Sessional Space-Time Based Model for Infectious Disease Prediction
Erna Piantari, Iqdam Musayyad Rabbani, Rani Megasari

Universitas Pendidikan Indonesia

Abstract

Research on infectious diseases prediction has become a concern for many researches globally. This is realized because the outbreak of infectious diseases has a negative impact for on the quality of human life, including economic conditions. The aim of prediction of infectious diseases is to maximize preventive action so the large-scale outbreak of infectious diseases could be minimized. Although predicting the diseases is not an easy job, but with more data being collected and the progress of methods, this not impossible to do. In addition, some infectious diseases such as ARI (Acute Respiratory Infection)often occur in certain environmental conditions and certain time. So it has been realized that some occurrences of these diseases are seasonal and cannot be separated from the condition of time and location. Therefore, in this study we create a model to predict a potential incidence of infectious diseases based on space-time condition using STARIMA (Sessional Space-Time Autoregressive Moving Average).

Keywords: infectious diseases, STARIMA, space-time model

Topic: Computer Science