Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers
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Briet, O. J. T.; Amerasinghe, Priyanie H.; Vounatsou, P. 2013. Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PLoS One, 8(6):e65761-e65761. doi: http://dx.doi.org/10.1371/journal.pone.0065761
Permanent link to this item: http://hdl.handle.net/10568/40218
Introduction: With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during ''consolidation'' and ''pre-elimination'' phases. Methods: Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results: The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negativebinomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions: G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.