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When Data mining brings its force to modelling

The epidemiological field has made extensive use of modelling and simulation techniques for making decisions. Still nowadays the problem of capturing accurately the real characteristics of epidemics is considered hard for both epidemiologists and developers, for two main reasons: 1) Big data, 2) unknown information. In this research, we propose to combine data mining techniques with modelling. Simulation of an initial simple model exposes mismatches between the model output and the observed data. By analysing the observed data set, data mining techniques can extract pertinent information. This new information can be integrated with the initial simple model to enhance the study of epidemic. Applying this combination to a tuberculosis case study, leads us to provide much better results, which help to understand where the initial simulated model failed.

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