title: Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. manwar6@jhu.edu.
authors.forename: Mohammad Y
authors.initials: MY
authors.lastname: Anwar
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Joseph A
authors.initials: JA
authors.lastname: Lewnard
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Sunil
authors.initials: S
authors.lastname: Parikh
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Virginia E
authors.initials: VE
authors.lastname: Pitzer
mesh_terms: D000346:Afghanistan / Q000453:epidemiology; D002980:Climate; D019353:Endemic Diseases; D004777:Environment; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical
keywords: Afghanistan; Autoregressive model; Environment; Malaria; Prediction