Reuse of Bayesian Networks: A Case-study in Classical and African Swine Fever
Summary
Developing a Bayesian Network has a high workload, also for domain experts, when not enough
data is available to learn the model. We aim to reduce this workload by reusing an existing
Bayesian Network when developing a new network. We study this by developing an initial
model for African Swine Fever (ASF) by reusing the already existing Classical Swine Fever
(CSF) model. African Swine Fever is a highly contagious disease, which is currently present in
Poland and the Czech Republic. The risk of contamination in the Netherlands is substantial,
and especially because no vaccine is available, a quick diagnosis is essential. Therefore, we
developed a Bayesian Network to support early detection of the disease without having to wait
for lab results.
The existing model for CSF consists of fi?ve phases, each representing a part of the body
affected. These phases are used as a base, on which to build the reused model. The initial
structure of the ASF model is determined, using only literature, very limited expert interviews
and data of inoculation studies. When learning the parameters of the model, the probabilities
of the CSF model where reused where possible. The remaining conditional probability tables
are determined by using a variant of the EM algorithm.
The resulting network displays how a good initial model can be made in signifi?cant less time
compared to developing a new one.