early detection group of the BLV for implementation. Training materials will be developed and BLV surveillance practitioners will be trained in the methods.