As the covid-19 pandemic has been dominating our lives for the past months and numbers and models are now mentioned daily in the news, we feel that there was only one possible theme for this StatUA afternoon! We are also in luck that the UA has many experts involved in this field so we are very happy that they will enlighten us during this afternoon on infection disease modelling, how this can help us in modelling what-if scenario’s if certains restrictions are lifted.
The final progam includes two talks:
- Lander Willem : A refreshing view how we could unravel disease transmission
- Elise Kuylen : Using individual-based models to understand superspreading: a COVID-19 case-study
This edition of the StatUa day takes place online on February 5, from 1.30pm till 4.00pm. The event is free, but we kindly ask you to register through the link below. Participants will receive a BB-collaborate link a few days before the event.
13:45 "A refreshing view how we could unravel disease transmission" by Lander Willem
15:00 "Using individual-based models to understand superspreading: a COVID-19 case-study" by Elise Kuylen
A refreshing view how we could unravel disease transmission
by Lander Willem, expert in individual-based simulations, UAntwerp
How infection disease modelling and data can help us building what-if scenarios to tackle the ongoing crisis. Lander Willem is not only researcher in infection modelling but is also a firm believer in technology and data. He addresses current public health challenges based on an engineering and software approach.
Using individual-based models to understand superspreading: a COVID-19
by Elise Kuylen, PhD student at the Centre for Health Economics Research and Modelling Infectious Diseases
Superspreading has often been identified as an important factor to
understand the spread of COVID-19 in the current pandemic. However, many
modelling approaches do not take this factor into account. Furthermore, these
approaches often rely on compartmental models, which are unable to adequately
represent heterogeneity in a population. Thus, to be able to understand the
potential effect of superspreading on the spread of COVID-19, we represented
superspreading in an individual-based model by including heterogeneity in the
infectivity of individuals. Combined with realistic contact patterns
implemented in the model, this allowed us to gauge the impact of different
levels of superspreading on the spread of the disease in a population.