Since the first case in November 2019, COVID-19 has spread fast, infected many people, and taken many lives. Once COVID-19 cases are confirmed, and hospital treatments are required, they will need high costs yet still be at risk of death. Therefore, insurance companies and social service providers need a reliable model to estimate the expenses and ensure their financial health. This study discusses the modeling of time-until-release and time-until-death since cases are confirmed, using the South Korean data. Doubledecrement model is assumed to follow Weibull and log-logistic hazard for time-until-release and time-until-death, respectively. Considered risk factors are sex, symptoms, age group, and the month when the case is confirmed. The nonlinear survival regression model is proposed, with the Solver function in Microsoft Excel for parameter estimation. The results suggest that virus lifespan and mortality risk get lower over time. Time-until-death is also lower, implying that we have less time to save lives from mortality risk. More frequent testing with faster results and not waiting for the symptoms to occur is needed for people under thirty years old due to shorter time-until-death and those at sixty and above due to higher case fatality rates.
|Number of pages||6|
|Journal||International Journal on Advanced Science, Engineering and Information Technology|
|Publication status||Published - 2022|
- Pandemic modeling
- Survival regression
- Time series modeling