Genomic epidemiology of a densely sampled COVID-19 outbreak in China

Volz, Erik; Fu, Han; Wang, Haowei; Xi, Xiauoyue; Chen, Wei; Liu, Dehui; Chen, Yingying; Tian, Mengmeng; Tan, Wenfeng; Zai, Junjie; Zan, Wanying; Li, Xinguang; Nie, Qing

doi:10.1101/2020.03.09.20033365

Cited by 12 publications

(11 citation statements)

References 23 publications

(25 reference statements)

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“…The program requires specification of an underlying epidemiological model, as well as any priors on parameters that will be estimated. In line with recent analyses 37 , we assumed that the epidemiological dynamics of SARS-CoV-2 were governed by SEIR dynamics. Transmission heterogeneity has previously been described for viral pathogens including SARS-CoV-1 (ref.…”

Section: Methodsmentioning

confidence: 91%

“…25,26 ). To account for the possibility of transmission heterogeneity, as in previous work 37 , we modeled two classes of infected individuals: one with low transmissibility I l and one with high transmissibility I h . Mathematically, the epidemiological model is given by Eqs.…”

Section: Methodsmentioning

confidence: 99%

“…In our model, we estimated a piecewise R 0 by estimating an initial R 0 that was in effect until March 19, 2020, when strong social distancing measures were implemented, along with a factor α by which R 0 changed on March 19. Instead of independently parameterizing β h and β l , we defined (as in previous work 37 ) the relative transmissibility of infected individuals in the I h and I l classes by the parameter τ ¼ β h =β l , and simplify notation by defining β β l . We further defined a parameter P as the fraction of secondary infections that were caused by a fraction p h of the most transmissible infected individuals and set P to 0.8.…”

Section: Methodsmentioning

confidence: 99%

“…Again, based on existing analyses (sarscov2phylodynamics.org, 37 ), we included an external reservoir in our analysis to allow for multiple introduced clades into Israel to be jointly considered. Instead of modeling both exposed (E) and infected (I) individuals in the external reservoir, we assume a single infected class Y undergoing exponential growth.…”

Section: Methodsmentioning

confidence: 99%

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Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

et al. 2020

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Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

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Section: Methodsmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

et al. 2020

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“…We utilized a compartmental structured coalescent model in the BEAST2 v6.1 PhyDyn package 14,15 to estimate the effective reproduction number and the number of infections through time from SARS-CoV-2 genetic sequences. The compartmental model has been previously described [16][17][18] and applied to SARS-CoV-2 sequence data. Importantly the model allows for bidirectional migration from and into an international reservoir, and splits the infected compartment into categories representing individuals with high or low rates of onward transmission.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 epidemic severity is associated with timing of non-pharmaceutical interventions

Ragonnet‐Cronin

Boyd

Geidelberg

et al. 2020

Preprint

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Background: Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. International comparisons are hampered by highly variable conditions under which epidemics spread and differences in the timing and scale of interventions. Cumulative COVID-19 morbidity and mortality are functions of both the rate of epidemic growth and the duration of uninhibited growth before interventions were implemented. Incomplete and sporadic testing during the early COVID-19 epidemic makes it difficult to identify how long SARS-CoV-2 was circulating in different places. SARS-CoV-2 genetic sequences can be analyzed to provide an estimate of both the time of epidemic origin and the rate of early epidemic growth in different settings. Methods: We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were cross-referenced with dates of the most stringent interventions in each location as well as the number of cumulative COVID-19 deaths following maximum intervention. Phylodynamic methods were used to estimate the rate of early epidemic growth and proxy estimates of epidemic size. Findings: The time elapsed between epidemic origin and maximum intervention is strongly associated with different measures of epidemic severity and explains 46% of variance in numbers infected at time of maximum intervention. The reproduction number is independently associated with epidemic severity. In multivariable regression models, epidemic severity was not associated with census population size. The time elapsed between detection of initial COVID-19 cases to interventions was not associated with epidemic severity, indicating that many locations experienced long periods of cryptic transmission. Interpretation: Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

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GMDH-Based Discovering Dynamic Regularities of the Ukraine Covid-19 Pandemic Process

Moroz¹,

Stepashko²

2020

Advances in Intelligent Systems and Computing

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This article presents the modeling results of the Ukraine Covid-19 pandemic process using official statistical data on the confirmed cases. Main goal is discovering dynamic regularities of the process given as daily data of the time series. That is why we use four different methods to build predictive difference models of the autoregression type: ordinary autoregression; autoregression of optimal structure obtained using the combinatorial-genetic GMDH algorithm COMBI-GA; another variant of the optimal structure built by the well-known method Lasso; and we compare prediction results of these methods with independent predictions published by the World Data Center. In our study, the baseline prediction is the one produced by the ordinary autoregression which includes all lags from 1 to a given their number. Unlike it, algorithms COMBI-GA as well as Lasso construct autoregressions with in some respect optimal compositions of lags. The WDC independent predictions are made using the Backpropagation ANN as nonlinear transformation of lag variables. This comparative study we carried out in two stages: first, for the period of strong quarantine in Ukraine and second, for the period of stepwise quarantine weakening. For both stages, the optimal models built by the COMBI-GA are most interpretable and demonstrate better predictive accuracy on validation datasets. This research is useful for defining tendency of coronavirus evolvement in time and predicting its future activity in order to take some protective measures.

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Genomic epidemiology of a densely sampled COVID-19 outbreak in China

Cited by 12 publications

References 23 publications

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

COVID-19 epidemic severity is associated with timing of non-pharmaceutical interventions

GMDH-Based Discovering Dynamic Regularities of the Ukraine Covid-19 Pandemic Process

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