Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

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 naive 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 StatementThis 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.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States

We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number ([Formula]). In particular, the forecasts based on our model suggest that the peak date of the US, New York state, and California state are 06/01/2020, 05/10/2020, and 07/01/2020 respectively. In addition, the estimated [Formula] of the US, New York state, and California state are 2.5, 3.6 and 2.2 respectively. The prediction results for all states in the US can be found on our project website: https://covid19.uclaml.org, which are updated on a weekly basis, and have been adopted by the Centers for Disease Control and Prevention (CDC) for COVID-19 death forecasts (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html).

Effect of di-(2-ethylhexyl)phthalate and its metabolite mono(2-ethylhexyl)phthalate on spermatogenic cell apoptosis in young male Wistar rats / 南方医科大学学报

<p><b>OBJECTIVE</b>To explore the influences of di-(2-ethylhexyl)phthalate (DEHP) and its principle metabolite mono(2-ethylhexyl)phthalate (MEHP) on spermatogenic cell apoptosis in young male Wistar rats.</p><p><b>METHODS</b>Ninety-eight 2-week-old male Wistar rats were randomly divided into 14 equal groups to receive daily intragastric administration of 0.2 ml/kg normal saline for 3 weeks (normal control), 100 mg/kg cyclophosphamide (CTX) for 1 week (positive control), 100, 200, and 300 mg/kg DEHP or MEHP for 1 week, or 100 mg/kg DEHP or MEHP for 1, 2, and 3 weeks. After the treatments, the pathological changes of the testicular tissues were examined, spermatogenic cell apoptosis was detected, and serum sex hormones levels were measured using TUNEL assay or radioimmunoassays.</p><p><b>RESULTS</b>CTX, DEHP, and MEHP all caused shrinkage, development retardation and quantitative reduction of spermatogenic cells with and mitochondrial swelling vacuolar changes. The damage of spermatogenic cells increased significantly with the increment of DEHP and MEHP doses and exposure time. Both DEHP and MEHP treatments resulted in significantly increased cell apoptosis index (AI) in close correlation with the exposure doses and duration (P<0.01). DEHP and MEHP treatments also significantly increased serum levels of follicle stimulating hormone and luteinizing hormone and decreased testosterone levels in a dose- and time-dependent manner (P<0.05).</p><p><b>CONCLUSION</b>DEHP and MEHP can induce obvious apoptosis of spermatogenic cells in young male rats with a dose- and time-dependent effect.</p>

Determination of ginsenoside Rg_1 and Re in Shenqi Granula by HPLC / 中草药

Objective To establish an HPLC method for the determination of ginsenosides Rg1 and Re in Shenqi Granula.Methods Chromasil C18 column(250 mm?4.6 mm)was used with acetonitrile-0.05% phosphoric acid solution(21∶79)as mobile phase.The flow rate was 1 mL/min and the detected wavelength was 203 nm.Results Ginsenosides Rg1 and Re could be baseline separated with in 30 min.The average recovery rates were 99.60% and 98.5%,corresponding RSD were 1.93% and 2.31% for ginsenoside Rg1 and Re,respectively(n=5).Conclusion This method is fast and accurate and can be used for quality control of Shenqi Granula.