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MIDAS Webinar: Towards Modeling Infectious Behaviors

November 19, 2021
online

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DATES:
November 19, 2021
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online
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DESCRIPTION

The MIDAS Webinar Series features research by MIDAS members, and is open to the public. 

Date: Friday, November 19th
Time: 1:00pm – 2:00pm Eastern (USA) **Note special start time of 1:00pm! **

Topic: Towards Modeling Infectious Behaviors 

Speakers: Raffaele Vardavas and Pedro Nascimento de Lima, RAND Corporation 

Abstract: Changes in behaviors are often acknowledged as a major source of uncertainty in infectious disease modeling work, and there is an increasing interest and need for simulation models of infectious diseases that couple transmission dynamics with adaptive behaviors. Such models could reflect how policies affect behaviors (in both desirable and undesirable ways) concerning subsequent disease epidemiology. This talk presents our path towards tackling behavior (and its inherent uncertainty) in our modeling work. We begin by introducing our COVID-19 model and analysis and explain how we accounted for some behavioral mechanisms in our stress-test of COVID-19 reopening policies. We then describe our prior efforts in developing and informing an ABM to model seasonal influenza vaccination dynamics whereby individuals are influenced by alters in their social network and use inductive reasoning to update their propensity to vaccinate. We will also present publicly available longitudinal surveys that we have designed and fielded through RAND’s American Life Panel for the purpose of informing our models. Finally, we discuss our project that will extend this framework for COVID-19 and collect behavioral data on a representative US sample for the next four years. If you are an infectious disease modeler and are interested in the idea of modeling infectious behaviors, please join this talk, and give us feedback! Your feedback may inform how we design future surveys and could result in exciting collaborations. 

Bios

Raffaele Vardavas is a mathematician at the RAND Corporation. His interest lies in applying methods from statistical physics to models of complex social systems and policy.  His main area of research focuses on modeling the spread of infectious disease and behavioral contagion in social systems using both population-based and agent-based models. He holds a Ph.D. in Physics from Imperial College London obtained in 2002. Before joining RAND in 2008, he was a post-doctoral researcher at UCLA in applied mathematics and later in the biomathematics departments. 

Pedro Nascimento de Lima is an assistant policy researcher at RAND, a Ph.D. candidate at the Pardee RAND Graduate School, and a Visiting Student at Argonne National Laboratory. He is also a member of the MIDAS Student group. His primary research interests are the health policy applications of Decision Making Under Deep Uncertainty (DMDU) methods. He is passionate about tackling complex and deeply uncertain policy questions with modeling and high-performance computing. 

 

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