Phylogenetic analysis has played a crucial role in increasing our understanding of many aspects of viral and bacterial pathogen biology. Recent advances in evolutionary analysis of sequence data, including time- stamped data and deep sequencing, have allowed quantitative description of epidemic structure for viruses like HIV and HCV. Phylogenetic approaches applied to local datasets of viral sequences with high density coverage of the target population, both from research cohorts and routine clinical care, have been used in studies of transmission correlates, vaccine efficacy evaluation, and various aspects of public health. The non- random structure of the underlying transmission network, its role in the epidemic and its implications for treatment and prevention can now be inferred from sequence data and modeled. In this project, we will develop more innovative models of pathogen transmission combining population genetics, sequence evolution, and network theory, provide efficient method implementation and fast approximate algorithms scalable to global-scale datasets, evaluate the effect of prevention and treatment approaches on epidemic dynamics in five localized epidemics of HIV and HCV, and model generalized epidemics for these and other pathogens. By developing computational and statistical methods that incorporate and analyze pathogen sequence and other epidemiologic data, we will be able to infer and characterize transmission networks to best identify targets for the most effective and parsimonious use of prevention interventions.
Network science and molecular sequence analysis are rapidly becoming central to understanding how contact and sexual networks influence the establishment, spread, and treatment of many important pathogens, including human immunodeficiency virus type 1 and hepatitis C virus. Increased sequencing and computing capacities, new network science, and modeling approaches are ready to be exploited for this purpose. The research proposed here will develop the tools needed for HIV, HCV and other pathogen research and by public health communities to characterize local and global epidemics, ascertain efficacy of prevention interventions, and determine how to target such interventions to reduce R0 below 1 and extinguish the epidemics over the long term.
|Frost, Simon D W; Magalis, Brittany Rife; Kosakovsky Pond, Sergei L (2018) Neutral Theory and Rapidly Evolving Viral Pathogens. Mol Biol Evol 35:1348-1354|
|Spielman, Stephanie J; Kosakovsky Pond, Sergei L (2018) Relative evolutionary rates in proteins are largely insensitive to the substitution model. Mol Biol Evol :|
|Weaver, Steven; Shank, Stephen D; Spielman, Stephanie J et al. (2018) Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes. Mol Biol Evol :|
|Volz, Erik M; Didelot, Xavier (2018) Modeling the Growth and Decline of Pathogen Effective Population Size Provides Insight into Epidemic Dynamics and Drivers of Antimicrobial Resistance. Syst Biol 67:719-728|
|Ragonnet-Cronin, Manon; Jackson, Celia; Bradley-Stewart, Amanda et al. (2018) Recent and Rapid Transmission of HIV Among People Who Inject Drugs in Scotland Revealed Through Phylogenetic Analysis. J Infect Dis 217:1875-1882|
|Spielman, Stephanie J; Kosakovsky Pond, Sergei L (2018) Relative evolutionary rate inference in HyPhy with LEISR. PeerJ 6:e4339|
|Kosakovsky Pond, Sergei L; Weaver, Steven; Leigh Brown, Andrew J et al. (2018) HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens. Mol Biol Evol 35:1812-1819|
|Caskey, Marina; Schoofs, Till; Gruell, Henning et al. (2017) Antibody 10-1074 suppresses viremia in HIV-1-infected individuals. Nat Med 23:185-191|
|Brayne, Adam B; Dearlove, Bethany L; Lester, James S et al. (2017) Genotype-Specific Evolution of Hepatitis E Virus. J Virol 91:|
Showing the most recent 10 out of 30 publications