Applying knowledge discovery to predict infectious disease epidemics

Predictive modelling, in a knowledge discovery context, is regarded as the problem of deriving predictive knowledge from historical/temporal data. Here we argue that neural networks, an established computational technology, can efficaciously be used to perform predictive modelling, i.e. to explore the intrinsic dynamics of temporal data. Infectious-disease epidemic risk management is a candidate area for exploiting the potential of neural network based predictive modelling—the idea is to model time series derived from bacteria-antibiotic sensitivity and resistivity patterns as it is believed that bacterial sensitivity and resistivity to any antibiotic tends to undergo temporal fluctuations. The objective of epidemic risk management is to obtain forecasted values for the bacteria-antibiotic sensitivity and resistivity profiles, which could then be used to guide physicians with regards to the choice of the most effective antibiotic to treat a particular bacterial infection. In this regard, we present a Web-based Infectious Disease Cycle Forecaster (IDCF), comprising a number of distinct neural networks, that have been trained on data obtained from longterm clinical observation of 89 types of bacterial infections, being treated using 36 different antibiotics. Preliminary results indicate that IDCF is capable of generating highly accurate forecasts given sufficient past data on bacteria-antibiotic interaction. IDCF features a client-server based WWW interface that allows for remote projections to be requested for and displayed over the Internet.