Despite ongoing prevention campaigns, the HIV epidemic persists on a global scale. To monitor the effects of intervention campaigns, and more generally, to estimate incidence and prevalence in human populations, patterns of disease spread must be accurately reconstructed. Our project brings together a team of experienced researchers from clinical, molecular biology, epidemiological, mathematical, and evolutionary fields. We will carefully examine phylogenetic limitations and develop statistical methods to address and quantify their effects on transmission reconstruction. We will build novel phylodynamic inference methods that adequately take these limitations into account, and incorporate recent statistical advances developed in social network and epidemiological sciences. We will use seven large datasets describing both HIV within-host and between-host evolution and transmission. These data comprise many thousands of HIV cases from the US, Europe, former Soviet Union, and Africa, described by HIV sequence, clinical and demographic data. The overarching hypothesis of this project is that the evolutionary process of HIV-1 records genetic mutations affected by its epidemiological history. Thus, in this project we aim to extract epidemiological information from phylogenetic analyses of HIV and integrate it with clinical, demographical, and geographical data to push the boundaries of current state-of-the-art epidemiological inference methods.
Our specific aims are: 1) Develop a more realistic within-host model of HIV evolutionary dynamics; 2) Jointly infer the unobserved transmission history and virus phylogeny; and 3) Create public software resources that facilitate molecular epidemiology inferences.

Public Health Relevance

Combining evolutionary theory, multi-scale dynamic modeling, and large-scale clinical and sequence data will transform how epidemics are investigated and prevented. Melding these resources, we will develop methods for contact tracing and population-level statistics and make them easily available to the general public health field.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Kuo, Lillian S
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Los Alamos National Lab
Los Alamos
United States
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Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie et al. (2017) Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases. Epidemics :
Volz, Erik M; Romero-Severson, Ethan; Leitner, Thomas (2017) Phylodynamic Inference across Epidemic Scales. Mol Biol Evol 34:1276-1288
Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan et al. (2017) Inference of Transmission Network Structure from HIV Phylogenetic Trees. PLoS Comput Biol 13:e1005316
Romero-Severson, Ethan O; Bulla, Ingo; Hengartner, Nick et al. (2017) Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation. Genetics 207:1089-1101
Pineda-Peña, Andrea-Clemencia; Varanda, Jorge; Sousa, João Dinis et al. (2016) On the contribution of Angola to the initial spread of HIV-1. Infect Genet Evol 46:219-222
Bártolo, Inês; Calado, Rita; Borrego, Pedro et al. (2016) Rare HIV-1 Subtype J Genomes and a New H/U/CRF02_AG Recombinant Genome Suggests an Ancient Origin of HIV-1 in Angola. AIDS Res Hum Retroviruses 32:822-8
Romero-Severson, Ethan O; Bulla, Ingo; Leitner, Thomas (2016) Phylogenetically resolving epidemiologic linkage. Proc Natl Acad Sci U S A 113:2690-5
Yoon, Hyejin; Leitner, Thomas (2015) PrimerDesign-M: a multiple-alignment based multiple-primer design tool for walking across variable genomes. Bioinformatics 31:1472-4
Romero-Severson, E O; Volz, E; Koopman, J S et al. (2015) Dynamic Variation in Sexual Contact Rates in a Cohort of HIV-Negative Gay Men. Am J Epidemiol 182:255-62
Romero-Severson, Ethan Obie; Lee Petrie, Cody; Ionides, Edward et al. (2015) Trends of HIV-1 incidence with credible intervals in Sweden 2002-09 reconstructed using a dynamic model of within-patient IgG growth. Int J Epidemiol 44:998-1006

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