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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
6R01AI087520-09
Application #
9635708
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Refsland, Eric William
Project Start
2010-06-15
Project End
2021-02-28
Budget Start
2019-03-01
Budget End
2021-02-28
Support Year
9
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Triad National Security, LLC
Department
Type
DUNS #
080961356
City
Los Alamos
State
NM
Country
United States
Zip Code
87545
Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie et al. (2018) Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases. Epidemics 23:1-10
Goyal, Ashish; Romero-Severson, Ethan Obie (2018) Screening for hepatitis D and PEG-Interferon over Tenofovir enhance general hepatitis control efforts in Brazil. PLoS One 13:e0203831
Romero-Severson, Ethan O; Ribeiro, Ruy M; Castro, Mario (2018) Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series. Front Microbiol 9:1529
Song, Hongshuo; Giorgi, Elena E; Ganusov, Vitaly V et al. (2018) Tracking HIV-1 recombination to resolve its contribution to HIV-1 evolution in natural infection. Nat Commun 9:1928
Fun, Axel; Leitner, Thomas; Vandekerckhove, Linos et al. (2018) Impact of the HIV-1 genetic background and HIV-1 population size on the evolution of raltegravir resistance. Retrovirology 15:1
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
Ratmann, Oliver; Hodcroft, Emma B; Pickles, Michael et al. (2017) Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison. Mol Biol Evol 34:185-203
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

Showing the most recent 10 out of 37 publications