Science Jam 41 (online): Co-evolution of social networks and infectious diseases (in the time of COVID-19)
We cordially invite you to join us at our virtual Centre for Complex Systems Studies on Microsoft Teams to meet other complexity researchers where you won’t miss out any online activities from us as well.
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Leading researcher: Hendrik Nunner (Social and Behavioural Sciences)
Webinar: link (the link will be provided soon)
The current developments of the COVID-19 pandemic show how vulnerable our globally connected network of societies is to the threat of infectious diseases. Especially the speed at which the virus has spread throughout the entire world is unparalleled. It is generally acknowledged that most infections arise through human-to-human transmission. Most countries have therefore implemented some form of social distancing to reduce the number of new infections. In other words, by changing the structure of our social networks we seek to influence the transmission dynamics of the virus. As a result, we can observe interdependent processes between two complex systems: dynamic social networks and infectious disease dynamics. A thorough understanding of how these systems co-evolve is essential for the selection and implementation of appropriate interventions. Common approaches for network-based models of infectious diseases, however, are unsuitable for this purpose, since network structures are regarded as either static or changing in a random fashion.
As part of a larger project to harness social networks for infectious disease control (start 2017), we addressed the problem of missing and not theoretically grounded social network dynamics in infectious disease models. We developed a model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that social networking in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations using a generic model implementation show that: (i) disregarding network dynamics can overestimate epidemic size (ii) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes, (iii) minor changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not, and (iv) networks with large numbers of ties, weak clustering, and short average path lengths create higher attack rates and shorter epidemics.
This upcoming science jam consists of two connected parts. First, I present our model for the co-evolution of dynamic social networks and infectious disease dynamics. Second, I will show how (static) social networks generated with our model provide realistic scenarios to assess the impact of network interventions for infectious disease control in times of COVID-19.
There will be 30-45 mins lecture from our speaker, followed by Question & Answer session.
To attend the lecture, please click this link at 15:00 on Tuesday 9th June 2020 (the link will be provided soon).
You are free to join the event without a Microsoft Teams account, the link above will direct you to open Teams on the web or download the program, and you can easily join the event as a guest in Teams.
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