title: Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo.
abstract: Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Most 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 human immunodeficiency virus 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.
authors.affiliation: Department of Bioinformatics, University of Michigan, Ann Arbor, MI.
authors.forename: R A
authors.initials: RA
authors.lastname: Smith
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, MI.
authors.forename: E L
authors.initials: EL
authors.lastname: Ionides
authors.affiliation: Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI.|Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI.|Department of Mathematics, University of Michigan, Ann Arbor, MI.
authors.forename: A A
authors.initials: AA
authors.lastname: King
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D018562:Disease Transmission, Infectious / Q000145:classification* / Q000706:statistics & numerical data; D019143:Evolution, Molecular; D006801:Humans; D009010:Monte Carlo Method; D010802:Phylogeny; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data
keywords: human immunodeficiency virus; iterated filtering; maximum likelihood; phylodynamics; sequential Monte Carlo; virus evolution