A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions

Abstract

Objectives: Public health officials need tools to assist in anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We constructed a modeling tool to aid active public health officials to estimate healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impact of population-wide social-distancing interventions.

Methods: The tool uses an SEIR compartmental model to project the pandemic's local spread. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention, and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile.

Results: Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at six to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals >70 years, and telework interventions may keep treatment demand below capacity.

Conclusions: Aggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.

Keywords: COVID; Capacity; Hospital; Intervention; Model; Social distancing.

Figure 1

Projected occupancy demands and capacity for hospital (non-ICU) beds in Región Metropolitana with and without intervention. Notes. Solid curves: projections using the high estimate for the reproduction number. Dashed curves: projections using the low estimate for the reproduction number. Table 1 contains all reproduction numbers. Horizontal red line: Hospital bed capacity. Blue shaded region: interventions in place.

Figure 2

Sensitivity analysis: Effects of the duration of intervention Strategy 2 (case isolation, home quarantine, social distancing of population >70 years of age, and telework) on hospital bed occupancy demands during the COVID-19 epidemic in Región Metropolitana when maintained for two (A), four (B), six (C), and eight (D) months (and initiated on April 1, 2020). Notes: Solid and dashed curves reflect uncertainty in the effectiveness of intervention strategies (Table 1).

Figure 3

Sensitivity Analysis: Effects of a 2 month Lockdown Suppression Strategy alone (A) and followed by various mitigation strategies for 6 months on Hospital Bed Occupancy Demands: Closing Schools and Universities + Telework (B), Case Isolation + Household Quarantine (C), and Case isolation, Household Quarantine, Social Distancing of >70 years of age, and Telework (D). Notes: Solid and dashed curves reflect uncertainty in the effectiveness of intervention strategies during both the Lockdown period and Post-lockdown intervention period per Table 1.