Forecast of the expected non-epidemic morbidity of acute diseases using resampling methods
In epidemiological surveillance it is important that any unusual increase of reported cases be detected as rapidly as possible. Reliable forecasting based on a suitable time series model for an epidemiological indicator is necessary for estimating the expected non-epidemic indicator and to elaborate an alert threshold. Time series analysis of acute diseases often use Gaussian autoregressive integrated moving average models. However, these approaches could be adversely affected by departures from the true underlying distribution. The objective of this paper is to introduce a bootstrap procedure for obtaining prediction intervals in linear models in order to avoid the normality assumption. We present a Monte Carlo study comparing the finite sample properties of the bootstrap prediction intervals with those of alternative methods. Finally, we illustrate the performance of the proposed method with a meningococcal disease incidence series.
|Date of creation:||Jul 2001|
|Date of revision:|
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- Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2001.
"Effects of parameter estimation on prediction densities: a bootstrap approach,"
International Journal of Forecasting,
Elsevier, vol. 17(1), pages 83-103.
- Ruiz, Esther & Romo, Juan & Pascual, L., 1999. "Effects of parameter estimation on prediction densities a bootstrap approach," DES - Working Papers. Statistics and Econometrics. WS 6304, Universidad Carlos III de Madrid. Departamento de Estadística.
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