Author Profile

Sergei Pond

http://hyphy.org/sergei

Institution: University of California San Diego

Department: Medicine

City: San Diego

State: CA

Country: USA

Following formal undergraduate training in computer science (at Kiev State University, Ukraine), I received a PhD from the interdisciplinary program in Applied Mathematics at the University of Arizona. My theoretical graduate research into statistical methodology for evolutionary analyses of coding sequence alignments found an application in an HIV research group at UCSD, which I joined as a postdoctoral fellow in 2003. Presently, I am an associate professor in the Divisions of Infectious Diseases and Biomedical Informatics in the UCSD Department of Medicine. In addition, I am the director the Bioinformatics, Evolutionary Analysis and Statistics Core at the UCSD Center of AIDS Research. My research interest include developing models and computational approaches for comparative analysis of sequence data, especially large and rich data set from measurably evolving pathogens, such as HIV-1, Influenza A virus and Hepatitis C virus. My group has published a number of methodological and applied papers applying evolutionary algorithms and machine learning techniques to complex problems in sequence evolution, especially in the context of HIV population history, adaptation to new hosts, transmission, immune escape, and the development of drug resistance. We have recently begun to develop the tools necessary to analyze and interpret large next generation sequencing data sets obtained from individual HIV positive subjects. I am also actively involved in open source software development. I am the primary developer of the molecular evolution package HyPhy (www.hyphy.org) and the companion evolutionary analysis webserver www.datamonkey.org.

Recent Posts

Phylodynamic Analysis of Ebola Virus in the 2014 Sierra Leone Epidemic

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Background: The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes.

Methods: We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak.

Results: We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions.

Conclusions: Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.