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Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers

Briët, Olivier J. T. and Amerasinghe, Priyanie H. and Vounatsou, Penelope. (2013) Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PloS one, Vol. 8, H. 6 , e65761.

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Official URL: http://edoc.unibas.ch/dok/A6146254

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Abstract

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.; 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.; The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial 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.; 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.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Health Systems Research and Dynamic Modelling > Dynamical Modelling (Smith)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics > Bayesian Modelling and Analysis (Vounatsou)
UniBasel Contributors:Briët, Olivier and Vounatsou, Penelope
Item Type:Article, refereed
Bibsysno:Link to catalogue
Publisher:Public Library of Science
ISSN:1932-6203
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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Last Modified:31 Dec 2015 10:53
Deposited On:16 Aug 2013 07:30

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