Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Objective: Metformin was assessed as an interventional medication for weight gain in children and adolescents taking atypical antipsychotic agents. Method: A 12-week open-label trial was conducted to evaluate metformin's effectiveness and safety for weight management. Eleven subjects, ages 10-18 years, participated in the study. Each subject received metformin orally up to 2000 mg=day. Primary outcome measures included weight, body mass index (BMI), and waist circumference. Secondary outcome measures included serum glucose, insulin, and fasting lipid profile. Changes in weight, BMI, waist, and metabolic profile were obtained by using repeated measures of covariance. Results: The mean reduction in weight, waist, BMI, serum glucose, and serum insulin was not statistically significant. However, 5 out of 11 patients lost weight (mean, À2.82 kg AE 7.25), and overall the sample did not continue to gain weight. There was a significant decrease in triglyceride levels. Metformin was fairly well tolerated. Conclusion: Preliminary data suggests that metformin may safely and effectively improve the triglyceride profile. However, contrary to study hypotheses, weight, waist, and BMI reduction were not statistically significant. Future double-blind studies with larger sample sizes and of longer duration are warranted to assess more fully the safety and efficacy of this intervention.