Poster #146 - Estimating Time Discount Rate for Covid-like Diseases Vaccination: from a Discrete Choice Experiment in Wuhan

• In Event: Friday Poster Reception

Abstract

This study explores temporal preferences for COVID-like diseases (CLD) vaccination in three districts of Wuhan, China, a critical pandemic epicenter, through a discrete choice experiment (DCE). Unlike previous literature, which relied on stated-preference methods with limited real-world applicability, this research employs the DCE method to simulate realistic decision-making. The study uniquely combines DCE and machine learning algorithms to estimate and compare both hyperbolic and exponential discount rates for CLD vaccination, which fills critical gaps in using DCE to calculate temporal discount rates for vaccination. A survey targeting 1,000 residents will evaluate five attributes: waiting time, vaccine origin, efficacy, side effects, and cash incentives. A mixed logit model and discounting functions will analyze preference heterogeneity across demographic and health behavior groups. The findings aim to inform tailored policy interventions to shorten people’s time preference and improve pandemic control for future outbreaks. By understanding how people’s time preference for vaccination influences people’s vaccination decisions across the demographics and health behaviors. We can have policy implications, including region-specific incentives, cash incentives for high-discount groups, or streamlined vaccination logistics to optimize wait time. Note to Reviewers: Full results are not presented as data collection is ongoing, with surveys currently being distributed through a Wuhan-based platform. Preliminary findings will be available by the conference, offering initial insights into temporal preferences and policy implications.

Authors

• Yichao Jin, University of Texas, Dallas
  Presenting Author

• Dohyeong Kim, University of Texas, Dallas
  Non-Presenting Co-Author