ELSC Seminar Series

How can we infer the spread of SARS-CoV-2 in social networks? How does this relate to spreading of activity in neural networks? And how is such inference done reliably, even if only a tiny fraction of the system can be observed experimentally? We recapitulate the basic principles of spreading dynamics, and investigate its role in shaping collective computation in neural networks. We then use this basis to investigate COVID-19 mitigation strategies. In particular, we demonstrate a tipping point in the test-trace-isolate strategies, which incurs (transient) supra-exponential growth. Avoiding that tipping-point can greatly facilitate the control of COVID-19.