Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

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Background: Shoulder injuries account for a large portion of all recorded injuries in professional baseball. Much is known about other shoulder pathologies in the overhead athlete, but the incidence and impact of acromioclavicular (AC) joint injuries in this population are unknown. We examined the epidemiology of AC joint injuries in Major League Baseball (MLB) and Minor League Baseball (MiLB) players and determined the impact on time missed. Methods: The MLB Health and Injury Tracking System was used to compile records of all MLB and MiLB players from 2011 to 2017 with documented AC joint injuries. These injuries were classified as acute (sprain or separation) or chronic (AC joint arthritis or distal clavicular osteolysis), and associated data extracted included laterality, date of injury, player position, activity, mechanism of injury, length of return to play, and need for surgical intervention. Results: A total of 312 AC joint injuries (183 in MiLB players and 129 in MLB players; range, 39-60 per year) were recorded: 201 acute (64.4%) and 111 chronic (35.6%). A total of 81% of acute and 59% of chronic injuries resulted in time missed, with a mean length of return to play of 21 days for both. Of the injuries in outfielders, 79.6% were acute (P < .0001), as were 66.3% of injuries in infielders (P ¼ .004). Pitchers and catchers had more equal proportions of acute and chronic AC injuries (P > .05 for all). Acute AC injuries occurred most often while fielding (n ¼ 100, 84.7%), running (n ¼ 25, 80.6%), and hitting (n ¼ 19, 61.3%), whereas chronic injuries tended to be more common while pitching (n ¼ 26, 68.4%). Of contact injuries, 82.5% were acute (P < .0001), whereas 59.0% of noncontact injuries were chronic (P ¼ .047). MLB players showed consistently higher regular-season rates of both acute and chronic AC injuries than MiLB players (P < .0001 for each). Conclusion: Acute AC joint injuries are contact injuries occurring most commonly among infielders and outfielders while fielding that result in 3 weeks missed before return to play, whereas chronic AC joint injuries occur more commonly in pitchers and catchers from noncontact repetitive overhead activity. Knowledge of these data can better guide expectation management in this elite population to better elucidate the prevalence of 2 common injury patterns in the AC joint. The Institutional Review Board of the Johns Hopkins University approved this study; the study procedures have been approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.