Climate based malaria forecasting system for Andhra Pradesh, India.

Abstract

Malaria is a major public health problem in tropical and subtropical countries of the World. During the year 1999, Visakhapatnam district of Andhra Pradesh, India experienced a major epidemic of malaria, and nearly 41,805 cases were reported. Hence, a retrospective malaria surveillance study was conducted from 2001 to 2016 and reported nearly a total of 149,317 malaria cases during the study period. Of which, Plasmodium vivax contributes 32%, and Plasmodium falciparum contributes 68% of the total cases. Malaria cases follow a strong seasonal variation and 70% of cases are reported during the monsoon periods. In the present study, we exploited multi step polynomial regression and seasonal autoregressive integrated moving average (SARIMA) models to forecast the malaria cases in the study area. The polynomial model predicted malaria cases with high predictive power and found that malaria cases at lag one, and population played a vital role in malaria transmission. Similarly, mean temperature, rainfall and Normalized Difference Vegetation Index build a significant impact on malaria cases. The best fit model was SARIMA (1, 1, 2) (2, 1, 1)12 which was used for forecasting monthly malaria incidence for the period of January 2015 to December 2016. The performance accuracy of both models are similar, however lowest Akaike information criterion score was observed by the polynomial model, and this approach can be helpful further for forecasting malaria incidence to implement effective control measures in advance for combating malaria in India.