SAN DIEGO (KGTV) — The 10-day Sturgis motorcycle rally held in August led to more than 266,000 new coronavirus cases, according to a new study from San Diego State University.
The study, released this week, says upwards of 460,000 people converged on Sturgis, a South Dakota city of 7,000, causing a bump in Coronavirus infections across the US.
"Large crowds, coupled with minimal mask-wearing and social distancing by attendees, raised concerns that this event could serve as a COVID-19 'super spreader,'" said the study, which did conclude that the rally was a super spreader event.
Researchers from San Diego State's Center for Health Economics and Policy Study used anonymized cell phone data to track where attendees came from, then traced it back to their counties. They found that the counties that had the highest numbers of Sturgis attendees saw a 7% to 12% increase in coronavirus cases.
The study also said that CDC data shows that cases in Meade County, South Dakota, where the rally is held, increased 6 to 7 cases per 1,000 population a month after the event started.
Descriptive evidence suggests these effects may be muted in states with stricter mitigation policies (i.e., restrictions on bar/restaurant openings, mask-wearing mandates)," the study says.
In all, the study says that led to more than 266,000 new COVID-19 cases nationwide.
The study says these cases accounted for an additional $12.2 billion in health costs, enough to pay each attendee $26,000 not to have attended the rally.
South Dakota Governor Kristi Noem called the study fiction, noting it was not peer-reviewed and based on "incredibly faulty assumptions." Her statement did not elaborate on what those assumptions were and what she considered faulty.
"This report isn’t science; it’s fiction. Under the guise of academic research, this report is nothing short of an attack on those who exercised their personal freedom to attend Sturgis,” Noem's statement read in part. “Predictably, some in the media breathlessly report on this non-peer reviewed model, built on incredibly faulty assumptions that do not reflect the actual facts and data."