The Intercollegiate Biomathematics Alliance and University of Wisconsin-Whitewater is jointly organizing a weekend Webinar series on the fundamental issues, complex challenges, and prediction limitations in using mathematical modelling approaches to understanding transmission dynamics of COVID-19 (SARS-CoV-2 infection).
- Olcay Akman – Illinois State University
- Aditi Ghosh – University of Wisconsin-Whitewater
- Sara Liesman – Illinois State University
- Anuj Mubayi – Illinois State University
- Rebecca Perlin – Arizona State University
- Padmanabhan Seshaiyer – George Mason University
In spite of vast literature on mathematical modeling of infectious diseases, the use of models in predicting COVID patterns have been limited. Existing models have been valuable, but they were not designed to support the types of critical decisions related to ever changing COVID patterns. For example, public health departments around the globe need to allocate COVID-19 tests amid limited supply, prioritize the deployment of healthcare staff, and direct health resources to those who need them most.
The overall aim of this webinar series is to bring together experts in academia as well as industry to disseminate scientific research in SARS-CoV-2 infection in a panel discussion format. We open this event to the entire scientific community interested in understanding the transition and transmission of this epidemic.
The focus of this webinar series will be on discussion related to why mathematical models have been able to capture COVID patterns in some cases and why they have been of limited scope in managing the spread of the disease through various non-pharmaceutical interventions (social distancing, contact tracing, testing, awareness, preparedness), as well as how models can adapt to battling the efficacy of interventions on both local and global scales. Researchers have been using different kinds of approaches (such as population level dynamical models, stochastic methods, statistical inferences and data analytical methods) in understanding underlying mechanisms and designing of interventions that contribute to the containment of this outbreak effectively in community. Since there has been continuously addition of volume, variety, and velocity of COVID-related data with much available in the public domain, modeler are facing a new challenges related to how these data can be incorporated/used in new sets of mathematical models to timely forecast disease outbreaks in populations, quantify uncertainty in trends and its effect in the near future in planning such as reopening of schools, universities and public places, which are a forefront of questions in everyone’s mind and matter of debate.