Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya

Abstract

Tuberculosis (TB) infections among children (below 15 years) is a growing concern, particularly in resource-limited settings. However, the TB burden among children is relatively unknown in Kenya where two-thirds of estimated TB cases are undiagnosed annually. Very few studies have used Autoregressive Integrated Moving Average (ARIMA), and hybrid ARIMA models to model infectious diseases globally. We applied ARIMA, and hybrid ARIMA models to predict and forecast TB incidences among children in Homa Bay and Turkana Counties in Kenya. The ARIMA, and hybrid models were used to predict and forecast monthly TB cases reported in the Treatment Information from Basic Unit (TIBU) system by health facilities in Homa Bay and Turkana Counties between 2012 and 2021. The best parsimonious ARIMA model that minimizes errors was selected based on a rolling window cross-validation procedure. The hybrid ARIMA-ANN model produced better predictive and forecast accuracy compared to the Seasonal ARIMA (0,0,1,1,0,1,12) model. Furthermore, using the Diebold-Mariano (DM) test, the predictive accuracy of ARIMA-ANN versus ARIMA (0,0,1,1,0,1,12) model were significantly different, p<0.001, respectively. The forecasts showed a TB incidence of 175 TB cases per 100,000 (161 to 188 TB incidences per 100,000 population) children in Homa Bay and Turkana Counties in 2022. The hybrid (ARIMA-ANN) model produces better predictive and forecast accuracy compared to the single ARIMA model. The findings show evidence that the incidence of TB among children below 15 years in Homa Bay and Turkana Counties is significantly under-reported and is potentially higher than the national average. Author summary Tuberculosis remains a disease of major public health concern especially in resource limited settings. Despite this, tuberculosis is still characterized by high morbidity and mortality from a single infectious disease, particularly among children in developing countries. The actual burden of tuberculosis among children is relatively unknown and about two-thirds of cases are either unreported or undiagnosed in Kenya. The use of novel mathematical models is critical and can be leveraged to guide policymakers in the prevention and control of infectious diseases such as tuberculosis. We use autoregressive moving average and hybrid forms of these models to model and forecast tuberculosis infections among children. We found out that hybrid autoregressive moving average models provide more accurate predictions and forecasts of tuberculosis infections among children. We also found out and confirmed that the actual burden of tuberculosis among children is still under-estimated. Our study highlights on the ever existing gap in the under-estimation of tuberculosis among children and points to the importance of novel modelling methods in the understanding of the actual burden of tuberculosis among children