A novel computational approach to reconstruct SARS-CoV-2 infection dynamics through the inference of unsampled sources of infection

Perera, Deshan; Perks, Ben; Potemkin, Michael; Gordon, Paul M.; Gill, John; Marle, Guido van; Long, Quan

doi:10.1101/2021.01.04.21249233

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…2017 ), using the dated phylogeny obtained with TreeTime. We ran the algorithm for 150,000 Markov Chain Monte Carlo iterations and assumed a Gamma distribution for the generation time with shape 1 and scale 0.01917 ( Perera et al. 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

et al. 2022

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A detailed understanding of how and when SARS-CoV-2 transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced 49 SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available, and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be 1-2 viral particles for sample pairs whose donor-recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

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Section: Methodsmentioning

confidence: 99%

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

et al. 2022

View full text Add to dashboard Cite

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“…Finally, we also explored TransPhylo (Didelot et al 2017), using the dated phylogeny obtained with TreeTime. We ran the algorithm for 150,000 MCMC iterations and assumed a Gamma distribution for the generation time with shape 1 and scale 0.01917 (Perera et al 2021).…”

Section: Inference Of Transmission Historymentioning

confidence: 99%

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

Gallego-García

Varela

Estévez-Gómez

et al. 2021

Preprint

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A detailed understanding of how and when SARS-CoV-2 transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced 49 SARS-CoV-2 patient samples from ten local clusters for which partial epidemiological information was available, and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be 1-2 viral particles for sample pairs whose donor-recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

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Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks

Liu

Cheng

et al. 2022

Viruses

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In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.

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A novel computational approach to reconstruct SARS-CoV-2 infection dynamics through the inference of unsampled sources of infection

Cited by 3 publications

References 40 publications

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks

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