Multiscale approach to COVID-19

coronadata coronamath
#1

Super interesting work on multi-scale approaches to contact tracing. This points to the importance of understanding what communities exist is in each cultural context.

“We analyze the spread of COVID-19 by considering the transmission of the disease among individuals both within and between communities. COVID-19 can be eliminated if the community-to-community reproductive number—i.e. the expected/average number of other communities to which a single infected community will transmit the virus—is reduced to less than one. We find that this community-to-community reproductive number is proportional to the probability that an individual in an infected community will travel to an uninfected community and exponential in the length of the time-delay before community-level action is taken.”

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Mathematical modeling of COVID-19
Epidemics and cumulants
#2

Combined with deep understanding of the epidemiological parameters of the virus, this offers a viable strategy for Switzerland.

Some sort of Swiss National Redoubt, with different strategies deployed at the end of valleys and in the cities. In the mountains and progressively from the end of the valleys, one could implement digital contact tracing based on consent. Progressively we would reach the cities where it would be particularly hard, as we will have to understand how different communities intermingle.

I propose we work on this in the EPFL hackathon on personal data, March 28th, where we might discuss among other things technical standards in preparation.

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Mathematical modeling of COVID-19
#3

Example of communities self-organising in UK under one banner. Basic, so just illustrative of the concept https://covidmutualaid.org

#4

This is potentially scary in how much it could contribute to spreading the disease, of course.

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#5

People are slowly rallying to Yaneer Bar-Yam’s ideas