KNAW corona webinar

Mathematics and COVID-19: social network modelling and the spread of infectious diseases

19 April 2021 from 19:00 to 20:30 hrs
Online, via Zoom
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Social networks can lead to the rapid spread of infectious diseases. Epidemiological modelling can be used to chart not only the spread of disease within these networks but also the impact of measures introduced to control that spread.

That makes it possible to study the effect of testing and contact tracing and how a disease can spread within a social network if no action is taken. During this webinar, three experts discuss the mathematical methods that underpin this research and can also be applied beyond the current pandemic.

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View this webinar via YouTube.



Opening with Michel Mandjes, professor of applied probability theory, University of Amsterdam.


Pinar Keskinocak, William W. George Chair and professor in the School of Industrial and Systems Engineering and the co-founder and director of the Center for Health and Humanitarian Systems at Georgia Institute of Technology, US - Infectious Disease Modeling.

There has been signifcant progress in science and medicine to increase our understanding of infectious diseases, preventive measures, and treatments. However, there are still numerous infectious diseases that continue to threaten human health and societies, at times leading to outbreaks or epidemics. The decisions about how to allocate a limited set of resources across a variety of public health interventions for prevention or response are complex, requiring the consideration of many factors such as geographic or demographic characteristics, infrastructure, population dynamics, compliance/adherence with recommendations, etc. In this presentation we will provide examples on how quantitative methods can be used in projecting the spread of infectious diseases and supporting decisions on public health interventions.


Shane Henderson, Charles W. Lake, Jr. Chair in Productivity in the School of Operations Research and Information Engineering (ORIE), Cornell University, US Modeling enabled Cornell University to reopen for in-person instruction in fall 2020.

Like all universities, in the Summer of 2020 Cornell faced the question of whether to go online-only in the Fall of 2020 or to bring students back for a residential semester. Like many universities, Cornell decided on a residential semester. Like few universities, Cornell managed to keep COVID-19 at bay for the entire fall semester, and the success continues to date. Our modeling team played a major role in the decision to reopen for residential instruction, perhaps paradoxically showing that with appropriate interventions a residential semester was safer than an online-only semester. Our team also helped design many of the interventions, including asymptomatic surveillance through pooled testing, adaptive testing and class scheduling. Stochastic simulation played a central role. I will share a perspective on this wild ride.


Clara Stegehuis, assistant professor at the department of Electrical Engineering, Mathematics and Computer Science, University of Twente, NL - Networks and contact tracing: why intuition may fail.

Epidemics spread on the networks that describe our social connections. But the structure of these connections also influence how effective measures to contain an epidemic are. We investigate the effectiveness of contact tracing with mathematical network models. Are there specific types of networks that benefit more from contact tracing than others? Why are 'superspreaders', who infect many others, likely to be found? And does this help us contain an epidemic?

20.05 Discussion and conclusion of the webinar, moderated by Michel Mandjes, professor of applied probability theory, University of Amsterdam, and Frank den Hollander, professor of mathematics, probability theory and statistics, Leiden University.

This webinar is an initiative of the Gravitation program NETWORKS.