Integrating AHP and TBATS for Infectious Disease Prioritization and Forecasting in East Java

Budi Fajar Supriyanto; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi

doi:10.47709/cnahpc.v7i4.7151

Authors

Budi Fajar Supriyanto Politeknik Negeri Jember
Salihati Hanifa Politeknik Negeri Jember
Nesa Ayu Murthisari Putri Politeknik Negeri Jember
Titin Andriyani Atmojo Politeknik Negeri Jember
Waridad Umais Al Ayyubi Politeknik Negeri Jember

DOI:

https://doi.org/10.47709/cnahpc.v7i4.7151

Keywords:

Agribusiness and Health Risk, Analytic Hierarchy Process (AHP), Precision Public Health, Public Health Surveillance, Spatial Epidemiology, TBATS Time Series Forecasting

Abstract

Agrarian regions like East Java province face complex public health challenges. Some cases are caused by the interaction between social factors, and others by agribusiness factors. An integrative approach is needed to understand the dynamics of disease cases. This study aims to analyse the disease with the highest number of cases and project case trends in East Java using an integrated quantitative approach. Using methods such as the Analytic Hierarchy Process (AHP) to determine disease weights, the TBATS model is used to project case trends through 2028. Standardised multiple regression models were used to assess the influence of social factors (population density, poverty) and agribusiness (rice harvest area, agricultural labour). The data used are secondary time-series data from 2013 to 2023 obtained from BPS, the Health Department, and BMKG. The AHP results show diarrhoea as the disease with the highest weight (0.494), followed by pneumonia (0.112), tuberculosis (0.090), malaria (0.051), and dengue fever (0.049). The TBATS projection indicates medium-term fluctuations with the potential for an increase in dengue fever cases. Meanwhile, the regression results show that people in the agricultural sector are at increased risk of malaria (p = 0.037), while other variables have an influence but are not significant. Therefore, integrating health, social, and agribusiness data is an urgent need. And it can be used for early disease warning systems and more precise public health policy strengthening.

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