Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence. (December 2016)

• Record Type: Journal Article
• Authors: Anwar, Mohammad; Lewnard, Joseph; Parikh, Sunil; Pitzer, Virginia
• Is Part Of: Malaria journal. Volume 15:Number 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Malaria -- Prediction -- Afghanistan -- Environment -- Autoregressive model; Malaria -- Periodicals; 616.9362
• DOI: 10.1186/s12936-016-1602-1
• Languages: English
• ISSNs: 1475-2875

Abstract

Abstract Background Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. Methods This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Results Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Conclusion Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.