2020 Sturgis Motorcycle Rally Analysis Mobility-Based Risk, Geographic Impacts, and Quarantine Compliance
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2020 Sturgis Motorcycle Rally Analysis Mobility-Based Risk, Geographic Impacts, and Quarantine Compliance September 5, 2020 Contents Preface 2 About the Alliance 2 Motivation 2 Authors 2 Acknowledgements 2 Executive Summary 3 Key Findings 3 Background 6 Data Methods 7 Identifying Sturgis Rally Attendees 7 Attributing Home Locations 7 Creating Individual-level Metrics For Infection and Transmission Risk 8 Statistical Adjustments for Sample Representativeness 8 Setting Evaluation Periods Before, During, and After the Sturgis Rally 9 Analysis Methods 11 States and Counties of Attendee Origin 11 Mobility-based Risks: Before, During, and After the Rally 11 Compliance with Quarantine Orders After Returning Home 11 Results 12 Estimated Rally Attendance by State: Total Attendees and Per 100,000 12 Estimated Rally Attendance by County: Top 50 Counties by Attendance Count 14 Estimated Rally Attendance by County: Top 50 Counties by Per Capita Attendance 16 Estimated Rally Attendees by County (Counts) 18 Estimated Rally Attendees by County (per 100,000 Residents) 18 Estimated Attendance among Majority-Native-American Counties (2000 Census) 19 Mobility-based Risks: Before, During, and After the Rally 20 Quarantine Order Compliance in Connecticut, New Jersey, Rhode Island, New York 23 Quarantine Order Compliance in Minnesota 24 1 Preface About the Alliance The COVID Alliance (known as the Alliance) is a volunteer-powered 501c3 nonprofit organization that was assembled in the early days of the COVID-19 pandemic. Conceptualized as an independent coalition of best-in-class talent and expertise across science, technology, and policy, the Alliance has now built a suite of research and insights platforms that are being used to inform policy making efforts across the country. Motivation The Alliance aims to accelerate the critical social science research and facilitate translation to evidence-based policies, starting with helping researchers more effectively collaborate and scale privacy-preserving studies on large data-sets — like XMODE’s geolocation data panel. In advance of a Request for Proposal (RFP) this month for academic and non-academic research projects to be carried out on the COVID Alliance Research Platform (“CARP”) — building on existing partnerships with the University of Chicago’s Harris School of Public Policy, UCLA School of Law, and others — the Alliance decided to conduct a one-off analysis looking at the behavior of Sturgis attendees. The decision to focus on Sturgis as a case study was based on media coverage suggesting that it might represent the largest potential “superspreader” event since the start of the epidemic earlier this year, with corresponding photos and video showing few social distancing protocols being followed. Authors ● Ryan Naughton, COVID Alliance Co-Executive Director ● Dr. Steven Davenport (Policy Analysis), COVID Alliance Chief of Staff; Adjunct Researcher, RAND Corporation ● Dr. Joshua Schoenfield (Physics), COVID Alliance Data Scientist ● Abraham Fraifeld, COVID Alliance Data Scientist; Analyst, Federal Reserve Board of Governors Acknowledgements This analysis would not be possible without the generous contributions of thousands of hours of time from our volunteers, especially those responsible for developing the core infrastructure for the COVID Alliance Research Platform (CARP) and associated interactive visualizations. We also are grateful for our data and technology partners, including X-MODE, Pachyderm, Immuta, Saturn Cloud, Snowflake, and Looker. 2 Executive Summary The Sturgis Motorcycle Rally (August 7th-16th) attracted an estimated 462,182 attendees from across the nation, according to the South Dakota Department of Transportation, despite public health concerns expressed by many state, local, and tribal public health officials related to COVID-19 transmission. Local health officials did not attempt to conduct comprehensive contact tracing, despite self-awareness that measures in place would be inadequate to measure the potential impact of the event on COVID-19 spread. To cast light on this phenomenon, the COVID Alliance has produced a one-off analysis of this event. This analysis makes use of the COVID Alliance Research Platform (CARP) and a geolocation dataset (provided via X-MODE) with over 25 million unique mobile devices in the USA, including 11,194 detected to attend the Sturgis motorcycle rally. To assess rally attendance, devices were tagged if they entered the area around Sturgis, South Dakota and spent considerable time at the event. To assess the infection/transmission risk of observed mobility behavior, device data was used to create individual-level metrics that have been found by some researchers as leading indicators for COVID-19 cases or morbidity. We use two metrics: daily distance travelled and the share of “dwell time” that individuals spent at home. Key Findings Rallygoers arrived in large numbers from across the nation. ● 462,182 attendees were counted by the South Dakota Department of Transportation. ● Just under half of attendees are estimated to have arrived from Great Plains states (222,450 attendees, for 48% of total)1 ● One-fifth of attendees were estimated to come specifically from South Dakota (20.1%). ● Rally attendees hailed from more than half of US counties (55.2%). ● Outside of the Great Plains, we also detected substantial attendees from: ○ Minnesota (31,433; 6.8% of rally attendees) ○ California (20,766; 4.5%) ○ Illinois (15,994; 3.5%) ○ Wisconsin (14,556; 3.1%) ○ Missouri (11,434; 2.5%) ○ Washington State (11,133; 2.4%) ○ Arizona (10,036; 2.2%) ○ Michigan (9,881; 2.1%) 1 Excluding South Dakota, these are Wyoming, Montana, North Dakota, Nebraska, Kansas, Colorado, Oklahoma, Texas, and New Mexico. 3 Objective mobility data confirms risky behavior at the rally. Consistent with first-hand reports of rally conditions, objective mobility data indicate risky activity at the Sturgis motorcycle rally, as measured by two metrics found to predict COVID-19 mortality rates: daily distance traveled and the share of dwell-time spent at home.2 ● Nationwide, during the rally dates, the median rally goer drove more kilometers daily (82.1 v 34.6) and spent less of their dwell-time at home (66.2% v 82.2%) than non-attendees. Even before attending the rally (July 11th-24th), rally goers demonstrated mobility behavior that would appear to elevate their risk of COVID-19 infection and transmission, compared to non-attendees in the same state of residence. ● Nationwide, the median rally goer drove more kilometers daily (67.8 v 33.3) and spent less of their dwell-time at home (67.4% v 83.1%) compared to non-attendees. ● Among South Dakota residents, the median rally goer drove more kilometers daily (59.2 v 41.2) and spent less of their dwell-time at home (67.5% v 74.2%) than non-attendees. ● Among Minnesota residents, the median rally goer drove more kilometers daily (87.6 v 40.0) and spent less of their dwell-time at home (66.9% v 81.2%) than non-attendees. ● Among Texas residents, the median rally goer drove more kilometers daily (72.6 v 31.6) and spent less of their dwell-time at home (74.0% v 85.7%) than non-attendees. Minnesota, Connecticut, Rhode Island, New Jersey, and New York all issued quarantines targeted at South Dakotans or Sturgis rally attendees, but mobility data suggests low compliance by attendees in the days following the rally (August 18th to September 4) for which data are available. (August 17th is withheld to allow time to travel home, noting also that many rally attendees leave before the rally’s last day.) ● In Minnesota (estimated 31,433 attendees), the median rally goer drove more kilometers daily (135 v 80) and spent less of their dwell-time at home (67.4% v 74.5%) than non-attendees. ● Combining Rhode Island, Connecticut, New Jersey, and New York (estiamted 10,398 attendees), the median rally goer drove nearly three times more kilometers daily (151 v 55) and spent less of their dwell-time at home (65.6% v 77.2%) than non-attendees. Rally attendees also originated in substantial numbers from counties where no Sturgis-linked cases have been reported. This may indicate undetected Sturgis-linked cases, particularly in areas without pro-active contact tracing programs. ● Maricopa County, Arizona (5,969 estimated attendees) ● Cook County, Illinois (3,586) ● Harris County, Texas (2,807) ● Polk County, Iowa (1,928) ● Tarrant County, Texas (1,907) 2 “Dwell-time” is defined as any location where the user sends repeated geolocation pings in a circular area defined by a 45-meter radius. 4 ● Clark County, Nevada (2,522) ● San Diego County, California (2,338) ● San Bernardino County, California (2,069) ● Riverside County, California (1,819) Attendees originated from several majority-Native-American counties (according to the 2000 Census), potentially representing viral vectors that may increase transmission to Native Americans. ● Bennett County, South Dakota, was estimated to have contributed 186 attendees (5% of its population). That county had only ever reported 6 COVID-19 cases at the start of the rally (August 7), increasing five-fold to 32 cases by September 5th 2020. ● Ziebach County, South Dakota (72% Native American) was estimated to originate 266 rally goers. Cumulative reported cases increased there from 33 to 55 over the same time period. ● Roosevelt County, Montana (56% Native American) was estimated to originate 491 attendees. Reported cases increased nearly three-fold from 21 to 61. ● Big Horn County, Montana (59% Native American) was estimated to originate 465 attendees. Reported cases increased 75% from 407 to 714. Policy Implications ● Given the wide geographic extent of potential COVID-19 transmissions related to the Sturgis rally, such events carry national-level implications, and therefore merit concern from local public health officials, even those located in areas as distant as Florida and Arizona. ● Policymakers may want to consider how to improve compliance with quarantine directives. Options for improvement may include better targeting of messaging campaigns, broader use of monitoring programs, and/or more intensive enforcement to detect and sanction non-compliant individuals.