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

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.

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.

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.

● 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. ● In addition to contact tracing, broader use of mobility data may be a useful addition to the toolkit used by public health agencies and researchers seeking to understand and mitigate viral spread. ● Given substantial attendance from majority-Native-American counties, the Sturgis contributed to viral transmission to Native American communities, some of which (e.g., Bennett County) had previously reported very low levels of COVID-19.

Background The Sturgis Motorcycle Rally was chosen to demonstrate the potential of the COVID Alliance Research Platform, including for research and analysis of geolocation data related to the COVID-19 response. From August 7th-16th, according to the South Dakota Department of Transportation, 462,182 vehicles attended the rally, only 7.5% lower than in 2019.

Public health concerns were publicly aired in the weeks leading up to the Sturgis Motorcycle Rally, held August 7th to 16th. Native American tribes in western South Dakota turned away motorcyclists who attempted to travel through the reservations to Sturgis and strongly urged their residents not to attend, in interest of protecting the local population. Rally goers returning to Minnesota, Connecticut, New Jersey, and Rhode Island were warned to consider the risks of attending the rally, and after returning from the rally, were requested to self-quarantine for 14 days.

At the event, photos and videos indicate social distancing protocols were not being widely followed, and individuals have reported flagrant behavior such as an alleged “sneezing contest”. South Dakota health officials have acknowledged they would be unable to discover the extent of COVID-19 transmissions at the rally, due to non-existent or incomplete contact tracing efforts, a sentiment shared by the president of the South Dakota State Medical Association.

Now, over two weeks after the conclusion of the rally, the Midwest and the Dakotas in particular are seeing a spike in coronavirus cases even as infections decline or plateau in the rest of the country. The first reported Sturgis-linked death occurred in Minneapolis, Minnesota. More than 260 linked cases thought to be a severe undercount, due to the resistance to testing and the limited contact tracing in some states. As of September 2, 2020, reported Sturgis-linked cases include: ● South Dakota: 118 ● Minnesota: 49 ● North Dakota: 30 ● Colorado: 25 ● Wyoming: 13 ● Michigan: 11 ● New Hampshire: 8 ● Nebraska: 7 ● Montana: 7 ● Washington: 3 ● New Jersey: 3 ● Wisconsin: 2 ● Idaho: 2

Data Methods

1. Identifying Sturgis Rally Attendees To identify devices owned by Sturgis attendees, the Alliance team used a “geo-fence” method. A rectangular area (24 miles east-west by 20 miles north-south) was drawn around Sturgis and areas containing affiliated camps outside the city, e.g., the “Buffalo Chip” campsite.3 To exclude trucks and other drivers passing by Sturgis, e.g., on I-90, devices were only marked as Sturgis rally attendees if they met a certain minimum dwell time and minimum number of geolocation data points at the rally. To be identified as an attendee, he/she had to have multiple dwell events lasting at least 60 minutes within this area. A dwell event is defined as when an individual is stationary at a location.

Summary Statistics for Identifying Attendees: ● Average Attendee spent 4.12 days at the rally ● Median Attendee spent 3.57 days at the rally ● The average person generated 644 geolocation pings / data points while they were at the rally. ● The total dwell time for the average rally attendee was 48 hours. If he/she slept 8 hours per night and slept there for 3 nights (given the 4 day average attendance), this leaves 24 hours of non-sleeping time where he/she was simply hanging out with other rally attendees or 6 hours per day of socializing. 2. Attributing Home Locations Geolocation data was grouped by the unique device ID, and fed to machine learning algorithms to identify geographical clusters where users were observed “dwelling”, i.e., remaining in one place, sending repeated mobile device pings, for a substantial period of time.4

This method produces a number of geographical clusters for each user. Algorithms were developed to identify a “home” and “work” location for each device, where the “home” location is the location where the device spent the most dwell time over a minimum two week window. These home locations are very consistent and stable such that the 99% of users’ home locations do not change location month over month, supporting our conclusion that these are their home locations. This attribution enables analysis comparing Sturgis attendees based on attributed county and state of residence.

3 Bounding box corner coordinates: [(44.2585057, -103.890427), (44.546862, -103.396921)]. 4 “Dwell-time” is defined as any location where the user sends repeated geolocation pings in a circular area defined by a 45-meter radius.

3. Creating Individual-level Metrics For Infection and Transmission Risk To assess the riskiness of mobility behaviors, geolocation data was used to generate two individual-level metrics that have been demonstrated in other research projects to act as leading indicators for COVID-19 mortality rates: (1) Kilometers travelled (Barrios et al 2020, Chen et al 2020)5 and (2) “home-dwell time” (Woody et al, 2020)6. Home-dwell time refers to the share of the individual’s detected “dwell time” that was attributed to their “home” location, rather than other dwell locations. One potential limitation of comparing “kilometers traveled” is that travel activity reported by Sturgis attendees during the rally may also include travel to or away from the rally, since not all participants arrive before the first day or stay through the last day.

4. Statistical Adjustments for Sample Representativeness It is trivial to count the number of devices detected in a given location, but estimating the number of people indicated by that number of devices is more complicated. Further, because the X-MODE-provided geolocation data is not structured as a clean probabilistic sample of Americans, and because the extent of coverage over time and according to the use of specific mobile apps that report data to X-MODE, traditional survey-based sampling weights are not suitable for use.

Rally attendance was estimated by place of residence at the county-level, over several steps. First, we assessed the representativeness of our data set on a per county basis utilizing our broader geolocation dataset. Given that we have 25 million Americans represented with over 400 billion rows of data, we have sufficient coverage of the entire country to understand how this dataset compares to the true populations of these countries. We assessed the number of individuals represented in X-MODE data who live in a given county (based upon the aforementioned home clustering analysis) and compared to that to the known total population of that county. Through this, we can understand how representative our data is on a per county basis and generate a per-county scaling factor accordingly.

This method resulted in an initial predicted Sturgis attendance of 125,382 -- well short of the 462,182 estimated attendees. This discrepancy indicates that rally-attendees as a sub-group were systematically different than non-attendees living in the same county, and that that sub-group is systematically under-represented in the X-MODE geolocation dataset. This is in part because the age distribution of the X-MODE data skews younger while the Sturgis attendance skews older. To

5 See J. M. Barrios, Y. Hochberg, "Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic," Working Paper No. 27008 (National Bureau of Economic Research, 2020) available at https://www.nber.org/papers/w27008.pdf; Chen et al, “Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission”, available at https://www.anderson.ucla.edu/faculty_pages/keith.chen/papers/WP_StayAtHomeOrders_and_COVID19.pdf 6 Woody, et al., (2020) “Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones”, available at (https://covid-19.tacc.utexas.edu/media/filer_public/87/63/87635a46-b060-4b5b-a3a5-1b31ab8e0bc6/ut_ covid-19_mortality_forecasting_model_latest.pdf

adjust for the distinctiveness of Sturgis attendees and their resulting under-representation in the dataset, the authors created and validated two candidate, independent adjustment factors.

The first candidate scaling factor was based on a county’s distance to the rally. To construct this scaling factor, it was assumed that the county-level distinctiveness of attendees versus non-attendees (and therefore the extent of attendee under-sampling) is proportional to the distance of that county to the Sturgis rally. In other words, the farther one had to travel to attend, we assessed that they are less similar to their neighbors in the county of residence and a more unique subgroup, subject to more extreme under-sampling. The intuition being that if one lives in Meade County (which contains Sturgis), he/she is likely to attend simply because it is the primary social event of the month. In contrast, to travel across the entire country to attend takes significant dedication and planning, causing a self selection to occur distinct from your non-attending peers from your home county. To implement this approach, an adjustment factor was calculated based on the log of distance from the county to the rally site.

A second candidate was based on the extent of overlap between a state’s general population and the subset of the county’s residents who attended the rally -- which can be measured as the proportion of devices in each county attributed as attending the rally. This follows intuition: as the percent of a county’s devices attributed to attending the rally increases, it may be inferred that the rally goers were more representative of the county’s broader population, and therefore requiring a smaller adjustment factor. This adjustment factor was implemented as a logarithmic function.

The two candidate scaling factors (log of distance traveled and log of county-level device coverage) demonstrated a very high correlation (97%), suggesting that their effects are largely interchangeable, and validating the underlying logic. After application of the coverage-based scaling factor, the sum total of estimated attendees equals 461,778, essentially identical to attendance estimated by the South Dakota Department of Transportation.

5. Setting Evaluation Periods Before, During, and After the Sturgis Rally To allow examination of mobility before, during, and after the rally, three separate time periods were introduced. “Before”, “during”, and “after” periods are identified with the objective of describing attendee behavior before, after, and at the rally, with minimal capture of attendees’ travel time to or from the rally. ● The “before” period is defined as July 11th to July 24th. This period allows attendees as much as two weeks to travel to the rally, and allows two weeks in the “pre-period” to garner adequate sample size from each device. ● “During” pertains to August 7th to 16th, the official dates of the rally. ● The “after” period refers to August 20th to September 4th. This allows a three-day buffer to return home for attendees who stayed through to the last day of the rally (August 16th). Admittedly, this may also capture some long-distance travel from attendees from very distant locations, e.g., Florida; however, the number of attendees travelling from that far

away was fairly limited. Moreover, Sturgis attendance was front-loaded to the beginning of the 10-day rally, with only 12% of attendees arriving on the last two days of the rally (as indicated below).

Sturgis Rally Arrivals by Day: Admissions and Cumulative Distribution Function (CDF)

Analysis Methods

1. States and Counties of Attendee Origin After estimating individuals’ place of residence based on their mobility and dwell behavior, statistical adjustments were made to estimate the number of individuals associated with those device counts. This enables estimation of Sturgis rally attendance from each state and county.

2. Mobility-based Risks: Before, During, and After the Rally To provide additional objective measures for the assessment of levels of risky behavior observed at the Sturgis motorcycle rally, attendees and non-attendees were compared for two leading metrics: daily distance traveled and share of dwell time spent at home.

Comparisons are made for three time periods: 1. Before the rally (July 11 - 24) 2. During the rally (August 7 - 16) 3. After the rally (August 20 - September 4)

Separate analyses were run at the national level and for three select states: South Dakota, Minnesota, and Texas. For each, attendees are compared to non-attendees hailing from the same geographic area (i.e., nation or state). The nationwide comparison is the most simple comprehensive analysis, although suffers the disadvantage of comparing dissimilar groups, since attendees hail predominantly from Great Plains states, which carry systematic differences relative to the rest of the nation. In response, the South Dakota comparison highlights the differences between South Dakotan rally attendees and non-attendees, two groups which share more similar culture backgrounds and important other characteristics that may determine their behavior under a COVID-19 pandemic. Minnesota was chosen as it is the site of the first COVID-19 fatality linked to the Sturgis rally; it is also an example of an origin fairly far away from Sturgis (roughly 600 miles, or 9 hours driving) -- given the additional trouble to attend the Sturgis rally from that distance, one may expect those attendees to show somewhat more extreme behavior than attendees from South Dakota, for whom the rally was a much easier trip. Finally, Texas is chosen for its distance from South Dakota (roughly 1,000 miles) and substantial number of estimated attendees (21,197).

3. Compliance with Quarantine Orders After Returning Home A similar analysis is conducted to assess levels of compliance with quarantine directives targeted at Sturgis rally attendees. Quarantine directives targeted at returning rally attendees were issued in Minnesota, Rhode Island, New Jersey, and Connecticut (as well as a South Dakota-wide advisory from New York State). To assess compliance, mobility behavior across attendees and non-attendees is compared for the period immediately after the rally (August 17 - September 24). We make two separate comparisons: (1) among Minnesota residents and (2) a pooled comparison of residents in RI, NY, MN, and NJ (for sample size).

Results

Estimated Rally Attendance by State: Total Attendees and Per 100,000

Estimated Estimated Rank Sturgis Rank Sturgis (Attendance Attendance Per (Attendance State Attendance Count) 100,000 Per Capita) SD 92,998 1 11,282 1 MN 31,433 2 581 6 CO 29,260 3 561 8 TX 21,197 4 88 31 CA 20,766 5 66 40 WY 18,753 6 3,243 2 NE 16,682 7 920 4 IA 16,280 8 564 7 IL 15,994 9 132 24 WI 14,556 10 266 12 ND 12,290 11 1,778 3 MO 11,434 12 213 13 WA 11,133 13 165 17 AZ 10,036 14 156 20 MI 9,881 15 111 27 FL 9,694 16 53 43 OH 8,876 17 86 32 KS 8,217 18 314 9 IN 7,631 19 141 22 MT 7,510 20 829 5 PA 7,398 21 71 39 NC 6,314 22 85 33

NY 6,017 23 39 46 OK 5,662 24 181 15 UT 5,069 25 191 14 GA 5,016 26 77 37 ID 4,514 27 300 10 NV 4,254 28 156 19 TN 4,215 29 82 34 VA 3,741 30 76 38 LA 3,685 31 105 28 OR 3,516 32 102 29 MA 3,213 33 55 42 SC 3,112 34 80 36 NM 2,899 35 163 18 AR 2,727 36 139 23 NJ 2,583 37 34 48 KY 2,459 38 93 30 AL 1,789 39 56 41 MS 1,743 40 122 25 CT 1,525 41 45 44 WV 1,307 42 176 16 MD 1,060 43 41 45 ME 998 44 142 21 NH 881 45 81 35 AK 478 46 120 26 VT 320 47 278 11 DE 280 48 37 47 RI 272 49 29 49 HI 109 50 11 50

Estimated Rally Attendance by County: Top 50 Counties by Attendance Count Estimated Rank Estimated Rank State County Sturgis (Attendance Attendance (Attendance Attendance Count) Per 100,000 Per Capita)

SD Lawrence County 22,685 1 91,254 1 SD Pennington County 21,927 2 20,435 5 SD Meade County 18,928 3 70,522 2 SD Butte County 6,861 4 66,817 3 WY Campbell County 5,970 5 12,316 11 AZ Maricopa County 5,969 6 146 1080 SD Minnehaha County 4,452 7 2,446 103 MN Hennepin County 4,155 8 344 647 CO Adams County 4,016 9 837 322 CA Los Angeles County 3,999 10 40 1565 IL Cook County 3,586 11 69 1440 WY Natrona County 3,279 12 4,055 62 CO El Paso County 3,223 13 484 509 CO Weld County 3,216 14 1,156 240 CO Jefferson County 3,115 15 557 451 CO Larimer County 2,815 16 866 312 TX Harris County 2,807 17 63 1466 NE Douglas County 2,565 18 472 517 NV Clark County 2,522 19 122 1173 MN Anoka County 2,420 20 709 376 CA San Diego County 2,338 21 72 1424 ND Burleigh County 2,112 22 2,333 111 CO Denver County 2,109 23 318 674 CA San Bernardino County 2,069 24 98 1270

IA Polk County 1,928 25 420 572 TX Tarrant County 1,907 26 98 1272 MT Yellowstone County 1,840 27 1,185 234 CA Riverside County 1,819 28 78 1385 UT Salt Lake County 1,803 29 165 1010 WA King County 1,773 30 85 1342 MN Dakota County 1,767 31 429 564 WY Crook County 1,747 32 23,989 4 MO Jackson County 1,719 33 252 792 WA Spokane County 1,712 34 352 640 WA Pierce County 1,640 35 197 917 NE Scotts Bluff County 1,561 36 4,264 58 TX Dallas County 1,523 37 61 1480 WY Laramie County 1,516 38 1,572 172 ND Cass County 1,463 39 877 309 CO Boulder County 1,361 40 433 559 IL DuPage County 1,354 41 145 1083 IA Woodbury County 1,321 42 1,289 215 SD Lincoln County 1,314 43 2,556 100 KS Johnson County 1,291 44 226 857 SD Brown County 1,289 45 3,354 71 WY Sheridan County 1,283 46 4,286 57 CO Arapahoe County 1,254 47 203 897 SD Davison County 1,252 48 6,315 29 ND Williams County 1,239 49 3,914 64 ND Stark County 1,232 50 4,129 60

Estimated Rally Attendance by County: Top 50 Counties by Per Capita Attendance Estimated Rank Estimated Rank State County Sturgis (Attendance Attendance (Attendance Attendance Count) Per 100,000 Per Capita)

SD Lawrence County 22,685 1 91,254 1 SD Meade County 18,928 3 70,522 2 SD Butte County 6,861 4 66,817 3 WY Crook County 1,747 32 23,989 4 SD Pennington County 21,927 2 20,435 5 SD Harding County 260 368 20,348 6 MT Carter County 245 393 18,883 7 WY Weston County 1,121 61 15,626 8 MT Treasure County 117 798 13,792 9 SD Custer County 1,139 59 13,479 10 WY Campbell County 5,970 5 12,316 11 ND Bowman County 382 226 11,810 12 SD Jones County 90 979 11,771 13 MT Powder River County 176 560 10,653 14 SD Perkins County 319 283 10,564 15 SD Jackson County 323 277 9,897 16 SD Ziebach County 266 360 9,437 17 SD Hand County 310 299 9,350 18 ND Towner County 209 475 9,100 19 NE Sheridan County 461 173 8,763 20 SD Mellette County 182 547 8,722 21 WY Niobrara County 213 459 8,545 22 SD Edmunds County 303 307 7,568 23 SD Fall River County 483 158 7,025 24

ND Grant County 165 589 6,953 25 NE Grant County 43 1445 6,634 26 SD Walworth County 367 237 6,620 27 SD Douglas County 187 527 6,328 28 SD Davison County 1,252 48 6,315 29 SD Corson County 260 367 6,246 30 SD Charles Mix County 566 130 6,106 31 SD Aurora County 167 584 6,104 32 ND Hettinger County 161 599 6,098 33 NE Sherman County 182 545 5,890 34 SD Haakon County 120 771 5,798 35 ND Billings County 54 1299 5,764 36 NE Box Butte County 639 114 5,668 37 ND Eddy County 132 730 5,567 38 SD Sully County 81 1055 5,551 39 SD Turner County 455 177 5,493 40 SD Bennett County 186 530 5,413 41 MT Petroleum County 24 1647 5,413 42 SD Hyde County 73 1123 5,040 43 NE Dawes County 446 181 4,909 44 SD Moody County 310 295 4,816 45 NE Loup County 26 1643 4,752 46 ND Dunn County 203 490 4,745 47 SD Hughes County 817 85 4,666 48 IA Lyon County 544 135 4,640 49 NE Pierce County 320 281 4,462 50

Estimated Rally Attendees by County (Counts)

Estimated Rally Attendees by County (per 100,000 Residents)

Estimated Attendance among Majority-Native-American Counties (2000 Census) Percent Native Estimated Estimated Rank State County American Sturgis Attendance (Attendance (2000 Attendance Per 100,000 Per Capita) Census) Todd SD County 86% 156 1,561 173 Apache AZ County 77% 136 188 946 Dewey SD County 74% 99 1,754 150 Ziebach SD County 72% 266 9,437 17 Corson SD County 61% 260 6,246 30 Big Horn MT County 59% 465 3,516 70 Roosevelt MT County 56% 491 4,369 54 Mellette SD County 52% 182 8,722 21 Bennett SD County 52% 186 5,413 41 Thurston NE County 52% 79 1,129 250

Mobility-based Risks: Before, During, and After the Rally Analysis was iterated over two different mobility metrics, four geographies, and three time frames. ● Pre-rally metrics are useful to assess whether rally attendees exhibited baseline elevated risk of being infected with and transmitting COVID-19 even prior to attending the rally, based solely on observed mobility patterns. ● During-rally metrics serve to assess riskiness of behavior during the rally itself. ● After-rally metrics serve to assess the extent of social distancing compliance by attendees after returning home to their local communities, which correlates with the risk of their transmitting the virus to neighbors.

All comparisons exhibited more movement and more dwell-time away from home among rally goers, across all time periods: before, during, and after the rally. ● National comparisons show attendees travelled twice the distance of non-attendees before the rally, 137% more during the rally, and 86% more in the days following the rally. ● National comparisons show attendees spent between 15% and 20% less of their dwell-time at home relative to non-attendees. ● Though attendee behavior was consistently riskier than non-attendee behavior, differences tended to be extremely large during the rally; somewhat smaller before the rally; and smallest (but still substantial) in the days after the rally. These splits indicate modest but limited attempts by rally attendees towards more cautious behavior after returning home. ● Attendee/non-attendee differences tended to be larger in more distant states, and smallest in South Dakota, confirming the intuition that the farther a rally goer travelled to attend the rally, the less cautious their mobility patterns relative to their neighbors who did not attend.

Mobility Metrics in Select States, by Place of Residence, Before, During, After Rally Home-Dwell Time (Median) KM Travelled Daily (Median) Non- Non- Attendee Attendee Difference Diff (%) Attendee Attendee Difference Diff (%)

Before 83.1 67.4 -15.7 -19% 33.3 67.8 34.4 103%

During 82.2 66.2 -16.0 -19% 34.6 82.1 47.4 137%

USA After 80.6 67.4 -13.2 -16% 33.6 62.4 28.8 86%

Before 73.4 67.3 -6.1 -8% 41.2 59.2 18.0 44%

During 74.2 67.5 -6.6 -9% 40.4 65.4 25.0 62%

SD After 68.5 67.3 -1.2 -2% 36.9 53.7 16.7 45%

Before 81.2 66.9 -14.3 -18% 40.0 87.6 47.5 119%

During 81.6 67.8 -13.8 -17% 40.2 110.8 70.7 176%

MN After 80.3 68.4 -11.9 -15% 40.1 76.4 36.3 90%

Before 85.7 74.0 -11.7 -14% 31.6 72.6 41.0 130%

During 83.2 65.0 -18.2 -22% 34.9 125.9 91.0 261%

TX After 82.2 72.9 -9.3 -11% 33.2 71.1 37.8 114%

Kilometers Traveled Daily (Median) Before, During, and After Rally

Percent of Dwell Time at Home (Median) Before, During, and After Rally

Quarantine Order Compliance in Connecticut, New Jersey, Rhode Island, New York In the days after the rally (August 17 - September 4), mobility behavior of attendees returning to Connecticut, New Jersey, and Rhode Island (est’d 10,398 attendees) appears more risky than other residents of those states. During that period, they drove more kilometers (median: 151 v 55) daily and spent less dwell-time at home (65.6% v 77.2%) than non-attendees.

Home-Dwell Percent Post-Rally: 25th and 75th percentiles (CT, NJ, RI, NY)

Daily Kilometers Travelled Post-Rally: Median (CT, NJ, RI, NY)

Quarantine Order Compliance in Minnesota The Minnesota Department of Public Health Commissioner suggested residents consider the risks involved in attending the Sturgis motorcycle rally, and urged attendees to quarantine for 14 days upon return. On the contrary, analysis of de-identified geolocation data indicates that after returning from the rally, attendees (estimated 31,433) exhibited movement activity even riskier than non-attendees. From August 17th to September 4th, they drove more daily kilometers (median: 135 v 80) daily and spent less dwell-time at home (67.4% v 74.5%) than non-attendees.

Minnesotans’ Share of Dwell Time at Home post-Rally (median) Attendees in red, compared to non-attendees in blue

Minnesotan’s Daily Kilometers Travelled post-Rally (Median) Attendees in red, compared to non-attendees in blue