|Title / Titel||Using viral sequence data to inform the parametrization of mathematical models of theHIV-1 epidemic in Switzerland|
|Abstract (PDF, 14 KB)|
|Summary / Zusammenfassung||The mechanisms, biology and sociodemographic components of HIV transmission are still poorly understood, because of the difficulties to obtain experimental and clinical data. Under these circumstances, population-level epidemiological studies are a very informative way to analyze transmission. Previous studies have relied on mathematical models or phylogenetic analyses to describe patterns of the HIV epidemics, but few attempts have been undertaken to integrate these two different methods.
The projects proposed here will follow the approach to first perform phylogenetic analyses and then to use the results to parameterize an established stochastic model of a generalized HIV epidemic in a developed country. The Swiss HIV Cohort Study (SHCS) and the SHCS drug resistance database provide an optimal setting for this undertaking, because the patient population is highly representative for the HIV epidemic in Switzerland. This will allow me to construct a clearly defined denominator for the analysis, which is prerequisite for the estimation of unbiased measures such as transmission rates or prevalence of transmitted HIV drug resistance.
For the first project I will use a cross-sectional phylogenetic approach to study transmission chains and transmitted drug resistance. Rather than pooling all available data like most other studies, I will primarily analyze viral genetic sequences from a clearly defined, highly representative sub-population of HIV infected individuals. This data set of “tracked” individuals will be complemented with additional viral sequences, and phylogenetic trees will be constructed. I will then investigate the “fate” of the tracked patients and descriptively analyze, how often tracked individuals are captured within transmission chains and how frequently transmitted drug resistance is observed. This approach further offers the opportunity to identify potential sources of transmission chains involving tracked individuals.
The second project aims at applying methods from multivariate statistics and machine learning on viral sequence data to identify genetic correlates (mutations) with sociodemographic factors, such as geographic region or risk group. Classification rules will be derived, tested, and eventually applied to 3000 unlinked sequences from the drug resistance database. This project will help to assess the completeness and representativeness of the SHCS and will eventually lead to more accurate estimates of the HIV infected population in Switzerland.
Taken together, the projects suggested here will lead to a better understanding of the factors that are shaping the HIV epidemic in Switzerland and may provide valuable insights to inform guidelines and the planning of prevention measures.
|Keywords / Suchbegriffe||HIV, transmission, phylogeny, drug resistance|
|Project leadership and contacts /
Projektleitung und Kontakte
|Funding source(s) /
|SNF (Personen- und Projektförderung), Foundation
Swiss HIV Cohort Study
|Duration of Project / Projektdauer||Jan 2010 to Jan 2013|