HIV continues to infect human populations worldwide, emphasizing the need for epidemiological tools that can accurately describe transmission patterns. Thus, the methods we will develop will have specific impact on HIV vaccines;evolution;epidemiological parameters: spread of infection in different groups;intervention;and more generally on the fundamental science of infectious diseases. The overall goal is to understand the relationship between virus evolution and its epidemiological history, and to create epidemiological tools that can make reliable contact tracings and assess changes in epidemic dynamics. We have recently shown that the epidemic rate is inversely correlated to the virus evolutionary rate on the population level. Thus, the specific hypothesis behind the proposed research is that there is a relationship between the speed at which an epidemic moves through a human population and the rate at which the virus evolves in that population. We have observed that there are discrepancies between transmission histories and viral phylogenies, and because the inferences of epidemics are based on phylogenetics, it becomes important to understand the limitations in such inferences. Based on this the specific aims of this proposal are to: 1. Create a model that accurately describes the connection between transmission history and viral phylogeny. Preliminary results suggest that there are """"""""hidden lineages"""""""" in viral phylogenies that are involved in transmission events, potentially misleading reconstruction of transmission events. We will especially investigate the effects of the effective population size in the donor, the bottleneck at transmission, and incomplete lineage sorting during transmission and sampling.
We aim to estimate meaningful confidence levels on reconstructed person-to-person transmissions enabling us to explore alternative hypotheses in a statistical framework specifically designed for epidemiological tracking. 2. Identify the mechanism that correlates epidemic rate and virus evolutionary rate. We will decipher the connection between epidemic rate and viral evolutionary rate. Currently, we have four alternative explanations that may cause the observed correlation between epidemic and evolutionary rate (host immune selection, viral generation time effects, selection during transmission, and recombination effects). We will use different gene sequence data, codon positions as well as amino acid signatures to discriminate between these hypothetical explanations. We will use large datasets to develop epidemiological models that include these four hypothetical explanations to investigate their effects on the population level, and also model social networks and epidemic and phylogeographic dynamics.

Public Health Relevance

The mathematical methods developed in this project aim to give better inferences of the spread of pathogens, here mainly HIV. At the contact tracing level we will estimate meaningful confidence levels on reconstructed person-to-person transmissions enabling us to explore alternative hypotheses in a statistical framework specifically designed for epidemiological tracking. At the epidemic level we will develop methods that can follow and signal when important changes in spread patterns occur, including the origin of the infection.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Sanders, Brigitte E
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Los Alamos National Lab
Los Alamos
United States
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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
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
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