Forecasting of Malaria in India Using Time Series Analysis


  • T Muthukumar
  • R Karnaboopathy
  • K Annadurai
  • TK Raja



Univariate time series, Malaria, ARIMA, BIC, Forecasting


Background: Although there has been marked reduction in the number of malaria cases in India under National Vector Borne Disease Control Programme (NVBDCP), malaria still is the leading cause of infectious diseases with the development of drug resistant Plasmodium species and insecticide resistant mosquitoes. Keeping this in view, the present study attempts to forecast the malaria cases in India. Methods: We adopted an Auto Regressive Integrated Moving Average (ARIMA) models on the data collected on the number of malaria cases from 1990 to 2017 .The same has been used to predict the number of cases till 2025 without any additional intervention. Results: The results also showed a decreasing trend in the actual and forecasted numbers of malaria cases. The appropriate ARIMA (10, 1, 9) model was selected based on Bayesian Information Criteria (BIC) values. Conclusions: Hence, to achieve the target of Sustainable Developmental Goal by 2030, additional interventions with an increase in the intensity of existing interventions and support of the international community along with WHO is essential.




How to Cite

Muthukumar , T. ., Karnaboopathy , R., Annadurai , K., & Raja , T. (2019). Forecasting of Malaria in India Using Time Series Analysis. Journal of Comprehensive Health, 7(2), 16–22.



Original Article