2020 # Outbreak dynamics of COVID-19 in China and the United States

**Abstract:** On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a loca…

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(106 citation statements)

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“…We model the spreading of COVID-19 through a mobility network of passenger air travel, which we represent as a weighted undirected graph G with N nodes and E edges (Peirlinck et al 2020). The N ¼ 27 nodes represent the countries of the European Union, the E ¼ 172 edges the most traveled connections between them.…”

confidence: 99%

“…We model the spreading of COVID-19 through a mobility network of passenger air travel, which we represent as a weighted undirected graph G with N nodes and E edges (Peirlinck et al 2020). The N ¼ 27 nodes represent the countries of the European Union, the E ¼ 172 edges the most traveled connections between them.…”

confidence: 99%

“…From these data, we map out the temporal evolution of the infectious group I as the difference between the confirmed cases minus the recovered cases and deaths. To simulate the country-specific epidemiology of COVID-19 with the SEIR model, we utilize these data to identify the basic reproduction number R 0 ¼ C=B using the latent and infectious periods A ¼ 1=a ¼ 2:56 days and C ¼ 1=c ¼ 17:82 days, which we had previously identified for the COVID-19 outbreak in N ¼ 30 Chinese provinces (Peirlinck et al 2020), since the current European data are not yet complete for the identification of all three parameters. In addition to the basic reproduction number R 0 ¼ C=B, which is a direct measure of the contact period B ¼ 1=b, we also identify the initial community spread q ¼ E 0 =I 0 that defines the initial exposed population E 0 (Maier and Brockmann 2020), and the affected population g ¼ N Ã =N that defines the fraction of the epidemic subpopulation N Ã relative to the total population N (Eurostat, 2020).…”

confidence: 99%

“…The constant reproduction number in Figure 1, left, nicely captures the exponential increase at the early stages of the outbreak, but fails to "bend the curve" before herd immunity occurs. Nonetheless, several recent studies have successfully used an SEIR model with a constant reproduction number to model the outbreak dynamics of COVID-19 in China [35] and in Europe [28] by explicitly reducing the total population N to an affected population N * = η N. The scaling coefficient η = N * /N is essentially a fitting parameter that indirectly quantifies the level of confinement [3]. For example, when averaged over 30 Chinese provinces, the mean affected population was η = 5.19 · 10 −5 ± 2.23 ± 10 −4 , suggesting that the effect of COVID-19 was confined to only a very small fraction of the total population [35].…”

confidence: 99%