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

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 multimodel ensemble forecast that combined predictions from dozens of 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-wk horizon three to five times larger than when predicting at a 1-wk 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

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Pure cycles in two-machine dual-gripper robotic cells

We consider a robotic cell served by a dual-gripper robot that consists of identical CNC machines placed linearly and a material handling robot loading/unloading the machines and transporting the parts between them. Identical parts are to be processed in this system and the CNC machines are capable of performing all the operations that a part requires. We consider the problem of sequencing activities of the robot in order to maximize the throughput rate. As a consequence of the flexibility of the CNC machines, a new class of robot move sequences, named as pure cycles, arises. In a pure cycle, the robot loads and unloads each machine once and each part is processed on exactly one of the machines. Thereby, the problem is to determine the best pure cycle that maximizes the throughput rate. We first determine the feasibility conditions for the pure cycles and prove some basic results that reduces the number of feasible pure cycles to be investigated. We analyze 2-machine robotic cells in detail and prove that five of the cycles among a huge number of feasible pure cycles dominate the rest. We determine the parameter regions in which each of the five cycles is optimal. We also analyze the performance improvement that can be attained by using a dual gripper robot and provide managerial insights

Model to Calculate Harms and Benefits of Early vs Delayed Liver Transplantation for Patients with Alcohol-Associated Hepatitis.

BACKGROUND & AIMS: Early liver transplantation (without requiring a minimum period of sobriety) for severe alcohol-associated hepatitis (AH) is controversial-many centers delay eligibility until a specific period of sobriety (such as 6 months) has been achieved. To inform ongoing debate and policy, we modeled long-term outcomes of early vs delayed liver transplantation for patients with AH. METHODS: We developed a mathematical model to simulate early vs delayed liver transplantation for patients with severe AH and different amounts of alcohol use after transplantation: abstinence, slip (alcohol use followed by sobriety), or sustained use. Mortality of patients before transplantation was determined by joint-effect model (based on model for end-stage liver disease [MELD] and Lille scores). We estimated life expectancies of patients receiving early vs delayed transplantation (6-month wait before placement on the waitlist) and life-years lost attributable to alcohol use after receiving the liver transplant. RESULTS: Patients offered early liver transplantation were estimated to have an average life expectancy of 6.55 life-years, compared to an average life expectancy of 1.46 life-years for patients offered delayed liver transplants (4.49-fold increase). Net survival benefit from early transplantation was highest for patients with Lille scores of 0.50-0.82 and MELD scores of 32 or more. Patients who were offered early transplantation and had no alcohol use afterward were predicted to survive 10.85 years compared to 3.62 years for patients with sustained alcohol use after transplantation (7.23 life-years lost). Compared with delayed transplantation, early liver transplantation increased survival times in all simulated scenarios and combinations of Lille and MELD scores. CONCLUSIONS: In a modeling study of assumed carefully selected patients with AH, early vs delayed liver transplantation (6 months of abstinence from alcohol before transplantation) increased survival times of patients, regardless of estimated risk of sustained alcohol use following transplant. These findings support early liver transplantation for patients with severe AH. The survival benefit was maintained in all simulated extreme scenarios, but should be confirmed in prospective studies. Sustained alcohol use following transplantation significantly reduced but did not eliminate the benefits of early transplantation-strategies are needed to prevent and treat post-transplantation use of alcohol

Patterns of Alcohol Use After Early Liver Transplantation for Alcoholic Hepatitis.

BACKGROUND & AIMS: Early liver transplantation (LT) for alcoholic hepatitis (AH) is lifesaving but concerns regarding return to harmful alcohol use remain. We sought to identify distinct patterns of alcohol use post-LT to inform pre-LT candidate selection and post-LT addiction care. METHODS: Detailed post-LT alcohol use data was gathered retrospectively from consecutive patients with severe AH at 11 ACCELERATE-AH sites from 2006-2018. Latent class analysis identified longitudinal patterns of alcohol use post-LT. Logistic and Cox regression evaluated associations between patterns of alcohol use with pre-LT variables and post-LT survival. A microsimulation model estimated the effect of selection criteria on overall outcomes. RESULTS: Of 153 LT recipients, 1-, 3-, and 5-year survival were 95%, 88% and 82%. Of 146 LT recipients surviving to home discharge, 4 distinct longitudinal patterns of post-LT alcohol use were identified: Pattern 1 [abstinent](n = 103;71%), pattern 2 [late/non-heavy](n = 9;6.2%), pattern 3 [early/non-heavy](n = 22;15%), pattern 4 [early/heavy](n = 12;8.2%). One-year survival was similar among the 4 patterns (100%), but patients with early post-LT alcohol use had lower 5-year survival (62% and 53%) compared to abstinent and late/non-heavy patterns (95% and 100%). Early alcohol use patterns were associated with younger age, multiple prior rehabilitation attempts, and overt encephalopathy. In simulation models, the pattern of post-LT alcohol use changed the average life-expectancy after early LT for AH. CONCLUSIONS: A significant majority of LT recipients for AH maintain longer-term abstinence, but there are distinct patterns of alcohol use associated with higher risk of 3- and 5-year mortality. Pre-LT characteristics are associated with post-LT alcohol use patterns and may inform candidate selection and post-LT addiction care

Gender-related clinical and management differences in patients with chronic heart failure with reduced ejection fraction

Aim Gender-related differences have been described in the clinical characteristics and management of patients with chronic heart failure with reduced ejection fraction (HFrEF). However, published data are conflictive in this regard

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

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 multimodel ensemble forecast that combined predictions from dozens of 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-wk horizon three to five times larger than when predicting at a 1-wk 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