Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic

Robert, Alexis; Edmunds, W. John; Watson, Conall; Henao-Restrepo, Ana María; Gsell, Pierre-Stéphane; Williamson, Elizabeth; Longini, Ira M.; Кейта, Сакоба; Kucharski, Adam J.; Touré, Alhassane; Nadlaou, Sévérine Danmadji; Diallo, Boubacar; Barry, M.; Fofana, Thierno Oumar; Camara, Louceny; Kaba, Ibrahima; Sylla, Lansana; Diaby, Mohamed; Soumah, Ousmane; Diallo, Abdourahime; Niare, Amadou; Diallo, Abdourahmane; Eggo, Rosalind M

doi:10.1093/aje/kwz090

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…It provides an opportunity to test potential cases, treat infections early, and break ongoing transmission. In addition, identifying the transmission direction provides essential knowledge for understanding risk factors of transmission and susceptibility ( 1 – 3 ), household transmission ( 4 ), geographical spread ( 5 – 7 ), and early pathogenesis events ( 8 ). This knowledge, in turn, informs the design and implementation of public health interventions ( 9 , 10 ).…”

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confidence: 99%

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Villabona-Arenas

Hué

Baxter

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.

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confidence: 99%

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Villabona-Arenas

Hué

Baxter

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…It provides an opportunity to test potential cases, treat infections early and break ongoing transmission. Moreover, identifying the direction of transmission (DoT), provides paramount knowledge for understanding risk factors of transmission and susceptibility [1][2][3] , household transmission 4 , spread [5][6][7] and early pathogenesis events 8 . Yet inferring directionality is challenging.…”

Section: Introductionmentioning

confidence: 99%

Using Phylogenetics to Infer Hiv-1 Transmission Direction Between Known Transmission Pairs

Villabona-Arenas

Hué

Baxter

et al. 2021

Preprint

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Inferring the direction of transmission between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. State-of-the-art approaches to infer directionality use phylogenetic ancestral state reconstruction to identify the individual in whom the most recent common ancestor of the virus populations originated. However, these methods vary in their accuracy when applied to different datasets and it is currently unclear under what circumstances inferring directionality is inaccurate and when bias is more likely. To evaluate the performance of phylogenetic ancestral state reconstruction, we inferred directionality for 112 HIV transmission pairs where the direction of transmission was known, and detailed additional information was available. Next, we fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic and phylogenetic factors influenced the outcome of the inference. Third, we repeated the analysis under real-life conditions when only routinely collected data are available. We found that the inference of directionality depends principally on the topology class and branch length characteristics of the phylogeny. Specifically, directionality is most correctly inferred when the phylogenetic diversity and the minimum root-to-tip distance in the transmitter is greater than that of the recipient partner and when the minimum inter-host patristic distance is large. Similarly, under real-life conditions, the probability of identifying the correct transmitter increases from 52%--when a monophyletic-monophyletic or paraphyletic-polyphyletic tree topology is observed, when the sample size in both partners is small and when the tip closest to the root does not agree with the state at the root--to 93% when a paraphyletic-monophyletic topology is observed, when the sample size is large and when the tip closest to the root agrees with the state at the root. Our results support two conclusions. First, that discordance between previous studies in inferring transmission direction can be explained by differences in key phylogenetic properties that arise due to different evolutionary, epidemiological and sampling processes; and second that easily calculated metrics from the phylogenetic tree of the transmission pair can be used to evaluate the accuracy of inferring directionality under real-life conditions for use in population-wide studies. However, given that these methods entail considerable uncertainty, we strongly advise against using these methods for individual pair-level analysis.

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“…The identification of transmission trees and transmission events during infectious disease outbreaks can lead to identifying factors associated with subsequent transmissions [1][2][3] , describing the populations or the areas more vulnerable to importations and transmission [4][5][6][7] , and quantifying the impact of control measures 8,9 . The most straightforward approach to reconstruct who-infected-whom is to carry out patient interviews and establish the previous contacts to connect the reported cases.…”

Section: Introductionmentioning

confidence: 99%

o2geosocial: Reconstructing who-infected-whom from routinely collected surveillance data

2021

Self Cite

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Reconstructing the history of individual transmission events between cases is key to understanding what factors facilitate the spread of an infectious disease. Since conducting extended contact-tracing investigations can be logistically challenging and costly, statistical inference methods have been developed to reconstruct transmission trees from onset dates and genetic sequences. However, these methods are not as effective if the mutation rate of the virus is very slow, or if sequencing data is sparse. We developed the package o2geosocial to combine variables from routinely collected surveillance data with a simple transmission process model. The model reconstructs transmission trees when full genetic sequences are not available, or uninformative. Our model incorporates the reported age-group, onset date, location and genotype of infected cases to infer probabilistic transmission trees. The package also includes functions to summarise and visualise the inferred cluster size distribution. The results generated by o2geosocial can highlight regions where importations repeatedly caused large outbreaks, which may indicate a higher regional susceptibility to infections. It can also be used to generate the individual number of secondary transmissions, and show the features associated with individuals involved in high transmission events. The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

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Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic

Cited by 15 publications

References 36 publications

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Using Phylogenetics to Infer Hiv-1 Transmission Direction Between Known Transmission Pairs

o2geosocial: Reconstructing who-infected-whom from routinely collected surveillance data

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