Predicting Epidemic Diseases using Mathematical Modelling of SIR

Abstract

Epidemic diseases such as Tuberculosis (TB), AIDS (Acquired Immune Deficiency Syndrome) and CCHF (Crimean-Congo Hemorrhagic Fever) remain as a major global health problem. For example, in 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease reported by WHO (including 320 000 deaths among HIV (human immunodeficiency virus) positive people) in the world. However, the presence of immunodeficiency such as in HIV positive cases helps TB to occur and to be contagious. Hence, to decrease the number of patients with TB lessens the socioeconomical burden, and, to prevent the people from TB as well as TB/HIV and MDR-TB (multi-drug-resistant tuberculosis) are of importance. Taking appropriate precautions in fighting epidemic diseases begins primarily with making predictions. In this respect, although the diagnosis and cure are known for some epidemic diseases, it is evident that a fighting program must depend on predictable cases. Therefore an investigation on an epidemic disease in framework of the mathematical modelling is indispensable and can potentially lead to better ways to analyze, forecast, and prevent epidemics. In this study, to help with all these concerns, we aimed to predict the effects of epidemic of TB including HIV positive patients, as well as of AIDS and CCHF in terms of number of infected people in Turkey by using the mathematical modelling of SIR. Here, we showed that SIR (susceptible-infected-recovered) Model can be used for such epidemic diseases.