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

Miller, Danielle; Martin, Michael A.; Harel, Noam; Kustin, Talia; Tirosh, Omer; Meir, Moran; Sorek, Nadav; Gefen-Halevi, Shiraz; Amit, Sharon; Vorontsov, Olesya; Wolf, Dana; Peretz, Avi; Shemer‐Avni, Yonat; Roif-Kaminsky, Diana; Kopelman, Naama M.; Huppert, Amit; Koelle, Katia; Stern, Adi

doi:10.1101/2020.05.21.20104521

Cited by 76 publications

(103 citation statements)

References 40 publications

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“…This is also consistent with some of the more sophisticated recent estimates growth rates such as that ofMiller et al (2020).…”

supporting

confidence: 89%

Implications of Heterogeneous SIR Models for Analyses of COVID-19

Ellison

2020

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This paper provides a quick survey of results on the classic SIR model and variants allowing for heterogeneity in contact rates. It notes that calibrating the classic model to data generated by a heterogeneous model can lead to forecasts that are biased in several ways and to understatement of the forecast uncertainty. Among the biases are that we may underestimate how quickly herd immunity might be reached, underestimate differences across regions, and have biased estimates of the impact of endogenous and policy-driven social distancing.

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“…This is also consistent with some of the more sophisticated recent estimates growth rates such as that ofMiller et al (2020).…”

supporting

confidence: 89%

Implications of Heterogeneous SIR Models for Analyses of COVID-19

Ellison

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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“…The SNV A23403G (resulting in D614G variant in spike protein) is a major viral mutation that has been observed in the major European countries of Italy, Spain, France, as well as Middle Eastern regions of Turkey and Israel [16,[64][65][66]. Some studies suggest that this D614G variant of the S spike protein provides greater survival and transmission ability to the virus, however there need to be additional studies conducted to confirm these claims [64].…”

Section: Subtypes Including the A23403g/d614g Spike Protein Variantmentioning

confidence: 99%

Genetic Grouping of SARS-CoV-2 Coronavirus Sequences using Informative Subtype Markers for Pandemic Spread Visualization

Zhao

Sokhansanj

Malhotra

et al. 2020

Preprint

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We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM and via an interactive website at https://covid19-ism.coe.drexel.edu/.

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

Cited by 76 publications

References 40 publications

Implications of Heterogeneous SIR Models for Analyses of COVID-19

Implications of Heterogeneous SIR Models for Analyses of COVID-19

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

Genetic Grouping of SARS-CoV-2 Coronavirus Sequences using Informative Subtype Markers for Pandemic Spread Visualization

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