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From 2023-2024, CSTE partnered with several academic and industry groups to develop analytic tools for public health outbreak response. In August, 2024, Signature Science's VP Nagraj delivered a presentation CTSE-hosted webinar series on the PLANES approach and rplanes software developed through this collaboration. Register to watch the presentation and presentations... MORE

Abstract:Methods for reviewing epidemiological signals are necessary to building and maintaining data-driven public health capabilities. We have developed a novel approach for assessing the plausibility of infectious disease forecasts and surveillance data. The PLANES (PLausibility ANalysis of Epidemiological Signals) methodology is designed to be multi-dimensional and flexible, yielding an overall score based on... MORE

The Flu Forecasting Interpretation Visualization and Evaluation (FluFIVE) web application provides a user-friendly interface to explore probabilistic near-term forecasts of incident influenza hospitalizations in the United States. The source of forecasts is the CDC FluSight initiative, which since 2013 has served as a consortium to coordinate influenza forecasting efforts in the United States. FluFIVE provides forecast... MORE

Abstract: Methods for reviewing epidemiological signals are necessary to building and maintaining data-driven public health capabilities. We have developed a novel approach for assessing the plausibility of infectious disease forecasts and surveillance data. The PLANES (PLausibility ANalysis of Epidemiological Signals) methodology is designed to be multi-dimensional and flexible, yielding an overall score based... MORE

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

fiphde was originally created to operationally forecast influenza hospitalizations during the 2021-22 and 2022-23 seasons of the FluSight challenge. The package includes functions for data retrieval, modeling, near-term forecasting, and forecast summarization. Functionality from fiphde has been implemented for other infectious disease forecasting, including the 2022-23 DoD COVID-like illness forecasting... MORE

Abstract Near-term probabilistic forecasts for infectious diseases such as COVID-19 and influenza play an important role in public health communication and policymaking. From 2013-2019, the FluSight challenge run by the Centers for Disease Control and Prevention invited researchers to develop and submit forecasts using influenza-like illness (ILI) as a measure... MORE

Abstract: Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to... MORE