276. Understanding COVID Vaccine Hesitancy via System Dynamics Modeling and Health Belief Model
Invited abstract in session MC-4: Analytics for Mis/Disinformation in Healthcare, stream Regular talks.
Monday, 11:00-12:30Room: Room S3
Authors (first author is the speaker)
| 1. | Nasim Sabounchi
|
| Health Policy and Management, CUNY Graduate School of Public Health and Health Policy | |
| 2. | Justine Maffei
|
| CUNY Graduate School of Public Health and Health Policy | |
| 3. | Rachel Thompson
|
| Center for Systems and Community Design, CUNY Graduate School of Public Health and Health Policy | |
| 4. | Terry T.-K. Huang
|
| Health Policy and Management, CUNY Graduate School of Public Health and Health Policy | |
| 5. | Ozgur Araz
|
| Supply Chain Management and Analytics, University of Nebraska Lincoln | |
| 6. | Mahdi Najafabadi
|
| CSUN | |
| 7. | David Lounsbury
|
| Albert Einstein School of Medicine | |
| 8. | Denis Nash
|
| CUNY Graduate School of Public Health and Health Policy | |
| 9. | Turner Canty
|
| CUNY Graduate School of Public Health and Health Policy |
Abstract
The purpose of this presentation is to demonstrate how psychological, socio-economic, and health policy factors influence the COVID vaccine acceptance and hesitancy in the U.S. through the application of system dynamics (SD) modeling and the health belief model (HBM). HBM constructs including perceived threat, perceived benefits, perceived barriers, and perceived self-efficacy can predict individuals’ vaccine intention with reasonable accuracy. These essential constructs also capture how misinformation affects vaccine hesitancy and acceptance. Building upon HBM constructs, we have developed a simulation platform which is validated against actual reported perceptions towards COVID-19 vaccine over time by using the data trends collected from the CHASING COVID Cohort Study, a national, community based prospective cohort study of 6,745 U.S. adults. We use the SD simulation model to compare scenarios to predict long-term dynamics with the goal of better understanding the determinants of vaccine intention among different sociodemographic groups of people, and to get insights about more successful public health initiatives in reducing vaccine skepticism and hesitancy through a closed-loop simulation model.
Keywords
- Modelling and simulation
- Healthcare policy modelling
Status: accepted
Back to the list of papers