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

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

Combining and aggregating environmental data for status and trend assessments: challenges and approaches

Increasingly, natural resource management agencies and nongovernmental organizations are sharing monitoring data across geographic and jurisdictional boundaries. Doing so improves their abilities to assess local-, regional-, and landscape-level environmental conditions, particularly status and trends, and to improve their ability to make short-and long-term management decisions. Status monitoring assesses the current condition of a population or environmental condition across an area. Monitoring for trends aims at monitoring changes in populations or environmental condition through time. We wrote this paper to inform agency and nongovernmental organization managers, analysts, and consultants regarding the kinds of environmental data that can be combined with suitable techniques and statistically aggregated for new assessments. By doing so, they can increase the (1) use of available data and (2) the validity and reliability of the assessments. Increased awareness of the difficulties inherent in combining and aggregating data for local-and regional-level analyses can increase the likelihood that future monitoring efforts will be modified and/or planned to accommodate data from multiple sources