| Covid Act Now | |
| Url: | https://covidactnow.org/ |
| Commercial: | No |
| Content Licence: | --> |
| Owners: | --> |
| Editors: | --> |
Covid Act Now (CAN) is an independent, 501(c)(3) nonprofit that provides local-level disease intelligence and data analysis on the COVID-19 pandemic in the United States, via a website and an API.
CAN assists partners ranging from local county health departments to multinational corporations in developing COVID response plans. Its API is used by many of the Fortune 500 to make data-driven reopening decisions.
The organization's first product was a traditional SEIR model for predicting the rate of COVID spread in the U.S. The model was based on open-source code by Alison Hill, an assistant professor at the Johns Hopkins’ Institute for Computational Medicine. Rebecca Katz and her team have served as critical advisors.
CAN's modelling and data partners include Grand Rounds, a digital healthcare company, and USA Facts. Its university affiliates are Georgetown University Medical Center, Stanford Medicine, and Harvard Global Health Institute.[1]
CAN began as a collaboration between four volunteers — Max Henderson (a former Google employee), Igor Kofman (a former Dropbox engineer), Zachary Rosen, and Jonathan Kreiss-Tomkins — publishing the first version of their model on March 20, 2020. The team was soon joined by public health experts, data scientists, and other professionals. The initial model raised awareness of the critical shortage of hospital capacity that the U.S. would face if the spread of COVID-19 was not mitigated.
The platform provides a range of features, including:
CAN's models and data visualizations were used by officials in multiple states to aid in decision-making related to lockdowns, reopening, and resource allocation. The organization's work was cited in policy discussions and media reports throughout the pandemic. In mid-2021, Business Insider described CAN as a "leading US non-profit".[3] As of October 2023, the organization claims to have served tens of millions of users and to have supported hundreds of federal, state, and county officials as well as numerous multinational corporations and NGOs.
Like many models during the early days of the COVID-19 pandemic, CAN's initial projections faced scrutiny for assumptions made and data used. However, the team responded to feedback by refining their models and adding more sources of data over time.