A researcher at Harvard's School of Public Health teamed up with researchers at the Yale School of Public Health to develop a model for understanding and making projections about COVID-19 disease spread at a local level.
Updated daily with the newest available data, the model combines information on reported deaths, disease stage duration, and severity and mortality risk to estimate the spread of COVID-19 across all 50 states and more than 3,000 local counties.
Assistant Professor of Global Health Nicolas A. Menzies, the Harvard researcher involved in the project, worked with several Yale-affiliated researchers on building the model by developing its original statistical estimation.
As more data and new evidence become available, Menzies will also continue to oversee efforts to keep refining the model’s approach.
A perceived shortage of reliable data inspired the researchers to develop their own rigorous model, he said.
“In the US, it was clear that deficiencies in routinely reported data made it hard to make good planning decisions, so this project was an attempt to fill in the gaps,” Menzies wrote in an email to The Crimson.
Menzies said that the model’s local approach makes it unique among other COVID models.
“In our work we feel that a lot of individual and public decisions about COVID-19 require local estimates, and we are one of the only efforts providing these modelled results at county-level on a daily basis,” he wrote.
Users can download files containing all the local and state COVID data, a function born out of the researchers’ desires to make the data accessible. The researchers also attached a “covidestim R package” to the home page of the model so public health officials and other researchers can run the model using their own data.
The model comes as Massachusetts has seen a renewed uptick in COVID cases, reaching a positive case rate of 1.3 percent on Oct. 22. Cambridge’s positive case rate has stayed at only 0.13 percent as of Oct. 14.
—Staff writer Natalie L. Kahn can be reached at firstname.lastname@example.org. Follow her on Twitter @natalielkahn.