COVID-19 Vaccination Prioritization Strategies Evaluated with Contact Survey Data

A dataset supporting the evaluation of primary and booster COVID-19 vaccine prioritization strategies by age and high-contact employment status. The analysis uses a mathematical transmission model and contact data from the Berkeley Interpersonal Contact Survey to compare five different roll-out strategies. The research was conducted by Ethan Roubenoff at the University of California, Berkeley.

Use Cases

• Compare mortality reduction across different vaccination prioritization strategies based on the described model outputs.
• Analyze trade-offs between preventing deaths and preventing clinical infections based on the prioritization of different demographic groups.
• Model the impact of booster dose strategies for new SARS-CoV-2 variants based on the described epidemiological parameters.

Strengths

• Analysis is based on uniquely detailed contact data from the Berkeley Interpersonal Contact Survey.
• Model evaluates five distinct vaccination prioritization strategies for both primary and booster doses.
• Results include specific percentage reductions in deaths and clinical infections for different strategies.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of California, Berkeley

Collection Method

Retrospective analysis using a mathematical model and survey data from the Berkeley Interpersonal Contact Survey.