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

Highlights

• We constructed a tool to estimate healthcare demand stemming from the COVID19 pandemic.

• The tool can help local authorities examine the impacts of intervention strategies.

• We apply the model to three different regions in Chile to illustrate its use.

• A surge in COVID-19 patients could overwhelm Chilean hospitals by June.

• Immediate aggressive action can avert much of the morbidity and mortality

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.