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.

Eighty-one adults with symptoms of acute sinusitis were studied by direct needle puncture and aspiration of the maxillary sinus (105 sinuses). Fifty-nine bacterial strains were isolated in titers of greater than or equal to 10(4) colony-forming units/ml; Streptococcus pneumoniae and Haemophilus influenzae accounted for 64% of the isolates. Other bacteria recovered included anaerobes (12%), Neisseria species (8.5%). Streptococcus pyogenes (3%), alpha-hemolytic Streptococcus (3%), non-group A beta-hemolytic Streptococcus (3%), Staphylococcus aureus (2%), Pseudomonas aeruginosa (2%), and Escherichia coli (2%). Viruses were isolated from 11 sinuses; these isolates included rhinovirus (six), influenza A (H3N2) virus (three), and two types of parainfluenza virus (one each). The efficacy of therapy with orally administered ampicillin, amoxicillin, or trimethoprim-sulfamethoxazole was evaluated by a repeat sinus puncture and culture. Clinical and bacteriologic responses to all three regimens were good.

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To evaluate procedures used for epidemiologic analysis of outbreaks of aspergillosis, we analyzed a collection of 35 Aspergillus fumigatus isolates using three typing methods: isoenzyme analysis (IEA), random amplified polymorphic DNA (RAPD) analysis, and restriction endonuclease analysis (REA). Twenty-one isolates were from a single hospital, with four isolates coming from different patients. Three clinical isolates came from a different hospital, and 11 clinical or environmental isolates were derived from a culture collection. With IEA, the patterns of alkaline phosphatase, esterase, and catalase discriminated nine types. In contrast, 22 types were obtained with five different RAPD primers, and 21 types could be detected with three of these (R108, R151, and UBC90). Restriction endonuclease analysis of genomic DNA, digested with either XbaI, XhoI, or SalI, detected 3, 17, and 13 different REA types, respectively, and 22 types were identified by combining the data from the XhoI and SalI REAs. Twenty-eight types were obtainable with a combination of REA, IEA, and RAPD patterns. Overall, the results pointed to substantial genetic variation among the isolates. Though two isolates had markedly distinct genotypes, their morphologic features and exoantigens were consistent with their being A. fumigatus. The analysis will help in planning epidemiologic studies of aspergillosis.

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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.

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We controlled the spread of epidemic methicillin-resistant Staphylococcus aureus (MRSA) infection in an 884-bed veterans' facility by cohorting known active MRSA carriers and MRSA-infected patients on one nursing unit. Simultaneously, all previously-institutionalized transfers into the veterans' facility were screened with swab cultures for MRSA at the time of admission. All MRSA patients were maintained on contact (gown and glove) or strict isolation and treated aggressively with topical and enteral antibiotics with the assistance of the infectious disease consultant. The monthly incidence of new MRSA patients dropped from a maximum of 16 per month to three or less per month within six months of instituting these infection control measures. There were no further MRSA bacteremias after the establishment of the MRSA cohort in a single unit. Aggressive cohort management of known MRSA patients and screening of previously-institutionalized patients on admission for MRSA controlled epidemic MRSA in this large institution.