Forecasting COVID-19 Transmission Across Time in U.S. Counties
Statement of Problem
As the nation responds 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. In the initial phase of the pandemic, this information was critical to help local, state and federal leadership make informed decisions about public policy solutions to manage the crisis.
As case incidence started to decline following the surges across our cities, there was a need to expand 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.
Now, as we enter a new phase of the COVID-19 pandemic with available vaccines and low community transmission, communities have reopened across the country. Still, COVID-19 will likely evolve into an endemic virus with peaks in transmission during the fall and winter seasons, and it will be crucial to understand how significant these potential yearly peaks could be.
COVID-Lab: 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 currently offering county-level data on test positivity rates and case counts through their COVID-Lab model.
Through this project, our interdisciplinary team from Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania use county-level data to longitudinally track COVID-19 transmission and test positivity rates across all U.S. counties. The models also provide four-week projections of case transmission for as many as 821 counties with active outbreaks, representing 82% of the U.S. population and 83% of all identified coronavirus cases. (We were doing this weekly between April 2020 and May 2021, and then resumed again on Sept. 15.) The 821 counties included 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 these models, the team accounts for the impact of weather, health, demographics, county-level vaccination rates, and other local area effects of the population and city characteristics to develop forecasts that were 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. We also incorporated other key data into the models to permit more comprehensive assessments of local-area risk for COVID-19 transmission. Our dashboards offer 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.
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.
The team utilized its modeling data to advise the White House Coronavirus Task Force, governors, state public health officials, and community leaders on emerging hotspots and local strategies for reducing the spread of the virus. Furthermore, the model accompanies guidance for in-person education in K-12 educational settings 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 learn how we validated this model to ensure accurate projections, click here, to learn more about the methods of this model, see this abstract, and to read our top three lessons learned from 14 months of modeling a pandemic, click here.
As mentioned above, we will continue to monitor community transmission and update the model weekly with the latest data and projections. The team is working on additional peer-reviewed articles based on this project that we plan to share once published.
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 September 2021.
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].