The COVID-19 Impact Analysis Platform provides data and insight on COVID-19’s impact on mobility, health, economy, and society for all states and counties with daily data updates. The platform was originally developed by Dr. Lei Zhang’s group at the Maryland Transportation Institute (MTI) in partnership with the Center for Advanced Transportation Technology Laboratory (CATT Lab). A multidisciplinary team of researchers are now making their COVID-19 data and research findings available to inform the general public and support decision making through this platform. Metrics are produced based on validated computational algorithms and privacy-protected data from mobile devices, government agencies, healthcare system, and other sources. The platform will evolve and expand over time as new metrics are computed and additional visualizations and decision support tools are developed.
DATA USE AND CITATION
To request data files at the national, state, and/or county levels, please email us (firstname.lastname@example.org) from your work email address and include the following information: 1. requestor name, organization, and contact information; and 2. intended use of the aggregate-level results at the state and county levels. The UMD team also welcomes suggestions for additional metrics and geographical coverage as we continue expanding the platform.
Data published on our platform are for visualization and COVID-19 decision support. Researchers and decision-makers are encouraged to use published data for COVID-19 research with proper citation of the platform based on suggested citations below. Please do not use written descriptions on the platform webpages without proper citation. Reposting of data created by MTI on other dashboards or data archives must be approved in writing by MTI Director.
- Maryland Transportation Institute (2020). University of Maryland COVID-19 Impact Analysis Platform, https://data.covid.umd.edu, accessed on [date here], University of Maryland, College Park, USA.
- Zhang L, Ghader S, Pack M, Darzi A, Xiong C, Yang M, Sun Q, Kabiri A, Hu S. (2020). An interactive COVID-19 mobility impact and social distancing analysis platform. medRxiv 2020. DOI: https://doi.org/10.1101/2020.04.29.20085472 (preprint).
DATA AND METRICS SUMMARY
The COVID-19 Impact Analysis Platform currently contains the following 39 metrics at the national, state, and county levels in the United States with daily updates unless otherwise specified.
|Category A: Mobility and Social Distancing|
|Social distancing index*||An integer from 0~100 that represents the extent residents and visitors are practicing social distancing. “0” indicates no social distancing is observed in the community, while “100” indicates all residents are staying at home and no visitors are entering the county. See “Method” page for details. Calculated by MTI.|
|% staying home||Percentage of residents staying at home (i.e., no trips with a non-home trip end more than one mile away from home). Calculated by MTI.|
|Trips/person||Average number of all trips taken per person per day. Calculated by MTI.|
|% out-of-county trips||Percentage of all trips that cross county borders. Calculated by MTI.|
|% out-of-state trips*||Percentage of all trips that cross state borders. Calculated by MTI.|
|Miles/person||Average person-miles traveled on all modes (car, train, bus, plane, bike, walk, etc.) per person per day. Calculated by MTI.|
|Work trips/person||Number of work trips per person per day (where a “work trip” is defined as going to or coming home from work location). Calculated by MTI.|
|Non-work trips/person||Number of non-work trips per person per day. Additional information on trip purpose (grocery, park, restaurant, etc.) is available, but not currently shown on the platform. Calculated by MTI.|
|Transit mode share||Percentage of rail and bus transit mode share. Source: baseline transit mode share from ACS; MTI is currently updating this metric with dynamic transit mode share based on mobility data.|
|Category B: COVID and Health|
|New COVID cases||Number of COVID-19 daily new cases. Source: JHU COVID-19 data repository.|
|New cases/1000 people*||Number of COVID-19 daily new cases per 1000 people (three-day moving average). Calculated by MTI based on JHU repository.|
|Active cases/1000 people||Number of active COVID-19 cases per 1000 people. Calculated by MTI.|
|Imported COVID cases*||Number of daily external trips by infectious persons from out of state/county. Calculated by MTI.|
|COVID exposure/1000 people||Number of residents already exposed to coronavirus per 1000 people. Calculated by MTI.|
|# days: decreasing COVID cases||Number of days with decreasing COVID-19 cases. Calculated by MTI based on weekly pattern of new daily cases.|
|# days: decreasing ILI cases*||Number of days with decreasing influenza-like illness trend. Calculated by MTI using CDC Weekly U.S. Influenza Surveillance Report.|
|Testing capacity gap*||Ability to provide enough tests based on World Health Organization-recommended positive test rate proxy. High positive test rates indicate a lack of sufficient testing and testing capacity gap. Source: The COVID tracking project.|
|Tests done/1000 people||Number of COVID-19 tests already completed per 1000 people. Source: The COVID tracking project.|
|# contact tracing workers/1000 people*||Number of contact tracing workers per 100,000 people. Source: NPR survey of state public health departments.|
|% hospital bed utilization*||% hospital beds occupied with patients. Calculated by MTI using ESRI: US Hospital Beds Dashboard and IMHE COVID-19 projections|
|% ICU utilization*||% ICU unites occupied with COVID-19 patients. Calculated by MTI using ESRI: US Hospital Beds Dashboard , The COVID Tracking Project , and IMHE COVID-19 projections.|
|Hospital beds/1000 people||Number of staffed hospital beds per 1000 people. Source: ESRI: US Hospital Beds Dashboard|
|ICUs/1000 people||Number of ICU beds per 1000 people. Source: ESRI: US Hospital Beds Dashboard. MTI could not find a source with daily or weekly updates on this metric.|
|Ventilator needs*||Number of ventilators needed for COVID-19 patients. Source: IHME COVID-19 projections.|
|Category C: Economic Impact|
|Unemployment claims/1000 people||New weekly unemployment insurance claims/1000 workers. Source: Department of Labor.|
|Unemployment rate*||Unemployment rate updated weekly. Calculated by MTI and periodically adjusted to match federal agency statistics.|
|% working from home*||Percentage of workforce working from home based on UMD models. Calculated by MTI based on changes in work trips and unemployment claims.|
|Cumulative inflation rate||Overall economic condition measured by cumulative inflation rate since COVID-19 outbreak. Calculated by MTI based on CPI data from Bureau of Labor Statistics.|
|% change in consumption*||% change in consumption from the pre-pandemic baseline based on observed changes in trips to various types of consumption sites as a proxy. Calculated by MTI.|
|Category D: Vulnerable Population|
|% people older than 60*||Percent of population above the age of 60. Source: Census Bureau.|
|Median income||Median income. Source: Census Bureau.|
|% African Americans||Percentage of African Americans. Source: Census Bureau.|
|% Hispanic Americans||Percentage of Hispanic Americans. Source: Census Bureau.|
|% male||Percentage of male. Source: Census Bureau.|
|Population density||Population density. Source: Census Bureau.|
|Employment density||Employment density. Source: Smart Location Database.|
|# hot spots/1000 people||Number of points of interests for crowd gathering per 1000 people. Calculated by MTI.|
|COVID death rate*||% deaths among all COVID-19 cases. Calculated by MTI based on number of deaths and estimated total COVID cases including confirmed and untested cases.|
|Population||Total population in a state or county. Source: Census Bureau.|
* These variables are used in the Society and Economy Assessment (SERA) tool.
The UMD team is currently working on adding the additional metrics below to the platform.
|Detailed employment and job impact by economic sector for each state and county||Based on baseline employment, observed changes in work trips by business types, and work-from-home factors, this metric tracks the loss or gain of jobs in each economic sector at the state and county levels. With weekly updates, it can track employment trends and provide decision support for economic stimulus in a timely manner. Estimate release date: May 18, 2020|
|person trip details||Number of trips by socio-demographic groups (income, age, gender, race, etc.), travel modes (air, rail, bus, driving, biking, walking, and others), and top 30 origin-destination pairs (e.g., origins/destinations of all trips to/from a county or a state). Estimated release date: May 25, 2020|
|Vehicle Trip Details||Changes in passenger vehicle, commercial truck trips, and commercial delivery trips using personal vehicles. Estimated release date: TBD; working with data providers to deploy algorithms.|
|Other||Please let us know your data needs related to COVID-19 and how our platform can help you or your organization.|
TEAM AND PARTNERS
The project team is led by Dr. Lei Zhang (project lead) and Michael Pack (co-lead). Additional team members from Dr. Zhang’s research group at MTI include Dr. Sepehr Ghader (project manager), Dr. Chenfeng Xiong (big data and cloud lead), Aref Darzi (methodology), Ya Ji (AWS deployment), Connie Tang (research coordination), and graduate students: Yixuan Pan, Jun Zhao, Qianqian Sun, Weiyi Zhou, Minha Lee, Mofeng Yang, Songhua Hu, Aliakbar Kabiri, Guangchen Zhao, and Weiyu Luo. CATT Lab team members include Nikola Ivanov (manager), Michael Van Daniker (platform lead), Patrick Redding (computing), Prashant Swarnapuri (database), Jenny Lees (graphic design), and Kyle Haas (platform developer). The team also includes researchers across multiple campuses at the University System of Maryland who study COVID-19-related social and health impact: Drs. Deb Niemeier, Alan Faden, Rosemary Kozar, Margaret Lauerman, and Kartik Kaushik; economic impact: Drs. John Haltiwanger, Katharine Abraham, and Erkut Ozbay; and geospatial behavior impact: Drs. Kathleen Stewart and Junchuan Fan. MTI and CATT Lab are both affiliated with the Department of Civil and Environmental Engineering, A. James Clark School of Engineering at the University of Maryland – College Park (UMD).
We would like to thank and acknowledge our partners including: (1) mobile device location data providers*; (2) Amazon Web Service and its Senior Solutions Architect, Jianjun Xu, for providing cloud computing and AWS technical support; (3) More than 20 established experts in epidemiology, public health, economics, social distancing, and vulnerable populations who provided inputs to the development of related metrics for the Society and Economy Reopening Assessment (SERA) tool; (4) partial funding support from the U.S. Department of Transportation (USDOT)’s Bureau of Transportation Statistics and National Science Foundation’s RAPID Program; and (5) computational algorithms for mobility metrics developed and validated in a previous USDOT Federal Highway Administration’s Exploratory Advanced Research Program.
* Privacy protection: All data sources used in the creation of the metrics are anonymized and contain no personal information. The metrics produced from the data are provided only in aggregated forms at the county, state, and national levels.