Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China
By Benjamin F. Maier, Dirk Brockmann. Science, 2020
804 citations recorded by the source corpus.
Containment works National governments have taken different approaches in response to the coronavirus disease 2019 (COVID-19) pandemic, ranging from draconian quarantines to laissez-faire mitigation strategies. In data from China collected in February 2020, Maier and Brockmann noticed that, unexpectedly, the epidemic did not take off exponentially. Nonexponential spread occurs when the supply of susceptible individuals is depleted on a time scale comparable to the infectious period of the virus. The results of the authors' modeling approach indicate that the public response to the epidemic plus containment policies were becoming effective despite the initial increase in confirmed cases. Science, this issue p. 742 Feedback between the epidemic process, the behavioral response to it, and nationwide containment policies can restrain COVID-19. The recent outbreak of coronavirus disease 2019 (COVID-19) in mainland China was characterized by a distinctive subexponential increase of confirmed cases during the early phase of the epidemic, contrasting with an initial exponential growth expected for an unconstrained outbreak. We show that this effect can be explained as 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 practices in response to containment policies or behavioral changes, and show that the model captures the observed growth behavior accurately. The insights provided here may aid the careful implementation of containment strategies for ongoing secondary outbreaks of COVID-19 or similar future outbreaks of other emergent infectious diseases.
Subfields: Economics and Econometrics, Infectious Diseases, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/1a854bfceb615249fd9c75f3f4491b0554e59c16
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Citations in the corpus
- Artificial intelligence vs COVID-19: limitations, constraints and pitfalls (2020, methodology): “Its model has been applied to China to illustrate that containment can be successful in reducing the spread to slower than exponential rates-see Maier and Brockmann (2020) .”
- Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network. (2020, background): “Considerable number of analysis of the available data of the number of cases and deaths have been attempted recently, and a few data driven models have also been proposed [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] .”
- COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, neutral): “…For instance, it has been argued that mobility reduction is likely instrumental in reducing the effective reproduction number in many countries (13–17), in agreement with theoretical models and simulations, which have shown that containment can effectively slow down disease transmission (18–20).”
- Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, methodology): “This subset of papers studies the effects of isolation and quarantine policies [49], models NPIs as effective changes of the parameters [44,45,48,50,52,54– 57,59], investigates the impact of different policies [53,58], and defines the conditions for the optimal control of the spreading [51].”