Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

Abstract

The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.