StatUa day 2021: Covid-19 (what else...?)

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. 

Click here for the full program


13:30 Introduction

13:45 "A refreshing view how we could unravel disease transmission" by Lander Willem

14:45 Break

15:00 "Using individual-based models to understand superspreading: a COVID-19 case-study" by Elise Kuylen

16:00 Closing

Summary of the talks

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 case-study 

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.