From 2023-2024, CSTE partnered with several academic and industry groups to develop analytic tools for public health outbreak response.

This course will present demonstrations from these groups on their developed tools. The presentations were provided by the following organizations.

Course objectives:
1. Explain the importance of assessing epidemiological data plausibility and use appropriate tools (e.g., rplanes) to conduct and interpret plausibility analyses. 
2. Describe the key components of mechanistic infectious disease models and apply a forecasting platform (e.g., PROF) to fit models, interpret outputs, and evaluate different modeling scenarios. 
3. Apply infectious disease modeling concepts (e.g.RSV) including model calibration, scenario analysis, and communication of results using public tools and data. 

Signature Science
In this webinar, we'll hear from Signature Science who has developed an open-source R package called rplanes that allows users to analyze the plausibility of epidemiological signals, including surveillance data and near-term forecasts.

Brown University
In this webinar, Brown University will present on Napkin Math, an approach to analytic thinking that uses simple models of the world both to provide a “first pass” answer to policy questions and to inform interpretation of more complex ones.

Predictive Science Inc.
In this webinar, Predictive Science Inc. (PSI) will present their PROF tool, an open-source, GUI-supported R package. PROF ingests publicly (or user-uploaded) hospitalization admission data, fits mechanistic and/or statistical models to the data, and provides 1-4 weeks ahead probabilistic forecasts. The presentation will highlight PROF’s capability to forecast the combined burden of multiple respiratory viruses.

University of Washington
In this webinar, University of Washington will present on their tool, which utilizes R and is a compartmental transmission model (SIR-type model) that allows users to create scenario projections for the impact of RSV immunizations (monoclonal antibodies for infants and vaccines for pregnant persons and adults over 60) on RSV hospitalizations.

Lesson 1: Signature Science

By the end of the lesson, participants will be able to:

1. Explain motivations for assessing the integrity of epidemiological signals. 
2. Install the rplanes R package. 
3. Describe the input format expected to analyze data with rplanes. 
4. Describe the steps necessary to run plausibility analysis programmatically and interactively with the rplanes explorer interface. 
5. Identify rplanes documentation and narrative vignettes. 

Lesson 2: Brown University

By the end of the lesson, participants will be able to:

1. Describe aiming off. 
2. Describe bounding. 
3. Describe benchmarking against a simple model. 

Lesson 3: Predictive Sciences Inc. 

By the end of the lesson, participants will be able to:

1. Describe the general capabilities and utility of PROF. 
2. Identify the data types and models that PROF supports. 
3. Explain the output that PROF generates. 
4. Outline the order of operations when fitting and forecasting one or two pathogens. 
5. Identify the key decisions the user needs to make in setting up and running a mechanistic model. 
6. Locate the resources explaining advanced topics in PROF. 
7. Describe how to incorporate one of the advanced features in a specific PROF run. 
8. Identify how to import data into PROF to produce a probabilistic forecast. 

Lesson 4: University of Washington

By the end of the lesson, participants will be able to:

1. Describe components of an RSV transmission model. 
2. Identify data needed to calibrate an RSV transmission model for RSV hospitalizations. 
3. Identify publicly available R code to calibrate an RSV transmission model using maximum likelihood estimation. 
4. Describe scenarios for RSV immunizations in infants and seniors. 
5. Identify publicly available R code to project RSV hospitalizations under different scenarios using a sample dataset. 
6. Illustrate results using a publicly available R shiny app. 
7. Describe the use of the model and identify model limitations. 

This training series was funded by CDC Cooperative Agreement No: 1 NU38OT000297-03-00. The contents of this training are solely the responsibility of the authors and do not necessarily represent the official views of CDC.

• 1.4 – Data Analytics and Assessment Skills – Conducts surveillance activities
• 1.6 – Data Analytics and Assessment Skills – Manages data
• 1.7 – Data Analytics and Assessment Skills – Analyzes data
• 1.8 – Data Analytics and Assessment Skills – Interprets results from data analysis
• 2.3 – Public Health Sciences Skills – Applies public health informatics in using epidemiologic data, information, and knowledge
• 2.4 – Public Health Sciences Skills – Manages information systems to promote effectiveness and security of data collection, processing, and analysis