Preditive Models And Health Sciences: A Brief Analysis

Background: Predictive Models are an important tool in event predicting and health planning. Despite this, there are few works focusing this area. Thus, the analysis of the real benefits of these models in Health Sciences is necessary to be performed. Results: Predictive techniques largely evolved in second half of XX century. The development of AR, MA, ARMA, ARIMA and SARIMA models, through Box-Jenkins methodology, constitute a robust conjunct of mechanisms able to help in solution of epidemiological modeling problems, mainly in Health Sciences, once it allows to evaluate individual characteristics of living beings and its correlation with pathologies in the same space-time. Nevertheless, AR, MA and ARMA does not have tendency in seasonality, which weakens the analysis. Conclusions: To predict the natural history of endemic/epidemic and its health-disease processes in a determined population is a sine que non condition to its adequate management in Public Health context and in adoption of affirmative measures concerning health promotion. Thus, the predictive models, with emphasis in ARIMA, SARIMA, Artificial Neural Networks and Formalism of Copulas are alternatives that can be feasible.