Blog Post

When will Covid-19 peak for your state? Here are 5 considerations for using IHME's model to prepare.

April 7, 2020

    Epidemiologists at the Institute for Health Metrics and Evaluation (IHME) recently released a forecast to estimate the impact of Covid-19 on bed-days, ICU-days, ventilator-days, and deaths for each U.S. state. As hospitals plan for future resource needs, volume, and budgets, this model could provide important insight into when your state will be at maximum crisis levels, and when you could potentially start to transition to normal business operations.

    Your Covid-19 checklist to expand capacity

    Why IHME's model is different

    The model—which has been cited by White House coronavirus response coordinator, Deborah Birx, and many other state officials, such as New York Gov. Andrew Cuomo (D)—is unlike other epidemiological models for Covid-19 in that it uses reported deaths rather than case counts as the basis of forecasts. This approach allows the IHME researchers to account for the likely underrepresentation of actual case counts due to asymptomatic patients and/or shortages of tests. Deaths are less likely to be underreported, the authors argue.

    Additionally, the model considers behavioral and policy changes such as school and business closures that affect death rates, which many other models have omitted. The modelers adjust death rates based on the date that a given state implemented social distancing guidelines.

    The forecasts are regularly updated based on new reported data. Here are some highlights from IHME's most recent update on April 5th:

    • Total projected U.S. Covid-19 deaths (first wave): 93,531;
    • Projected date of peak hospital use nationally: April 15;
    • Projected number of invasive ventilators needed nationally at peak: 18,992; and
    • Projected number of hospital beds needed nationally at peak: 140,823.

    Some critics argue that IHME's estimates are overly optimistic. Indeed, any model is only as good as the assumptions that go into it. The IHME researchers acknowledge this fact, saying, "Uncertainty in the time course of the epidemic, its duration, and the peak of utilization and deaths is large this early in the epidemic."

    How hospital planners can use the IHME model

    However, the IHME model may at least provide a starting place for hospital planners, as it is perhaps the most well-developed state-by-state forecast available at the moment. Here are five considerations for localizing IHME's projections:

    1. Evaluate adherence to social distancing practices in your community. IHME researchers used data from Wuhan, China; Italy; and Spain to estimate how stay-at-home orders and closures of non-essential locations decelerated rates of death post-implementation. This approach assumes that the United States will exhibit the same level of adherence to these guidelines as its international counterparts have done. As a starting point, consult online social distancing scorecards, such as this one from Unacast, to see how your region is faring compared with others.

    2. Adjust state-wide estimates to account for community dynamics impacting transmissibility. The density of a population and the availability of public transit and other shared spaces can greatly affect how quickly the virus spreads. For instance, rural areas may have shortened or delayed timeframes to move through their epidemic curves compared with state-wide estimates.

    3. Assess the efficacy—and speed—of local social distancing policies and containment efforts compared with state-wide performance. Areas within a state that took early action to institute social distancing guidelines and expand access to testing may have been better able to contact trace, and isolate individuals compared with the state as a whole, shortening the disease curve.

    4. Coordinate with supply chain experts to project possible reprieves from equipment shortages. As manufacturers ramp up production of supplies, hospitals may be able to resume elective and non-urgent procedures before completely moving through their disease curve. However, this projection assumes hospitals are able to create safe environments apart from infected individuals, do not have space or staff constraints, and do not have state guidelines prohibiting these procedures from taking place.

    5. Recognize unknowns that may prolong or shorten epidemic curves, such as travel patterns and climate. A more transient community has a greater likelihood of new infected individuals being introduced. This will either prolong epidemic curves or create a new curve altogether. Additionally, as you scenario plan, consider the potential for Covid-19 to be seasonal, which could shorten disease curves in the first wave, but perhaps introduce a second wave in the future.

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