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An Agent-Based Computational Model of COVID-19 Vaccine Hesitancy

Hiryur, Kavya (2021) An Agent-Based Computational Model of COVID-19 Vaccine Hesitancy. Master Essay, University of Pittsburgh.

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Abstract

Vaccine hesitancy plays a huge role in the trajectory of the COVID-19 pandemic and the time it takes to reach herd immunity. A number of factors, including demographic and geographic characteristics, affect an individual’s likelihood to accept a vaccine. An agent-based model can be applied to simulate interactions and behavior change over time. In this study, the Health Belief Model, Transtheoretical Model, Dube Conceptual Model of Vaccine Hesitancy, and the WHO SAGE Vaccine Continuum were used as a foundation to build a conceptual model representing COVID-19 vaccine hesitancy. After collecting relevant data, the FRED agent-based modeling platform was used to simulate outcomes from 12/20/2020 to 3/20/2021 in Jefferson County, Pennsylvania (population of 45,000). Each agent’s initial vaccine propensity score, on a continuum between 0.0 and 1.0, assigned them susceptibility and transmissibility values for the competing behavior contagions “acceptance” (closer to 1.0) and “refusal” (closer to 0.0). Based on interactions with other agents, their susceptibility and transmissibility values were modified, to eventually impact the probability they would take the vaccine at the end of each modeled week. In Jefferson County, centering on a propensity score of 0.5, initial population vaccine propensity scores had the largest peaks between 0.30-0.34 and 0.59-0.63. About 15,000 agents took the vaccine after the first week, with ~53% of the population taking the vaccine by the end of the simulation. However, with greater incentive to vaccinate and propensity scores centered on 0.6, >20,000 agents took the vaccine after the first week, with approximately 65% taking the vaccine by simulation end. Contrastingly, with lesser incentive to vaccinate and propensity scores centered on 0.3, <5,000 agents took the vaccine after the first week, with about 14% taking the vaccine by simulation end. This model examined the complex dynamics of linked infectious and behavioral contagions. Results revealed how various factors play a role in vaccine hesitancy, and how agents can influence the behavior of other agents they come into contact with. The public health significance of this study is that the model allows stakeholders and policymakers to understand and evaluate the best methods to combat vaccine hesitancy in the population.


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Details

Item Type: Other Thesis, Dissertation, or Long Paper (Master Essay)
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hiryur, Kavyakyh18@pitt.edukyh18
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
Committee ChairBurke, Donalddonburke@pitt.edudonburkeUNSPECIFIED
Committee MemberBuchanich, Jeaninejeanine@pitt.edujeanineUNSPECIFIED
Committee MemberGlynn, Nancy W.epidnwg@pitt.eduepidnwgUNSPECIFIED
Date: 25 April 2021
Date Type: Completion
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 52
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Epidemiology
Degree: MPH - Master of Public Health
Thesis Type: Master Essay
Refereed: Yes
Date Deposited: 14 May 2021 19:01
Last Modified: 14 May 2021 19:01
URI: http://d-scholarship.pitt.edu/id/eprint/40813

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