An investigation of the efficacy of different statistical models in malaria forecasting in the semi-arid regions of Gujarat, India.

Abstract

Robust forecasting of malaria cases is desirable as we are approaching towards malaria elimination in India. Methods enabling robust forecasting and timely case detection in unstable transmission areas are the need of the hour. Forecasting efficacy of the eight most prominent statistical models that are based on three statistical methods: Generalized linear model (Model A and Model B), Smoothing method (Model C), and SARIMA (Model D to model H) were compared using last twelve years (2008-19) monthly malaria data of two districts (Kheda and Anand) of Gujarat state of India. The SARIMA Model F was found the most appropriate when forecasted for 2017 and 2018 using model-building data sets 1 and 2, respectively, for both the districts: Kheda and Anand. Model H followed by model C were the two models found appropriate in terms of point estimates for 2019. Still, we regretted these two because confidence intervals from these models are wider that they do not have any forecasting utility. Model F is the third one in terms of point prediction but gives a relatively better confidence interval. Therefore, model F was considered the most appropriate for the year 2019 for both districts. Model F was found relatively more appropriate than others and can be used to forecast malaria cases in both districts.