Estimating the COVID-19 Death Counts Using a Hesitant Fuzzy Linear Regression Depend on Race, Age and Location

Abstract

The COVID-19 pandemic that has struck the world has caused social and economic problems in people's lives. Many countries are trying to reduce the impact of the pandemic by taking precautions to prevent the spread of the virus and reduce the number of deaths. Despite the precautions, many people have contracted the virus and a significant number have lost their lives. This suggests that some factors about the exact mechanism of how people contract the virus and get sick are still unclear. Therefore, many researchers wonder if there are other factors that make people more susceptible to the COVID-19 virus. In this study, hesitant fuzzy linear regression (HFLR) models are applied depending on variables such as age, race, and place of residence that are thought to influence COVID-19 deaths. HFLR provides an alternative approach to statistical regression for modeling situations with incomplete information. The models include input and output variables as hesitant fuzzy elements (HFEs). The relationship between the considered variables and the number of deaths is examined using data related to people living in different states of the United States. In addition, HFLR is used as an estimation model due to the uncertainty in the data obtained from the Centers for Disease Control and Prevention (CDC). The proposed HFLR models are used both to estimate COVID-19 deaths and to determine the effects of selected variables on COVID-19 deaths. In this way, countries can estimate the risk of death for individuals given these factors and determine what precautions to take for high-risk groups.