Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention

Zhang, Yunjun; Leitner, Thomas; Albert, Jan; Britton, Tom

doi:10.1371/journal.pcbi.1008122

Cited by 7 publications

(5 citation statements)

References 42 publications

(67 reference statements)

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“…However, RNA viruses generally exhibit high mutation rates due to error-prone viral RNA polymerases, which typically leads to the presence of various viral variants within a single host [25]. The resulting intra-host diversity has been shown to affect disease progression [26], tissue tropism [27], transmission risk [28], transmission heterogeneity [29], and treatment outcome [30, 31] in various RNA viruses. Recent findings indicate that the mutation rate of SARS-CoV-2 is about as high as the one observed in the SARS-CoV genome (0.80–2.38 × 10 −3 nucleotide substitutions per site per year) [32, 33].…”

Section: Introductionmentioning

confidence: 99%

Within-patient genetic diversity of SARS-CoV-2

Kuipers

Batavia

Jablonski

et al. 2020

Preprint

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SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity.

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Section: Introductionmentioning

confidence: 99%

Within-patient genetic diversity of SARS-CoV-2

Kuipers

Batavia

Jablonski

et al. 2020

Preprint

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“…This complication is compounded when many transmissions happened over a short time. It is possible that prior identification of super-spreader activity can identify when and where in a phylogeny these problems exist [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

et al. 2022

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To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

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Section: Discussionmentioning

confidence: 99%

Combining biomarker and virus phylogenetic models improves epidemiological source identification

Lundgren

Romero-Severson

Albert

et al. 2021

Preprint

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To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling, and we evaluate real HIV-1 transmission clusters. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

show abstract

Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention

Cited by 7 publications

References 42 publications

Within-patient genetic diversity of SARS-CoV-2

Within-patient genetic diversity of SARS-CoV-2

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

Combining biomarker and virus phylogenetic models improves epidemiological source identification

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