Archived video link now available for this webinar.
The early stages of the COVID-19 pandemic put tremendous strain on public health departments and created uncertainty for decision makers at all levels. At the same time, this emergency presented an opportunity for teams searching for novel approaches to integrate data science and predictive modeling into public health practice.
In this webinar, developed in partnership between the Washington State Department of Health and the University of Washington School of Public Health, you will learn how one team used predictive modeling to provide actionable information to decision makers during a public health crisis. First, you will identify the key elements of the infectious disease modeling workflow. Then, you will examine how data is applied to the Susceptible-Exposed-Infectious-Recovered (SEIR) modeling workflow. Finally, you will consider use cases for leveraging novel data to inform policy and integrating health equity principles into public health work.
Training Level
Beginner
Presenter
Ian Painter, PhD
Senior Epidemiologist, Washington State Department of Health
Learning Objectives
- Explain the key elements of the infectious disease modeling workflow
- Describe how data is applied to the Susceptible-Exposed-Infectious-Recovered (SEIR) modeling workflow
- Identify use cases to inform policy and integrate health equity principles
Slides
Using Pandemic Data Modeling to Inform Public Health Decisions slides