
A network-based explanation of why most Covid-19 infection curves are linear - viburnum
https://www.pnas.org/content/early/2020/08/21/2010398117
======
tduberne
Really interesting paper. Layman summary for those not used to network
science: infection rate are lower than expected from standard models, because
they consider each infected person meets new completely random strangers,
while in reality, they are more likely to meet those who infected them, or
those who infected those who infected them.

In particular, the paper shows that a "phase transition" happens when the
degree (average number of person met per person) exceeds a threshold, which
the paper estimates to be around 7. Above that threshold, growth becomes
exponential rather than linear.

------
lmilcin
I don't think it's true.

I believe the reason we have linear or stable infection rates is that the
determination to stop the virus grows as the number grow and then wanes as
infection rates fall.

This basically works like a thermostat (viro-stat?) and is due to governments
or large parts of population not willing to put up with restriction when rates
are falling ("why do I need to comply, this is no longer a real problem?")

This is what I see here in Poland. The government supposedly "closely" watches
the situation and puts new restriction wherever the infection rates spike but
then promptly removes them when they start falling to what is described as
"acceptable level".

This is no way to combat the virus, this is the recipe to keep it around
indefinitely.

------
derbOac
This is a really great paper but I thought there was a general move to
network-based models anyway? I recall it coming up during quarantine policy
discussions.

Nice work though.

It seems more practically evident to me now than early on. In my area, the
returning growth seems driven by medium sized clusters that are scattered
geographically and aren't the same as what you might have thought early on, in
that it's not spreading evenly in densely populated areas.

------
scoot_718
Most of the Covid infection curves I've seen are linear because the scales are
logarithmic.

------
gnusty_gnurc
Michael Levitt has been arguing covid-19 never experiences the apocalyptic
exponential growth that most people/media seem to think is going on.

With a Gompertz curve, we start out with very high growth in the beginning but
the growth constantly declines from the outset.

Even the idea of network-based analysis and heterogeneity was something he
talked about back in March or thereabouts IIRC.

[https://www.medrxiv.org/content/10.1101/2020.06.26.20140814v...](https://www.medrxiv.org/content/10.1101/2020.06.26.20140814v2)

------
rrobukef
Wonderful paper! I wonder if amateur real-world simulations are possible based
on public data (streets, population density, daily movement).

The computing power needed for 100,000 nodes, ~8 edges, ~30-60 sim. days,
>>100 experiments seems fairly limited. An estimated 5 GFLOP should be
possible for a desktop in reasonable time.

------
fargle
Mar's law.

Rule 6 of Akin's Laws of Spacecraft Design:

6\. Everything is linear if plotted log-log with a fat magic marker.

[http://spacecraft.ssl.umd.edu/akins_laws.html](http://spacecraft.ssl.umd.edu/akins_laws.html)

~~~
rrobukef
No log-log plots nor fat magic markers in this paper though.

