Association of National Football League Fan Attendance With County-Level COVID-19 Incidence in the 2020-2021 Season.

Importance: The 2020-2021 National Football League (NFL) season had some games with fans and others without. Thus, the exposed group (ie, games with fans) and the unexposed group (games without fans) could be examined to better understand the association between fan attendance and local incidence of COVID-19. Objective: To assess whether NFL football games with varying degrees of in-person attendance were associated with increased COVID-19 cases in the counties where the games were held, as well as in contiguous counties, compared with games without in-person attendance for 7-, 14-, and 21-day follow-ups. Design, Setting, and Participants: This cross-sectional study used data for all 32 NFL teams across the entirety of the 2020-2021 season. Separate daily time-series of COVID-19 total cases and case rates were generated using 7-, 14-, and 21-day simple moving averages for every team and were plotted against the actuals to detect potential spikes (outliers) in incidence levels following games for the county in which games took place, contiguous counties, and a combination. Outliers flagged in the period following games were recorded. Poisson exact tests were evaluated for differences in spike incidence as well across games with different rates of attendance. The data were analyzed between February 2021 and March 2021. Exposures: Games with fan attendance vs games with no fan attendance, as well as the number of fans in attendance for games with fans. Main Outcomes and Measures: The main outcome was estimation of COVID-19 cases and rates at the county and contiguous county level at 7-, 14-, and 21-day intervals for in-person attended games and non-fan attended games, which was further investigated by stratifying by the number of persons in fan-attended games. Results: This included a total of 269 NFL game dates. Of these games, 117 were assigned to an exposed group (fans attended), and the remaining 152 games comprised the unexposed group (unattended). Fan attendance ranged from 748 to 31 700 persons. Fan attendance was associated with episodic spikes in COVID-19 cases and rates in the 14-day window for the in-county (cases: rate ratio [RR], 1.36; 95% CI, 1.00-1.87), contiguous counties (cases: RR, 1.31; 95% CI, 1.00-1.72; rates: RR, 1.41; 95% CI, 1.13-1.76), and pooled counties groups (cases: RR, 1.34; 95% CI, 1.01-1.79; rates: RR, 1.72; 95% CI, 1.29-2.28) as well as for the 21-day window in-county (cases: RR, 1.49; 95% CI, 1.21-1.83; rates: RR, 1.50; 95% CI, 1.26-1.78), in contiguous counties(cases: RR, 1.37; 95% CI, 1.14-1.65; rates: RR, 1.45; 95% CI, 1.24-1.71), and pooled counties groups (cases: RR, 1.41; 95% CI, 1.11-1.79; rates: RR, 1.70; 95% CI, 1.35-2.15). Games with fewer than 5000 fans were not associated with any spikes, but in counties where teams had 20 000 fans in attendance, there were 2.23 times the rate of spikes in COVID-19 (95% CI, 1.53 to ∞). Conclusions and Relevance: In this cross-sectional study of the presence of fans at NFL home games during the 2020-2021 season, results indicated that fan attendance was associated with increased levels of COVID-19 in the counties in which the venues are nested within, as well as in surrounding counties. The spikes in COVID-19 for crowds of over 20 000 people suggest that large events should be handled with extreme caution during public health event(s) where vaccines, on-site testing, and various countermeasures are not readily available to the public.

Correction to: Measuring the impact of the COVID­19 lockdown on crime in a medium­sized city in China.

[This corrects the article DOI: 10.1007/s11292-021-09486-7.].

Measuring the impact of the COVID-19 lockdown on crime in a medium-sized city in China.

OBJECTIVES: The study examines the variation in the daily incidence of eight acquisitive crimes: automobile theft, electromobile theft, motorcycle theft, bicycle theft, theft from automobiles, pickpocketing, residential burglary, and cyber-fraud before the lockdown and the duration of the lockdown for a medium-sized city in China. METHODS: Regression discontinuity in time (RDiT) models are used to test the effect of the lockdown measures on crime by examining the daily variation of raw counts and rate. RESULTS: It is indicated that in contrast to numerous violent crime categories such as domestic violence where findings have repeatedly found increases during the COVID-19 pandemic, acquisitive crimes in this city were reduced during the lockdown period for all categories, while "cyber-fraud" was found more resilient in the sense that its decrease was not as salient as for most other crime types, possibly due to people's use of the internet during the lockdown period. CONCLUSIONS: The findings provide further support to opportunity theories of crime that are contingent upon the need for a motivated offender to identify a suitable target in physical space.

A Case Study of Family Violence During COVID-19 in San Antonio

The current study investigates the effects of coronavirus restrictions on family violence in the seventh largest city in the country, San Antonio, Texas. Two streams of data were used to evaluate the potential change between what occurred during the lockdown period versus what would have been expected, including the COVID-19 Government Response Stringency Index and police calls for service from the San Antonio Police Department. The methodological approach used takes advantage of feature engineering, various machine learning time series forecasting techniques commonly leveraged in financial technical analysis, as well as cross-validation for optimized model selection. These techniques have not been considered in previous domestic or family violence-related research. During the lockdown period in San Antonio, we observed a larger than expected increase in calls to police for family violence incidents. Specifically, an increase of over fourteen percent of police calls for family incidents was observed. The findings of the current study suggest that social service and social welfare agencies consider and plan for how future pandemics or other major disasters will affect the incidence of family violence and take appropriate steps now to bolster resources and scale up for the future.

Where, When, and By Whom: An Exploratory Analysis of COVID-19 Public Health Violations

COVID-19 health restrictions not only affected crime rates but also created a new and temporary type of crime, COVID-19 public health violations. Unfortunately, this new crime type has not yet been empirically scrutinized. The current study is the first to explore these COVID-19 public health violations by using a dataset created by the City of San Antonio which documents all calls and inspections about COVID-19 public health violations. Specifically, this study investigates the location types (where) that produce the greatest number of calls/inspections, warnings, and citations for COVID-19 public health violations;how they trended over time (when);and which agencies responded to and enforced them (who). The results indicate that there were differences across location type, variation throughout the observation period, and violations were enforced by several agencies. It is crucial to document the effect of COVID-19-related policies so that we may be better prepared for the future. [ABSTRACT FROM AUTHOR] Copyright of Journal of Contemporary Criminal Justice is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China.

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.