Projections of COVID-19 in the US at county level generated on March 24 2020

Projections were generated for daily demand of hospital beds, ICU beds and ventilators as well as mortality (2.5, 25, 50, 75 and 97.5 percentiles). Details on the model and projection method are reported in the PDF document.

The movies and plots should be fairly self explanatory (ny is New York City metro area).  Note that hospitalizations and ICU bed movies and figures indicate the current demand on a given day (NOT just the new persons needing beds).  Deaths are new deaths on a given day.  The 3 scenarios show the effects of 25% and 50% further reductions of contact.

Note, the NY metropolitan area in the figure includes 11 counties: Kings County NY, Queens County NY, New York County NY, Bronx County NY,Richmond County NY, Westchester County NY, Bergen County NJ, Hudson County NJ, Passaic County, NJ, Putnam County NY, Rockland County NY

Interpretation Considerations

Several qualifications with respect to these projections must be noted and considered during interpretation. Firstly, the model is optimized using observations through March 24, 2020; however, those observations, i.e. confirmed cases by county, represent infections that were acquired by individuals 1-2 weeks earlier during a time prior to the implementation of many of the social distancing and isolation measures in place on March 24, 2020. Because of this long delay between infection acquisition and case confirmation, any flattening of the curve due to these effects is not yet apparent in observations nor communicated to the model during optimization. Consequently, the no intervention scenario roughly represents the transmission potential of the virus around March 12th projected forward. As many new control policies were effected after March 12, 2020, the 25% and 50% contact reduction projections may depict paths that some counties are already following and thus may be more representative of future activity. In this fashion, the 3 scenarios provide references against which the effectiveness of control measures already enacted can be assessed. We also ask readers to note the cones of uncertainty associated with each scenario projection, which broaden into the future.

Secondly, the landscape to which this model has been optimized is highly variable in space and time, due to differences in contact behavior, population density, control measures and testing practices. These differences in space and time make the fitting of any model of this scale challenging. In this shifting landscape, it is very important to reiterate that it will take 10-14 days before the effects of real-world interventions—any flattening of the curve weeks—become apparent.