A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

Area of Science:

• Healthcare Management
• Epidemiological Modeling
• Health Systems Research

Background:

• The COVID-19 pandemic has severely strained global healthcare systems, leading to critical resource shortages.
• Existing epidemiological models often lack direct translation into actionable hospital service requirements, such as bed occupancy.
• Hospital managers require effective tools to anticipate patient influx and manage bed capacity, including intensive care unit (ICU) beds.

Purpose of the Study:

• To develop and validate a predictive tool for forecasting COVID-19 patient hospital bed occupancy.
• To assist healthcare managers in making informed decisions regarding resource allocation and capacity management.
• To ensure sustained access to hospitalization and ICU services for confirmed COVID-19 cases.

Main Methods:

• Utilized a discrete event simulation approach.
• Employed archetypes, derived from empirical analysis of actual patient trajectories, to model disease progression and resource utilization.
• Enabled fitting archetypes to regional data or specific viral variants with minimal data.

Main Results:

• Demonstrated the accuracy of the predictive tool through numerical experiments on realistic scenarios.
• Showcased the tool's capability to support daily decision-making for hospital managers.
• Validated the tool's effectiveness in maximizing bed availability and ensuring patient access to necessary services.

Conclusions:

• The proposed simulation tool accurately predicts hospital bed occupancy for COVID-19 patients.
• This tool empowers healthcare managers to optimize resource management and enhance patient care delivery.
• Effective capacity planning is crucial for maintaining healthcare system resilience during pandemics.