Harvard Global Health Institute

Data cited at: https://globalepidemics.org/our-data/hospital-capacity/   Notes: HGHI launches regionalized capacity estimates.  We built this model to provide the public, health care leaders and policy makers with a better understanding of hospital bed capacity across the United States. We have run the numbers not just nationally, but for each Hospital Referral Region — 305 local hospital markets in the country. By making this information available on such a granular level, we can showcase which regions will be particularly stressed as coronavirus infections rise, and an more and more people need to be hospitalized. Our model offers two main levers framing the bed capacity outcomes: Users can select the percentage of the population that will be infected with SARS-CoV-2, and they can select in which time frame this percentage of the total population of the selected region will be infected. For example, our pre-set when first clicking on a Hospital Referral Region shows hospital bed capacity for a scenario in which x percent of that region’s population are infected in y months. When we first published our model in collaboration with ProPublica, we chose nine main scenarios on which to focus: A population infection rate of 20 percent, 40 percent, or 60 percent, each modeled over either 6, 12 or 18 months. Our original infection rate scenarios are based on estimates by leading epidemiologists such as Harvard’s Mark Lipsitch, who predicts that 20 to 60 percent of the population will get infected with the novel coronavirus over the course of the pandemic. Our original time stamps – 6, 12 or 18 months – are based on the assumption that it will take about 18 months for a vaccine to be widely available (if there will be a vaccine.) These scenarios allow us to explore the impact of mitigation efforts such as physical distancing — which help us  keep the infection rate lower for longer — up to the earliest time point when we could have an additional measure — the vaccine or a treatment — to bring down hospitalizations due to COVID-19.