Characteristics of SARS-CoV-2 super-spreaders in Japan.
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ABSTRACT:
SUBMITTER: Kawahara T
PROVIDER: S-EPMC8717706 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spain has been one of the main global pandemic epicenters for coronavirus disease 2019 (COVID-19). Here, we analyzed >41 000 genomes (including >26 000 high-quality (HQ) genomes) downloaded from the GISAID repository, including 1 245 (922 HQ) sampled in Spain. The aim of this study was to investigate genome variation of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and reconstruct phylogeographic and transmission patterns in Spain. Phylogeographic analysis suggested at least 34 independent introductions of SARS-CoV-2 to Spain at the beginning of the outbreak. Six lineages spread very successfully in the country, probably favored by super-spreaders, namely, A2a4 (7.8%), A2a5 (38.4%), A2a10 (2.8%), B3a (30.1%), and B9 (8.7%), which accounted for 87.9% of all genomes in the Spanish database. One distinct feature of the Spanish SARS-CoV-2 genomes was the higher frequency of B lineages (39.3%, mainly B3a+B9) than found in any other European country. While B3a, B9, (and an important sub-lineage of A2a5, namely, A2a5c) most likely originated in Spain, the other three haplogroups were imported from other European locations. The B3a strain may have originated in the Basque Country from a B3 ancestor of uncertain geographic origin, whereas B9 likely emerged in Madrid. The time of the most recent common ancestor (TMRCA) of SARS-CoV-2 suggested that the first coronavirus entered the country around 11 February 2020, as estimated from the TMRCA of B3a, the first lineage detected in the country. Moreover, earlier claims that the D614G mutation is associated to higher transmissibility is not consistent with the very high prevalence of COVID-19 in Spain when compared to other countries with lower disease incidence but much higher frequency of this mutation (56.4% in Spain vs. 82.4% in rest of Europe). Instead, the data support a major role of genetic drift in modeling the micro-geographic stratification of virus strains across the country as well as the role of SARS-CoV-2 super-spreaders.
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Project description: Not available
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Project description:In individual SARS-CoV-2 outbreaks, the count of confirmed cases and deaths follow a Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies. Our main finding is that Gompertz growth observed for early outbreaks occurs only for a scale-free network, in which nodes with many more neighbors than average are common. These nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically it is seen that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters: the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.
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