Thread by @BenFMaier

In our new paper we show why lab-confirmed case numbers of the #Coronavirus #COVID19 do not grow exponentially: arxiv.org/abs/2002.07572

Answer: Effective containment policies isolate the healthy population well enough for case numbers to follow an algebraic scaling law. 1/9

classically, uncontained epidemic outbreaks can be modeled by means of the SIR model: individuals are either (S)usceptible, (I)nfectious, or (R)emoved from the process.

"S" enter the "I" compartment with rate alpha, and "I" are transferred to "R" with rate beta

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In this basic system, the number of infecteds grows exponentially, initially.

So why don't we see that in the current outbreak?

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The first thing one may think of is that symptomatic infecteds are usually quarantined. One can model that by including a quarantine rate kappa with which infecteds are isolated from the transmission process

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However, this would still yield an exponential growth behavior, albeit with a slower velocity

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Now, the implemented containment policies in several provinces in China did not only affect infecteds but also the general public. We can model that by including a public isolation rate kappa0

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Doing this, we see that the exponential growth is suppressed by the depletion of susceptible individuals from the transmission process

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additionally, the lab-confirmed cases are not reflected by the (I) compartment -- rather, we have to assume that infecteds can only be counted after a time lag, approximately when they are quarantined in a new compartment (X) which follows the empirically observed behavior.

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our analysis material is available online:

github.com/benmaier/COVID…

Thank you, @DirkBrockmann, for this fruitful and fast collaboration!

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