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Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo

R. A. Smith, E. L. Ionides, A. A. King
doi: https://doi.org/10.1101/096396
R. A. Smith
1Department of Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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E. L. Ionides
2Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA
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A. A. King
3Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA
4Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA
5Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
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Abstract

Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.

Footnotes

  • ↵* E-mail: alxsmth{at}umich.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 23, 2016.
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Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo
R. A. Smith, E. L. Ionides, A. A. King
bioRxiv 096396; doi: https://doi.org/10.1101/096396
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Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo
R. A. Smith, E. L. Ionides, A. A. King
bioRxiv 096396; doi: https://doi.org/10.1101/096396

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