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The recent outbreak of COVID-19 in Mainland China is characterized by adistinctive algebraic, sub-exponential increase of confirmed cases during theearly phase of the epidemic, contrasting an initial exponential growth expectedfor an unconstrained outbreak with sufficiently large reproduction rate.Although case counts vary significantly between affected provinces in MainlandChina, the scaling law $t^{\mu}$ is surprisingly universal, with a range ofexponents $\mu=2.1\pm0.3$. The universality of this behavior indicates thatdespite social, regional, demographical, geographical, and socio-economicalheterogeneities of affected Chinese provinces, this outbreak is dominated byfundamental mechanisms that are not captured by standard epidemiologicalmodels. We show that the observed scaling law is a direct consequence ofcontainment policies that effectively deplete the susceptible population. Tothis end we introduce a parsimonious model that captures both, quarantine ofsymptomatic infected individuals as well as population wide isolation inresponse to mitigation policies or behavioral changes. For a wide range ofparameters, the model reproduces the observed scaling law in confirmed casesand explains the observed exponents. Quantitative fits to empirical data permitthe identification of peak times in the number of asymptomatic oroligo-symptomatic, unidentified infected individuals, as well as estimates oflocal variations in the basic reproduction number. The model implies that theobserved scaling law in confirmed cases is a direct signature of effectivecontaiment strategies and/or systematic behavioral changes that affect asubstantial fraction of the susceptible population. These insights may aid theimplementation of containment strategies in potential export induced COVID-19secondary outbreaks elsewhere or similar future outbreaks of other emergentinfectious diseases.

Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China