Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation.

ABSTRACT: Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes' theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carol

SUBMITTER: Hadley E 

PROVIDER: S-EPMC8887758 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature