COVID-19 hotspot detection in a university setting.

The onset of the COVID-19 pandemic commenced an era of widespread disruptions in the academic world, including shut downs, periodic shifts to online learning, and disengagement from students. In an effort to transition back to in-person learning, many universities and schools tried to implement policy that balanced student learning with community health. While academic administrators have little control over some aspects of COVID-19 spread, they often choose to use temporary shutdowns of in-person teaching based on perceived hotspots of COVID-19. Specifically, if administrators have substantial evidence of within-group transmission for a class or other academic unit (a "hotspot"), the activities of that class or division of the university might be temporarily moved online. In this article, we describe an approach used to make these types of decisions. Using demographic information and weekly COVID-19 testing outcomes for university students, we use an XGBoost model that produces an estimated probability of testing positive for each student. We discuss variables engineered from the demographic information that increased model fit. As part of our approach, we simulate semesters under the null hypothesis of no in-class transmission, and compare the distribution of simulated outcomes to the observed group positivity rates to find an initial p-value for each group (e.g., section, housing area, or major). Using a simulation-based modification of a standard false discovery rate procedure, we identify possible hot spots-classes or groups whose COVID-19 rates exceed the levels expected for the demographic mix of students in each group of interest. We use simulation experiments and an anonymized example from Fall 2020 to illustrate the performance of our approach. While our example is based on hotspot detection in a university setting, the approach can be used for monitoring the spread of infectious disease within any interconnected organization or population.

Body shape perception in men and women without obesity during caloric restriction: a secondary analysis from the CALERIE study.

OBJECTIVE: To examine body shape perception in 218 adults without obesity or history of eating disorders during caloric restriction (CR). METHODS: Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) is a 2-year, randomized clinical trial using a 2:1 assignment (CR, 25% reduction in calories; Control, typical diet). For this secondary analysis, we examined perceived body shape using the Body Shape Questionnaire (BSQ). Analyses of BSQ scores are reported by group, over time, by sex, and by BMI. Data for body fat percentage, symptoms of depression, food cravings, maximal oxygen consumption, and stress were analyzed for their association with BSQ scores. RESULTS: Compared to control, CR reduced BSQ scores. Women tended to have greater concern with body shape than men across all measurement times. There was no difference in change in BSQ scores at 12 or 24 months between those with a BMI < 25 kg/m2 or ≥ 25 kg/m2. Change in body fat percentage was most correlated with change in BSQ score from 0 to 12 (r = 0.39) and 0-24 months (r = 0.38). For change in BSQ score, Akaike/ Bayesian information criterion (AIC/BIC) found that the model of best fit included the following three change predictors: change in body fat percentage, depression symptoms, and food cravings. For 0-12 months, AIC/BIC = 1482.0/1505.6 and for 0-24 months AIC/BIC = 1364.8/1386.5. CONCLUSIONS: CR is associated with reduced concern for body shape in men and women without obesity and with no history of eating disorders. Body shape perception among this sample was complex and influenced by multiple factors. LEVEL OF EVIDENCE: Level I, randomized controlled trial.