Abstract: Near-term disease forecasting and scenario projection efforts rely on the availability of data to train and evaluate model performance. In most cases, more extensive epidemiological time series data can lead to better modeling results and improved public health insights. Here we describe a procedure to augment an epidemiological time... MORE

Abstract: Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS),... MORE

Abstract: Operational infectious disease modeling efforts are important to public health response before, during, and after outbreak events. Recent collaborative initiatives have demonstrated the success of hubs, which openly solicit modeling output from contributors. Automation techniques can help improve timeliness of submissions to hubs. Furthermore, we suggest that containerization approaches... MORE

Abstract: Unbiased long read sequencing holds enormous potential for the detection of pathogen sequences in clinical samples. However, the untargeted nature of these methods precludes conventional PCR approaches, and the metagenomic content of each sample increases the challenge of bioinformatic analysis. Here, we evaluate a previously described novel workflow for... MORE

Abstract: Unbiased long read sequencing approaches for clinical metagenomic sample analysis holds enormous potential for pathogen detection, including improved detection of unknown, novel or emerging viruses. However, the rapid rate of development in nanopore sequencing and library preparation methods complicates the process of selecting a standardized method for unbiased RNA... MORE