Getting Our Models Prepared for the Next Pandemic

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The global landscape has been reshaped by the recent COVID-19 pandemic, underscoring the critical importance of pandemic preparedness and effective modeling. This talk will delve into the essential steps required to enhance our predictive models and overall readiness for future pandemics. We will explore the lessons learned from the COVID-19 crisis, emphasizing the need to act now in order to mitigate future threats. Topics covered include data-driven modeling approaches, real-time surveillance, and collaborative efforts in research and response.

Join us in this discussion to discover how we can better equip ourselves to face the challenges of the next pandemic!



  Date and Time

  Location

  Hosts

  Registration



  • Date: 05 Dec 2023
  • Time: 07:00 PM to 08:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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Virtual Session Zoom Link:

https://lehigh.zoom.us/j/91269215296

All are welcome. You must register, but you do not have to be an IEEE member to attend.

  • Contact Event Hosts
  • Starts 07 November 2023 12:53 AM
  • Ends 05 December 2023 07:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge


  Speakers

Jing Huang Jing Huang of University of Pennsylvania

Topic:

Getting Our Models Prepared for the Next Pandemic

The global landscape has been reshaped by the recent COVID-19 pandemic, underscoring the critical importance of pandemic preparedness and effective modeling. This talk will delve into the essential steps required to enhance our predictive models and overall readiness for future pandemics. We will explore the lessons learned from the COVID-19 crisis, emphasizing the need to act now in order to mitigate future threats. Topics covered include data-driven modeling approaches, real-time surveillance, and collaborative efforts in research and response. Join us in this discussion to discover how we can better equip ourselves to face the challenges of the next pandemic!

Biography:

Dr. Jing Huang is an Assistant Professor of Biostatistics at the University of Pennsylvania. Her research is focused on creating methodologies to understand disease dynamics and manage health using multivariate longitudinal health data. Dr. Huang's work spans several research areas, including risk prediction for congenital heart disease patients, dynamic interventions based on mobile health data, and pharmacovigilance using FDA and CDC safety reports. During the COVID-19 pandemic, Dr. Huang led the statistical modeling of infectious disease transmission at PolicyLab in the Children’s Hospital of Philadelphia. Her team was the first in the U.S. to model county-level COVID-19 transmission rates nationwide. This research has been nationally recognized and has informed the White House Coronavirus Task Force's state-level guidance. The models have also assisted the Pennsylvania Governor's crisis team and local officials across the Commonwealth in their pandemic response.

Address:United States