Bayesian approaches for combining and interpreting the results of event detection algorithms from many varied real time data sources
Contact Person: John Berezowski
Duration: 2016 - 2019
This project builds upon the results of previous research that supports the development of an early detection system for diseases of livestock in Switzerland. One component of the early detection system is a syndromic surveillance (SyS) system that will, as data sources become available, monitor many time series from multiple livestock data sources in one system. Analyzing many time series on one system has many advantages including providing more information about the population. However there are challenges and interpreting the results of many event detection algorithms will require additional methods. Since SyS is continuous and aims to be real time (or near real time) these methods will need to be automated so they can be run frequently; as new data becomes available, ultimately on a daily basis.
Bayesian Networks and Naïve Bayesian Classifiers are methods that have been shown to perform well when making inferences from many data. These methods can also be used to combine data with other forms of knowledge, such as expert knowledge, and can be updated as new data becomes available (Witten). They have been used in public health and animal health surveillance. This project will adopt a Bayesian approach to interpreting the results of many event detection algorithms. To enhance the method, knowledge about livestock diseases (from experts and the literature) and the characteristics of the time series under surveillance (from BLV early detection team members) will be incorporated into the method.
The first deliverable will be an automated tool that calculates likelihood estimates for diseases under surveillance and ranks them according to the likelihood that they may be causing an epidemic in the population. Other deliverables will be enhancements to the tool to estimate the likelihood that there is an epidemic ongoing, and to estimate the likelihood that the epidemic is due to a previously unseen emerging disease. The method will be designed to run on data that are available to the BLV early detection system. It is expected that by the start date of the project, available databases will include the ALIS and TVD databases. The method will be piloted in cattle, and on a small number of diseases. The project will be a collaborative effort between the Veterinary Public Health Institute (VPHI) and the BLV early detection group, thereby ensuring that the project outputs are a useful addition to livestock surveillance in Switzerland.
