text: 415,62,1230127951548690434,3325105445,Ben F. Maier,"In our new paper we show why lab-confirmed case numbers of the #Coronavirus #COVID19 do not grow exponentially: <LINK> Answer: Effective containment policies isolate the healthy population well enough for case numbers to follow an algebraic scaling law. 1/9 <LINK> 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 2/9 <LINK> In this basic system, the number of infecteds grows exponentially, initially. So why don't we see that in the current outbreak? 3/9 <LINK> 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 4/9 <LINK> However, this would still yield an exponential growth behavior, albeit with a slower velocity 5/9 <LINK> 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 6/9 <LINK> Doing this, we see that the exponential growth is suppressed by the depletion of susceptible individuals from the transmission process 7/9 <LINK> 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. 8/9 <LINK> our analysis material is available online:  <LINK> Thank you, @DirkBrockmann, for this fruitful and fast collaboration! 9/9 @balzacdiet @DirkBrockmann yes. this would be reflected in the infecteds' quarantine rate. we also tested a variation of that model where confirmation is decoupled from quarantine and find similar growth patterns. The growth behavior is dominated by the removal of healthy individuals from the process. @bene_beck @DirkBrockmann @ewyler so far, this seems to be the case. However, the model assumes that eventually all susceptibles become and remain isolated. This is impossible in reality, so we'll see what happens in the following few weeks. I suspect the model underestimates total number of cases (see Discuss.)",http://arxiv.org/abs/2002.07572,"The recent outbreak of COVID-19 in Mainland China is characterized by a distinctive algebraic, sub-exponential increase of confirmed cases during the early phase of the epidemic, contrasting an initial exponential growth expected for an unconstrained outbreak with sufficiently large reproduction rate. Although case counts vary significantly between affected provinces in Mainland China, the scaling law $t^{\mu}$ is surprisingly universal, with a range of exponents $\mu=2.1\pm0.3$. The universality of this behavior indicates that despite social, regional, demographical, geographical, and socio-economical heterogeneities of affected Chinese provinces, this outbreak is dominated by fundamental mechanisms that are not captured by standard epidemiological models. We show that the observed scaling law is a direct consequence of containment policies that effectively deplete the susceptible population. To this end we introduce a parsimonious model that captures both, quarantine of symptomatic infected individuals as well as population wide isolation in response to mitigation policies or behavioral changes. For a wide range of parameters, the model reproduces the observed scaling law in confirmed cases and explains the observed exponents. Quantitative fits to empirical data permit the identification of peak times in the number of asymptomatic or oligo-symptomatic, unidentified infected individuals, as well as estimates of local variations in the basic reproduction number. The model implies that the observed scaling law in confirmed cases is a direct signature of effective contaiment strategies and/or systematic behavioral changes that affect a substantial fraction of the susceptible population. These insights may aid the implementation of containment strategies in potential export induced COVID-19 secondary outbreaks elsewhere or similar future outbreaks of other emergent infectious diseases. ","Effective containment explains sub-exponential growth in confirmed cases