ABSTRACT
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.
ABSTRACT
BackgroundThe COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. MethodsBeginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. ResultsAnalysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. ConclusionsThis analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.
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OBJECTIVE: To analyze the process of a nursing clinical skills assessment in a hospital in China, design the scoring system of the nursing clinical skills(SSNCK), and discuss its clinical application effects. METHODS: To analyze the flow of the current practical skills assessment with an operation process analysis chart to identify potential improvement priorities. This was accomplished by developing the SSNCK with Microsoft Visual Basic. A total of 100 nurses were selected. They were randomly divided into an experimental group and a control group. The experimental group completed the SSNCK, while the other completed a paper-based assessment. The differences between the two groups in terms of testing time and costs were compared. RESULTS: The SSNCK simplified the process of nursing clinical skills assessment and efficiently allocated human, material, and financial resources. The time required to complete the SSNCK was less than that for the paper-based assessment (t=12.99, P<0.01), and the overall cost was lower than that for the other assessment (t=13.56, P<0.01). CONCLUSION: The application of the SSNCK improves the efficiency of a nursing practical assessment. It also reduces testing costs and further develops hospital nursing education.
Subject(s)
Clinical Competence , Nursing Assessment , Nursing Staff , China , HumansABSTRACT
WHAT IS KNOWN AND OBJECTIVE: Patients with rheumatic disease are at risk for infections. Evaluating antitumour necrosis factor (anti-TNF) drug-associated risk of infections requires justification of baseline risk in the population at high risk of infection. We examined the incidence of active tuberculosis (TB) and its risk factors in patients with rheumatic disease started with anti-TNF-α therapy or with existing disease-modifying antirheumatic drug (DMARD) therapy. METHODS: A retrospective cohort study of anti-TNF-α therapy new users (anti-TNF-α group) and those starting with a DMARD after the failure of at least one other DMARD or who had added to existing DMARD treatment (DMARD group) for rheumatic disease in the largest medical setting in Taiwan from 1 January 2005 through 31 November 2013 was conducted to determine relative risk of TB between patient groups. Patients in the DMARD group were stratified into "mild" and "severe" disease severity as proxies for low and high background risk of infection. RESULTS AND DISCUSSION: A total of 3640 patients were enrolled (anti-TNF: 955; DMARD: 2685). The incidence of TB was 903.9/100 000 patient-years for anti-TNF-α new users and 391.7/100 000 patient-years for DMARD switchers. In Cox regression model, adjusted HR for TB in the anti-TNF-α group was higher than for the entire DMARD group (aHR, 2.41; 95% confidence interval [CI], 1.2-4.85), subgroup with mild disease (2.91; 1.31-6.47) and subgroup with severe disease (1.65; 0.68-4.03). Significant independent risk factors for TB were being male, age ≥60 years, history of respiratory disease, glucocorticoids dose >7.5 mg/d and living in a TB-prevalent region. WHAT IS NEW AND CONCLUSION: Anti-TNF-α therapy was independently associated with increased risk of TB in patients with mild disease, but it was not significantly correlated in patients with severe disease after adjusting for confounders.
