Infectious diseases such as measles, morbillivirus, influenza, AIDS have caused millions of infecteds and deaths, great workforce lost and economical cost since the beginning of civilization. The spread dynamics of bacteria and viruses that cause infectious diseases through a population is carefully analysed to be able to effectively use intervention methods such as vaccination, quarantine, medicine, and etc. while considering scarce resources and costs. SIR (Susceptable-Infected-Recovered) compartmental model has been used to model spread dynamics of infectious diseases through a population, to predict total number of infected and death people and to calculate economical cost of diseases for roughly a century. In this study, we develop a software, YAYsim, coded in Python programming language, to be able to help decision makers and interested users to analyse results of an epidemic or pandemic by allowing them to change disease parameters as attack rates, recovery periods, the number of infected people at the beginning. YAYsim includes demografic information of each city in Turkey. Thus, it enables users to see how a pandemic affects on a selected city and to be able to decide based on their working area according to the results. Finally, an example study is carried out to estimate and evaluate rates of infected and death people during a possible H1N1 pandemic in Gaziantep city. It is observed that 35.8% of Gaziantep’s population are affected from the disease and 0.7% of them are death based on the disease parameters of 1918 Spanish Flu.
: January 16, 2020
|APA||Demi̇rbi̇lek, M . (2020). YAYsim: Salgın Modelleme ve Karar Destek Sistemi . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 7 (1) , 104-112 . DOI: 10.35193/bseufbd.675734|