Simultaneous reconstruction of evolutionary history and epidemiological dynamics from viral sequences with the birth–death SIR model

The evolution of RNA viruses, such as human immunodeficiency virus (HIV), hepatitis C virus and influenza virus, occurs so rapidly that the viruses' genomes contain information on past ecological dynamics. Hence, we develop a phylodynamic method that enables the joint estimation of epidemiological parameters and phylogenetic history. Based on a compartmental susceptible–infected–removed (SIR) model, this method provides separate information on incidence and prevalence of infections. Detailed information on the interaction of host population dynamics and evolutionary history can inform decisions on how to contain or entirely avoid disease outbreaks. We apply our birth–death SIR method to two viral datasets. First, five HIV type 1 clusters sampled in the UK between 1999 and 2003 are analysed. The estimated basic reproduction ratios range from 1.9 to 3.2 among the clusters. All clusters show a decline in the growth rate of the local epidemic in the middle or end of the 1990s. The analysis of a hepatitis C virus genotype 2c dataset shows that the local epidemic in the Córdoban city Cruz del Eje originated around 1906 (median), coinciding with an immigration wave from Europe to central Argentina that dates from 1880 to 1920. The estimated time of epidemic peak is around 1970.

[1]  Evan Wood,et al.  The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic , 2006, The Lancet.

[2]  S. Bonhoeffer,et al.  Birth–death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV) , 2012, Proceedings of the National Academy of Sciences.

[3]  C. Andrieu,et al.  The pseudo-marginal approach for efficient Monte Carlo computations , 2009, 0903.5480.

[4]  David Welch,et al.  Recursive algorithms for phylogenetic tree counting , 2013, Algorithms for Molecular Biology.

[5]  M. Beaumont Estimation of population growth or decline in genetically monitored populations. , 2003, Genetics.

[6]  Daniel J. Wilson,et al.  Coalescent inference for infectious disease: meta-analysis of hepatitis C , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[7]  Jeffrey W. Eaton,et al.  HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa , 2012, PLoS medicine.

[8]  Erik M. Volz,et al.  Viral phylodynamics and the search for an ‘effective number of infections’ , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  The Swiss,et al.  Cohort Profile: The Swiss HIV Cohort Study , 2010 .

[10]  Beda Joos,et al.  Estimating the basic reproductive number from viral sequence data. , 2012, Molecular biology and evolution.

[11]  Alexei J. Drummond,et al.  Phylogenetic and epidemic modeling of rapidly evolving infectious diseases , 2011, Infection, Genetics and Evolution.

[12]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[13]  David A. Rasmussen,et al.  Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series , 2011, PLoS Comput. Biol..

[14]  D. Kendall On the Generalized "Birth-and-Death" Process , 1948 .

[15]  T. Gojobori,et al.  A comparison of the molecular clock of hepatitis C virus in the United States and Japan predicts that hepatocellular carcinoma incidence in the United States will increase over the next two decades , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  R. Campos,et al.  Phylodynamics of Hepatitis C Virus Subtype 2c in the Province of Córdoba, Argentina , 2011, PloS one.

[17]  Erik M. Volz,et al.  Complex Population Dynamics and the Coalescent Under Neutrality , 2012, Genetics.

[18]  Huldrych F. Günthard,et al.  Using an Epidemiological Model for Phylogenetic Inference Reveals Density Dependence in HIV Transmission , 2013, Molecular biology and evolution.

[19]  O. Pybus,et al.  Unifying the Epidemiological and Evolutionary Dynamics of Pathogens , 2004, Science.

[20]  William Feller Die Grundlagen der Volterraschen Theorie des Kampfes ums Dasein in wahrscheinlichkeitstheoretischer Behandlung , 1939 .

[21]  O. Pybus,et al.  The Epidemic Behavior of the Hepatitis C Virus , 2001, Science.

[22]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[23]  Amalio Telenti,et al.  Cohort profile: the Swiss HIV Cohort study. , 2010, International journal of epidemiology.

[24]  Kholoud Porter,et al.  The creation of a large UK‐based multicentre cohort of HIV‐infected individuals: The UK Collaborative HIV Cohort (UK CHIC) Study , 2004, HIV medicine.

[25]  Stéphane Hué,et al.  Genetic analysis reveals the complex structure of HIV-1 transmission within defined risk groups. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  P. Donnelly,et al.  A new statistical method for haplotype reconstruction from population data. , 2001, American journal of human genetics.

[27]  T. Stadler Sampling-through-time in birth-death trees. , 2010, Journal of theoretical biology.

[28]  Erik M. Volz,et al.  Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection , 2012, PLoS Comput. Biol..

[29]  W. O. Kermack,et al.  Contributions to the mathematical theory of epidemics—II. The problem of endemicity , 1991, Bulletin of mathematical biology.

[30]  Joachim Metallmann Der Kampf um die Autonomie des Lebens , 1939 .