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

Randomized Clinical Trial of High-Dose Rifampicin With or Without Levofloxacin Versus Standard of Care for Pediatric Tuberculous Meningitis: The TBM-KIDS Trial

Background. Pediatric tuberculous meningitis (TBM) commonly causes death or disability. In adults, high-dose rifampicin may reduce mortality. The role of fluoroquinolones remains unclear. There have been no antimicrobial treatment trials for pediatric TBM. Methods. TBM-KIDS was a phase 2 open-label randomized trial among children with TBM in India and Malawi. Participants received isoniazid and pyrazinamide plus: (i) high-dose rifampicin (30 mg/kg) and ethambutol (R30HZE, arm 1); (ii) high-dose rifampicin and levofloxacin (R30HZL, arm 2); or (iii) standard-dose rifampicin and ethambutol (R15HZE, arm 3) for 8 weeks, followed by 10 months of standard treatment. Functional and neurocognitive outcomes were measured longitudinally using Modified Rankin Scale (MRS) and Mullen Scales of Early Learning (MSEL). Results. Of 2487 children prescreened, 79 were screened and 37 enrolled. Median age was 72 months; 49%, 43%, and 8% had stage I, II, and III disease, respectively. Grade 3 or higher adverse events occurred in 58%, 55%, and 36% of children in arms 1, 2, and 3, with 1 death (arm 1) and 6 early treatment discontinuations (4 in arm 1, 1 each in arms 2 and 3). By week 8, all children recovered to MRS score of 0 or 1. Average MSEL scores were significantly better in arm 1 than arm 3 in fine motor, receptive language, and expressive language domains (P < .01). Conclusions. In a pediatric TBM trial, functional outcomes were excellent overall. The trend toward higher frequency of adverse events but better neurocognitive outcomes in children receiving high-dose rifampicin requires confirmation in a larger trial. Clinical Trials Registration. NCT02958709

A dynamic cruise control system (DCCS) for effective navigation system

With the fast development of artificial intelligence, robotics, and embedded system along with sensor technologies, the speed control mechanism is required in various other applications such as automatic or self-piloting aircraft, auto-driven vehicles, auto driven lifts and much other robotics based automation plants, etc. For each unpredictable and progressed vehicular framework accompanies a better route that is fit for utilizing the two GPS and INS related sign. There have been a noteworthy number of research works being completed towards creating sliding mode control framework. In case of inaccurate navigational data or no availability of navigational service, the cruise control could also stop working. Hence, there is a need to evolve up with a novel system offering reliable and fault tolerant navigation system in order to minimize the dependencies on GPS-based information and maximize the utilization of INS based information. This manuscript presents a dynamic cruise control system to achieve better navigation under uncertainties. The performance of the system is analyzed by incorporating sliding mode and fuzzy logic and achieves better accuracy in tracking error, computational complexity (28 sec of simulation time) under chattering and switching action operation

Estimating the recreational value of Pakistan's largest freshwater lake to support sustainable tourism management using a travel cost model

Keenjhar Lake, Pakistan's largest freshwater lake and an important Ramsar site, provides habitat for internationally important water birds. Annually, 385,000 people visit the lake. The lake is threatened by a variety of causes, including industrial and agricultural pollution. To support its sustainable management and conservation, the lake's recreational value is estimated using an individual travel cost model. Randomly selected visitors are interviewed during peak season about their recreational travel behavior and perception of lake conditions. Key issues in travel cost modeling are addressed, including the opportunity cost of time, group travel, substitution and income effects, and endogenous stratification and truncation due to on-site sampling. Poisson and negative binomial regression models produce similar results. We find significant over-dispersion, and therefore, use the more conservative truncated negative binomial model results to estimate consumer surplus. The value of this assessment method for resource managers is illustrated by comparing the consumer surplus with existing pricing and budgeting mechanisms. The annual flow of benefits from lake recreation appears to be almost 50 times higher than the average entrance fee paid by the predominantly higher-income segments visiting the lake, suggesting scope for increasing fees and reallocating government budgets to finance the necessary lake protection measures. © 2013 Copyright Taylor and Francis Group, LLC