Fundamental Identifiability Limits in Molecular Epidemiology

Abstract Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or...

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Bibliographic Details
Published in:Molecular biology and evolution 2021-09, Vol.38 (9), p.4010-4024
Main Authors: Louca, Stilianos, McLaughlin, Angela, MacPherson, Ailene, Joy, Jeffrey B, Pennell, Matthew W
Format: Article
Language:English
Subjects:
Analysis
Bayes Theorem
Communicable Diseases
Disease transmission
Epidemiology
Humans
Medical research
Medicine, Experimental
Methods
Models, Theoretical
Molecular Epidemiology - methods
Phylogeny
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Summary:Abstract Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible “congruent” scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the “congruence class” of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.
ISSN:1537-1719
0737-4038
1537-1719
DOI:10.1093/molbev/msab149