Viral phylodynamics and the search for an ‘effective number of infections’

Abstract

Information on the dynamics of the effective population size over time can be obtained from the analysis of phylogenies, through the application of time-varying coalescent models. This approach has been used to study the dynamics of many different viruses, and has demonstrated a wide variety of patterns, which have been interpreted in the context of changes over time in the ‘effective number of infections’, a quantity proportional to the number of infected individuals. However, for infectious diseases, the rate of coalescence is driven primarily by new transmissions i.e. the incidence, and only indirectly by the number of infected individuals through sampling effects. Using commonly used epidemiological models, we show that the coalescence rate may indeed reflect the number of infected individuals during the initial phase of exponential growth when time is scaled by infectivity, but in general, a single change in time scale cannot be used to estimate the number of infected individuals. This has important implications when integrating phylogenetic data in the context of other epidemiological data.

Rates of coalescence for common epidemiological models at equilibrium

“…The first approach, developed by Volz et al [12], uses an equation for the rate of lineage coalescence in an SIR model to reconstruct how the number of infected individuals changes deterministically over time. This work also elegantly pointed out that the rate of lineage coalescence should depend on both disease incidence and disease prevalence, rather than on prevalence levels alone [12,14]. The second approach extends a Bayesian inference method based on particle filtering to accommodate gene genealogies as observed data [13].…”

“…We can compare this expression to the expression derived in Volz et al [12] and Frost et al [14] that governs the dynamics of the number of ancestral lineages over time…”

Targeting HIV Prevention Based on Molecular Epidemiology Among Deeply Sampled Subnetworks of Men Who Have Sex With Men

“…This threshold is an accepted standard for linkage based on work demonstrating an evolutionary rate of 0.7%/year for HIV-1 pol within individuals and the knowledge that the expected distance for genetically unrelated pol sequences is >5% [4,6,14]. A cluster was then defined as all sequences within 1.5% genetic distance from at least 1 other sequence, but not necessarily within 1.5% genetic distance of all sequences within the cluster [6,15,16]. Phylogenetic analysis was performed for the circulating recombinant forms (CRFs) and subtype predominating in China: CRF02_AE, CRF07_BC, and B.…”

Host–Symbiont–Pathogen–Host Interactions: Wolbachia, Vector-Transmitted Human Pathogens, and the Importance of Quantitative Models of Multipartite Coevolution

“…Third, and putting the second and first together, disease ecology and epidemiology aim to answer specific questions concerning the distribution and risk of disease, which are not necessarily the same questions of interest from animals or plants, e.g., for conservation purposes, it is of interest to know whether an animal population had its diversity affected by a bottleneck or fragmentation of its habitat (Heller et al 2013), while for disease bottlenecks are part of everyday life, and their high mutation rates help them make up for the diversity lost after a population crash (also known as elimination or disease control). Additionally, important classic metrics of population genetics often do not have an obvious meaning for epidemiologists; effective number of infections (Frost and Volz 2010), the epidemiological equivalent to the effective population size, is not immediately interpretable or useful for medical purposes.…”