GREENHOUSE GASES ANALYSIS AND IMPROVEMENT OF EXPONENTIAL GREY MODEL RESULTS USING THE MARKOV PROPERTY: A CASE STUDY OF TUBERCULOSIS IN KAZAKHSTAN

Abstract

The study focuses on analysis of tuberculosis (TB) which remains top cause of diseases in the world, especially in developing countries. Unfortunately, this is also the case for Kazakhstan. Grey Relational Analysis (GRA) is used to examine the relationship of greenhouse gas emissions, which are considered to be one of the factors negatively affecting the disease, to TB. In addition, a new approach is introduced to forecast TB cases in Kazakhstan in the coming years. This way, the study sets two objectives, first of which is analyzing the relationship between tuberculosis and greenhouse gases using GRA, namely Deng’s Degree Grey Incidence Analysis (DGIA), Absolute Degree (ADGIA), and Second Synthetic Degree (SSDGIA), where the latter is an average of the previous two. The second objective lies in introducing Exponential Grey - Markov Model, which gave better accuracy compared to Grey - Markov Model due to the fact that Exponential Grey Model in general results in more accurate outcomes compared to Grey Model. The approach was used to forecast the tuberculosis cases for Kazakhstan for 2021-2025. The results showed that decreasing trend of TB is expected to continue and that PM2.5 had the highest level of relation to tuberculosis in Kazakhstan.