The contagion externality of a superspreading event: The Sturgis Motorcycle Rally and COVID‐19

Abstract

Large in‐person gatherings of travelers who do not socially distance are classified as the “highest risk” for COVID‐19 spread by the Centers for Disease Control and Prevention (CDC). From August 7–16, 2020, nearly 500,000 motorcycle enthusiasts converged on Sturgis, South Dakota for its annual rally in an environment without mask‐wearing requirements or other mitigating policies. This study is the first to explore this event's public health impacts. First, using anonymized cell phone data, we document that foot traffic at restaurants/bars, retail establishments, and entertainment venues rose substantially at event locations. Stay‐at‐home behavior among local residents fell. Second, using a synthetic control approach, we find that the COVID‐19 case rate increased substantially in Meade County and in the state of South Dakota in the month following the Rally. Finally, using a difference‐in‐differences model to assess nationwide spread, we find that following the Sturgis event, counties outside of South Dakota that contributed the highest inflows of rally attendees experienced a 6.4–12.5% increase in COVID‐19 cases relative to counties without inflows. Our findings highlight that local policy decisions assessing the tradeoff between local economic benefits and COVID‐19 health costs will not be socially optimal in the presence of large contagion externalities.

Should masks be worn outdoors?

“…One proposed reason for the observed differences is that the Sturgis Rally was associated with lower compliance with non-pharmaceutical interventions (NPIs), such as mask wearing and physical distancing, that are associated with decreased transmission risk. Observational data showed that attendees from regions with stricter NPI mandates were associated with lower onward transmission risk than attendees with weaker such mandates 9…”

“…At the time, many experts and pundits warned that such protests may fuel large transmission clusters for covid-19, but these fears were not realised 8. By contrast, the mass outdoor Sturgis Motorcycle Rally in South Dakota, USA, is considered to have been the trigger for a huge superspreading-type event that resulted in a devastating chain of covid-19 transmission and disease 9. One proposed reason for the observed differences is that the Sturgis Rally was associated with lower compliance with non-pharmaceutical interventions (NPIs), such as mask wearing and physical distancing, that are associated with decreased transmission risk.…”

Do pandemics lead to rebellion? Policy responses to COVID-19, inequality, and protests in the USA

“…Endogeneity may, however, persist if policy stringency levels and protests are correlated for other reasons. For instance, the level of policy stringency might be shaped by previous protests when they acted as super spreaders (see Dave et al 2020aDave et al , 2020bDave et al , 2020cDave et al , 2021. To further address these concerns, we test if trends in the outcome variable (probability of protests) before the outbreak of the COVID-19 pandemic are parallel across different levels of exposure to the treatment (different levels of policy stringency and different levels of inequality).…”

The Impact of the first COVID-19 shelter-in-place announcement on social distancing, difficulty in daily activities, and levels of concern in the San Francisco Bay Area: A cross-sectional social media survey

“…For the present study, we employed convenience sampling to rapidly ascertain and summarize the impact of the announcement of the nation’s first SIPO on March 16, 2020. More specifically, we use a difference-in-differences estimator to estimate changes among respondents living in the seven counties in the San Francisco Bay Area affected by the announcement versus those living elsewhere in the U.S. A large body of COVID-19 literature has employed quasi-experimental methods to examine the impact of the pandemic on a variety of topics including superspreader events [ 21 – 23 ], air pollution [ 24 , 25 ], unemployment [ 26 ], and demand for online shopping [ 27 ]. Many of these quasi-experimental studies have focused on changes in human mobility using aggregated smartphone-based measures such as time spent at home [ 28 , 29 ].…”