Use our estimator to assess the financial impact of canceled surgeries during the pandemic

As such, Advisory Board recommends two options for planning teams to determine if they're on the downslope of their local Covid-19 curve.

Option 1: Build (and routinely adjust) your own model

Over the past few weeks, we've spoke with many hospital planners who are currently building analytical models that measure the spread of Covid-19 in their local communities. If possible, we recommend planners build a Susceptible, Infected, and Recovered (SIR) model. This mathematic model uses baseline hospitalization rate and transmissibility assumptions to divide the population into three groups: those vulnerable to infection; those who are currently infectious; and those gradually removed from the equation by recovery or death.

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To build a SIR model at the facility level, you'll need several localized inputs including regional population, market share of hospital beds, the current number of hospitalized Covid-19 patients within your region, doubling time before the current date, the number of infectious days, and the percentage reduction in social contact due to social distancing. Most recently, researchers at Penn Medicine built this online tool to help hospital planners build their own SIR projection. Although you'll need to make critical assumptions about the impact of social distancing, you can mitigate some of this vulnerability by regularly reevaluating the assumed percentage reduction in social contact in the context of changes to your local government or community behavior.

Option 2: Monitor these metrics

We recognize some systems may not be able to create and maintain a localized SIR model. If that’s the case for your system, we recommend adding these four community indicators to your metric dashboard. Although each metric comes with its own benefits and drawbacks, the combination can be a helpful way to identify your placement in your community's current curve.

1. Daily new confirmed cases as a rolling three-day average
2. • Pros: Incorporates the broadest range of recent data. In addition, the three-day average cushions against inevitable ebbs and flows.
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   • Con: Highly dependent on level of testing in region.
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   • What to look for: Decrease in daily new confirmed cases
3. Daily new confirmed deaths as a rolling three-day average
4. • Pro: Less dependent on testing availability while still cushioning against ebbs and flows.
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   • Con: Death toll numbers are likely underestimated throughout the country. This metric is also likely to lag behind your community's hospitalization rates.
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   • What to look for: Decrease in daily new confirmed deaths
5. Daily increase in hospitalizations as a rolling three-day average
6. • Pro: Minimally dependent on testing availability while still cushioning against ebbs and flows.
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   • Con: Only reflects the number of confirmed cases severe enough to warrant hospital admission—ignores patients who treat their mild symptoms at home.
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   • What to look for: Decrease in daily hospitalizations
7. Days for number of confirmed cases to double
8. • Pro: Easy to visualize exponential growth
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   • Con: Highly dependent on level of testing in your region
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   • What to look for: Increase in the doubling days

For the first three metrics, progress in your community will be marked by a measured decrease in the metric value. In contrast, an increase in the number of days required for the amount of confirmed cases to double indicates a deceleration in the pandemic's spread.

Your top resources for Covid-19 readiness

Advisory Board is collating our best-in-class research and tools to support members and non-members alike in tackling Covid-19. We’re updating this page regularly with our top resources on how to safely manage and prevent the spread of Covid-19.

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