Data

NSSAC researchers are working hard to curate data related to all aspects of COVID-19. We would like to share this data with the scientific community, and will update this page with links to those data sets throughout our response support efforts.

Surveillance Dashboard

Our dashboard data is currently available in CSV format by choosing the blue "Download All" button on the upper left side of the dashboard. Click the "FAQ" button to learn more about our methodologies, citation requests, and disclaimers.

Population contact rates by Age and County

The contacts analyzed here are derived from a synthetic population of the U.S. in which each person is assigned an activity template based on American Community survey data and locations for each activity are assigned based on location types, sizes, and distance from residence or catchment region. Contacts are inferred between people present at a location simultaneously. For locations with many occupants, a subgraph is constructed that interpolates between a minimum and maximum number of contacts, depending on the total number of occupants. Activities are assigned differently for weekdays and weekends. Hence the contact networks also differ. In addition, we analyzed an activity-independent network the includes only household contacts.

Baidu mobility data for January through March, 2020

This archive contains mobility data made public by Baidu and scraped from their qiangxi.baidu.com web site in February and March, 2020. We have reformatted the data into a more easily computable form, comma-separated value (csv) files providing the full origin-destination matrix for each day. We are publishing this reformatted version for research purposes under Article 22 of the Copyright Law of the People's Republic of China (https://wipolex.wipo.int/en/text/466268). We make no representation as to the suitability of the data for any purpose, but nevertheless hope that it may be useful for researchers trying to calibrate models of 2019-ncov. We wish to thank Baidu especially for making these valuable data available and encourage them to continue to do so. Thanks also to Chunhong Mao for making us aware of this data source and explaining what the data represent and to James Schlitt for scraping the data.