Phylodynamics of Infectious Disease Epidemics

Abstract

We present a formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations. Virus phylogenies have been used in many recent studies to infer properties of epidemics. These approaches rely on coalescent models that may not be appropriate for infectious diseases. We account for phylogenetic patterns of viruses in susceptibleinfected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) models of infectious disease, and our approach may be a viable alternative to demographic models used to reconstruct epidemic dynamics. The method allows epidemiological parameters, such as the reproductive number, to be estimated directly from viral sequence data. We also describe patterns of phylogenetic clustering that are often construed as arising from a short chain of transmissions. Our model reproduces the moments of the distribution of phylogenetic cluster sizes and may therefore serve as a null hypothesis for cluster sizes under simple epidemiological models. We examine a small cross-sectional sample of human immunodeficiency (HIV)-1 sequences collected in the United States and compare our results to standard estimates of effective population size. Estimated prevalence is consistent with estimates of effective population size and the known history of the HIV epidemic. While our model accurately estimates prevalence during exponential growth, we find that periods of decline are harder to identify.

Dynamics of Two Separate but Linked HIV-1 CRF01_AE Outbreaks among Injection Drug Users in Stockholm, Sweden, and Helsinki, Finland

“…However, for both the Swedish and Finnish data sets, the epidemic dynamics shown by the above ML analyses were also captured by Bayesian analyses, using a relaxed clock and two different growth models (the nonparametric skyline growth model and the logistic growth model). This allowed us to infer when epidemic events occurred ( This coincides with the increase in newly diagnosed HIV-1 cases among IDUs, but it is important that the RGD does not directly correspond to the number of infected individuals (2,38). According to our analyses using the skyline model, the local Finnish epidemic started with a sharp increase of RGD in 1998, followed by a short plateau and another increase in 2001 (Fig.…”

The HIV Epidemic: High-Income Countries

“…More recently they have allowed estimates to be made of the origins of diverse epidemics in different countries and risk groups Salemi et al 2008). Finally, in combination with very large data sets, these approaches have allowed a bridge to be made across to infectious disease epidemiology, as under certain assumptions, the viral evolutionary history can be used to estimate critical epidemiological parameters that are not accessible from other routes (Volz et al 2009). …”

Impact of Sampling Density on the Extent of HIV Clustering

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] However, there is confusion surrounding HIV clustering due to differences in sampling, methodological approaches, and interpretation of HIV clustering results across studies. Issues that still need to be resolved through dedicated studies, meta-analyses, and comprehensive reviews include the following: What is the definition of an HIV cluster?…”