ON THE USE OF BAYESIAN PROBABILITY NETWORKS WITH A REVIEW OF MALARIA CASE

Abstract

In spite of the versatile and general acceptability of estimation of disease cases from the various available methods in literature, incorporating model uncertainty remains an open issue. In this article, we derived a probability based graphical model using expert opinions in related studies on malaria and its hypothesized predictors with a Bayesian belief network (BBN). This approach is well applied in ecological studies and other environmental sciences in recent times for various estimations and predictions based on Bayesian reasoning. We gave a brief description of a BBN framework, its pros and cons, examine the principle of conditional independence. Also, we explore Markov Chain principles as it relates to a BBN formulation and useful guidelines for developing the preliminary structure of the network. We finally derived the topology of a BBN as a directed acyclic graph with malaria predictors as network nodes. We also illustrated the use of the network with an illustrative example.