Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study^*

Abstract

Objective

To evaluate the predictive power of different malaria case time-series models in the state of Amapá, Brazil, for the period 1997-2016.

Methods

This is an ecological time series study with malaria cases recorded in the state of Amapá. Ten deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons.

Results

The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. It was possible to predict future malaria cases 6 and 12 months in advance.

Conclusion

The ARIMA model is recommended for predicting future scenarios and for earlier planning in state health services in the Amazon Region.

Time Series Studies; Malaria; Decision Support Techniques; Epidemiological Monitoring; Forecasting