Forecasting COVID-19 Transmission Across Time in U.S. Counties

Statement of Problem

As the nation continues to respond to the once-in-a-century challenge of the COVID-19 pandemic, forecasting viral transmission across local communities has been an important tool for planning and response. This information is critical to help local, state and federal leadership make informed decisions about public policy solutions to manage the crisis.

In an initial response to the pandemic, society largely shut down through a variety of social distancing interventions that varied in magnitude and timing, and were designed to help slow the rate of infection, or flatten the curve. Flattening the curve was essential to prevent overwhelming health care systems, particularly with increased demands for ICU beds and ventilators, as well as to improve access to rapid and serologic testing for public health surveillance. Now, as communities have reopened in various stages across the country, we are facing new challenges with schools and colleges resuming in-person learning, new variant strains emerging that may be more transmissible than early strains, states lifting public restrictions, and pandemic fatigue among individuals and institutions for following the proven safety protocols, such as masking and distancing that can reduce risk for widespread community transmission.

As the pandemic response evolves across our cities, the team has been expanding on robust and reliable data models, while reviewing emerging data on school and community safety, to guide decision-making regarding returning more children to in-school instruction, as well as informing safe reopening of communities.

Description

Forecasting COVID-19 Transmission Across Time in U.S. Counties

Our interdisciplinary team across Children’s Hospital of Philadelphia and the University of Pennsylvania is using local data to provide short-term forecasts based on current social distancing practices.

Our interdisciplinary team from Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania is using county-level data to longitudinally track COVID-19 transmission and test positivity rates across all U.S. counties. The models forecast four-week projections of case transmission weekly for 821 counties with active outbreaks, representing 82% of the U.S. population and 83% of all identified coronavirus cases. The 821 counties include those with state capitals, populations of 40,000 individuals or more (with a minimum population density of 250 people per square mile), and sustained outbreaks, which are defined further in this abstract.

Through this model, the team accounts for the impact of weather, health, demographics, and other local area effects of the population and city characteristics to develop forecasts that are calibrated to the actual rate of growth in county transmission during the prior week. The models consider over time the influence of temperature and humidity on SARS-CoV-2 transmission; determine the impact of social distancing in modifying the trajectory of SARS-CoV-2 transmission during current and future outbreaks; investigate how city characteristics modify the transmission; and forecast the effects of policy change and social distancing practices on the risk for virus resurgence.

The data and methodology behind this modeling project was informed by a peer-reviewed study, published in JAMA Network Open, revealing social distancing to be the strongest mitigating factor in reducing COVID-19 transmission. Early models also revealed a strong seasonality effect of SARS-CoV-2 transmission; spring-like temperatures contributed to some reduction in the spread of the virus, but the absence of masking and distancing increased the risk for outbreaks in the summer of 2020. However, the strong influence of colder temperatures on the spread of the virus led to a challenging 2020-21 winter with sustained high transmission across the country.

Over the year, we incorporated other key data into the visualization of our models to permit more comprehensive assessments of local-area risk for COVID-19 transmission. Our dashboards now include visualizations that display a national map highlighting state-level COVID-19 intensive care unit occupancy, hospital daily census, new daily COVID-19 admissions and daily COVID-19 emergency department visits.

Furthermore, the model’s dashboard with county-level case incidence and test positivity rates also accompanies revised guidance for in-person schooling developed with CHOP’s Division of Infectious Diseases to help school districts and communities around the country negotiate the dynamic challenges of this pandemic and plan for the future. To view a data visualization of our findings, click here, to learn how we validate this model to ensure accurate projections, click here, and to learn more about the methods of this model, see this abstract.

Next Steps

Using this real-time data, we have been engaging with local, state, and federal policymakers who are managing the COVID-19 response to support their efforts.

For more on PolicyLab's COVID-19 response and guidance to assist schools and communities with reopening strategies, click here.

This project page was last updated in March 2021. 

Suggested Citation

Children's Hospital of Philadelphia, PolicyLab. Forecasting COVID-19 Transmission Across Time in U.S. Counties. [Online]. Available at: http://www.policylab.chop.edu. [Accessed: plug in date accessed here].