Contact tracing graph in Geneva. Source:

Zeynep Tufekci’s next piece

Paul-Olivier Dehaye
2 min readSep 30, 2020


Zeynep Tufekci just wrote a fantastic piece for The Atlantic on a statistical variable of high relevance to the COVID pandemic, and its implication for backward contact tracing.

Seriously, go read it.

I have been closely monitoring the literature on the topic since April, and curating it on Wikidata for a few weeks.

Ms Tufekci is well-known for her critique of social media, and its impact on various democratic processes, particularly disinformation dynamics.

It’s no surprise she would write this piece on COVID now. Both disinformation and COVID are about viral content, in the sense of accelerating propagation of information on networks (virus DNA, false information). Both have this high index of dispersion: a high variability in the number of secondary cases stemming from a primary case (formally: ratio of variance over mean). Mathematically, both behave in similar ways.

What’s very interesting to me is that this analogy should be extremely beneficial to public discourse. We have invented a series of words in each of the two contexts to communicate higher level patterns of propagation dynamics and our own responses to them, and slowly got accustomed to using these relatively complex concepts. The parallels carry over very well at the level of each individual concept: filter bubbles should correspond to clusters, for instance.

It would be very interesting to systematically explore the two glossaries, and see the nuances lost while exploiting the analogy.

I hope Ms Tufekci will consider it.



Paul-Olivier Dehaye

Mathematician. Co-founder of PersonalData.IO. Free society by bridging ideas. #bigdata and its #ethics, citizen science