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

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 th...

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Bibliographic Details
Published in:PloS one 2022-03, Vol.17 (3), p.e0264704-e0264704
Main Authors: Hadley, Emily, Rhea, Sarah, Jones, Kasey, Li, Lei, Stoner, Marie, Bobashev, Georgiy
Format: Article
Language:English
Subjects:
Age
Agent based models
Bayesian analysis
Biology and life sciences
Care and treatment
Comorbidity
Coronaviruses
COVID-19
COVID-19 - epidemiology
COVID-19 - therapy
COVID-19 diagnostic tests
Decision making
Decision theory
Diagnosis
Estimates
Hospital care
Hospitalization
Humans
Infections
Laboratories
Mathematical models
Medicine and Health Sciences
Models, Biological
Mortality
North Carolina - epidemiology
Pandemics
Parameter estimation
Parameter sensitivity
Patient outcomes
Patients
People and Places
Personal protective equipment
Physical Sciences
Population
Predictive Value of Tests
Prevention
Probability
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
Statistics
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Summary: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 Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0264704