Predicting influenza waves with health insurance data
In recent years, many of the most developed countries have started to create influenza surveillance systems, because influenza still is very costly, not just concerning the health systems, but also economically. In most of these systems statistical methods are applied, unfortunately with rather moderate results. In contrast to statistical methods case-based reasoning explicitly uses former episodes. Because we already successfully applied our prognostic method, which combines case-based reasoning (CBR) with temporal abstraction, to kidney functions, we use it again to forecast influenza. Because health centers collect extensive laboratory data but their availability is usually delayed for at least two weeks, we use quickly available data from the main German health insurance scheme. In this article, we propose the use of CBR for influenza forecast and we show promising results.
influenza surveillance case-based reasoning time-series prediction