Campuses:

IMA Tea and more (with POSTER SESSION)

Monday, November 17, 2003 - 3:40pm - 5:00pm
Lind 400
  • A Graph-Theoretic Analysis of Global Human Genetic Structure
    Rodney Dyer (Iowa State University)
    The amount and geographic patterning of human genetic variation is an evolutionary consequence of several historical and contemporary processes including population expansion, demographic subdivision, and migration. Quantifying this variation, and in turn the extent to which these forces have acted in shaping human genetic structure is a key component to understanding the evolution of human populations. Here I present an analysis framework based upon graph-theory, which we call Population Graphs (PG). While the PG framework allows the extraction of traditional population genetic statistics such as differentiation (PhiST) and isolation by distance hat M, topological analysis of the connectedness among human populations provides heretofore-unattainable information on intra-population evolutionary history. I highlight the utility of the PG framework using data consisting of 1056 individuals assayed for 376 variable microsatellite loci sampled from 52 populations around the globe. An analysis of the topology of the human graphs reveals the following characteristics of human population genetic structure. First, groups of populations exhibit significant topological structuring consistent with geographically relevant population subdivisions. Second, the topological distances among populations are significantly correlated with geographic separation supporting the notion of isolation by distance and spatially proximate migration patterns. Finally, we show how the topology of the graph is used to identify specific populations whose patterns of connectivity prove to be critical to the movement of genetic information across the entire graph.
  • Sacle-free Networks and Sexually Transmitted Diseases: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe
    Anne Schneeberger (Imperial College London)
    Sexually transmitted infections spread through a network of contacts created by the formation of sexual partnerships. Methods developed in physics can characterise a wide range of networks through a description of the distribution of numbers of sex partnerships. It has been suggested that in the Swedish population this 'degree' distribution follows a power law and therefore indicates a 'scale-free' network. Our objectives were to test statistically whether distributions of numbers of sexual partners reported by different populations and over different time periods are well described by power laws and to estimate their exponent and its implications. Maximum likelihood estimates of the exponent of a scale free network fitted to reported distributions of numbers of partners are compared with the fit for an exponential null model. Data are taken from 4 population based surveys, three from Britain and one from rural Zimbabwe. We find that the networks can be described by a power law over a number of orders of magnitude. In addition, exponents differ significantly and meaningfully, with an 'accelerating network' formed between men who have sex with men (MSM). Networks with an exponent indicating the lack of a 'critical spread rate' are also found for the other populations except for women in Britain. Thus statistical analyses demonstrate that a scale-free network approach provides a reasonable description of distributions of reported numbers of sexual partners. Further, if these networks are formed over a short time only a very small transmission probability will be sufficient to lead to persistence of infection.
  • Stochastic Simulation of Epidemics on Large Contact Networks
    Markus Schwehm (Eberhard-Karls-Universität Tübingen)
    Joint work with Martin Eichner (Department of Medical Biometry, University of Tübingen, Germany).

    We have implemented a fast stochastic individual-based simulator the analysis of disease transmission and containment interventions. The simulator consists of a discrete event simulator for the processing of event-based models and plug-ins for different contact network topologies.

    The discrete event simulation distinguishes tree types of events. The first type implements the standard SEIRS infection dynamics with susceptible, exposed, infectious and recovered states as well as vaccination and a simple birth/death process. The second type models the visibility of the disease according to none, detectable or obvious symptoms. The third type allows to model intervention strategies (like contact tracing, quarantine and case isolation), which influence the contact structure of individuals. Events can trigger further events for the same individual and via the contact network for other individuals. All events are processed in a discrete event simulator which is optimized for large numbers of events using a priority queue (indirect heap algorithm) and can process about 50.000 events per second.

    The inhabitants of the population are represented by their internal state (infection, symptom and contact status) and represent nodes in a contact network. The modular design allows to exchange the contact network independent of the chosen discrete event model. For each individual the contact network allows to identify a limited number of contacts for transmission of the infection or for implementing contact tracing interventions. Currently there exist parameterized network generators for local, global, random and scalefree contact networks. Moreover, the data struc-ture allows to maintain arbitrary networks consis-ting of several independent layers. We were able to simulate populations of two million individuals on a personal computer.
  • The HIV Transmission Gradient
    David Bell (Affiliated Systems, Inc.)
    Of critical importance in the transmission of HIV are gatekeepers, the HIV-negative partners of persons who are HIV-infected. These are the persons at risk and they are the persons who can eventually spread the disease further. And since the highest infectivity comes in the first months after infection, usually before knowledge of infection, the behavior of these gatekeepers while they are HIV- is critical. In a sample of 267 persons from high drug use neighborhoods, we collected data on 3254 relationships involving 1271 other persons. We quantitatively describe the gradient of infection potential by which HIV can diffuse from the HIV+ population through gatekeepers to the rest of the population through both drug injection behaviors and sex behaviors.
  • Contact Tracing and Disease Control
    Ken Eames (University of Cambridge)
    Contact tracing, followed by treatment, is a key control measure in the battle against infectious diseases. It represents an extreme form of locally targetted control, a hyper-parasite acting on infection, and as such has the potential to be highly efficient, especially when dealing with low numbers of cases. Modelling contact tracing requires explicit information about the transmission pathways from each individual and hence the network of contacts. Using pair-wise approximations and full stochastic simulations to model network-based processes, the utility of contact tracing is investigated. A simple relationship between the efficiency of contact tracing necessary to eradicate infection and te basic reproductive ratio of the disease is shown to hold in a wide variety of scenarios. Only clustering within the transmission network is found to destroy this relationship, enhancing the effectiveness of contact tracing by providing alternative tracing pathways. Since the critical efficiency depends on the characteristics of individuals within the network, applying different tracing regimes within differing subpopulations can achieve the elimination of infection whilst lowering the burden on health care services.
  • Simulation of Epidemiological Models on Networks
    Simon Frost (University of California, San Diego)
    Joint work with Klaus G. Muller.

    Individual- or agent-based simulations are useful tools for understanding how the spread of an infectious agent or computer virus is affected by the structure of the underlying contact network and by the natural history of infection at an individual level. Spurred by the lack of freely available software to simulate epidemic processes on networks, we are developing a package, Epydemic, based upon SimPy (http://simpy.sourceforge.net), an open-source discrete-event simulation library written in Python, that permits the rapid prototyping of epidemic models. Infections consisting of multiple stages are easily and concisely modeled using semi-coroutines. The software includes a graphical user interface for parameter entry, result visualization etc., and an interactive console that allows the user to directly analyze components of the simulation. The poor performance normally associated with the use of an interpreted language for simulation is compensated for; by the use of efficient algorithms for the contact process and for the scheduling of events; by run-time compilation; by the use of extension modules programmed in C; and by parallelization of model runs using the Message Passing Interface. We present an example of a standard SIR model spreading in a configuration graph.
  • Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
    Matthew Salganik (Columbia University)
    The task of slowing the spread of HIV is complicated by our difficulties in collecting accurate information about certain key subpopulations, such as injection drug users and commercial sex workers. Using a new sampling and estimation method called respondent-driven sampling, researchers are now able to collect information about some of these key subpopulations more quickly, cheaply, and accurately than before.

    A respondent-driven sample is selected with a snowball-type design (sample members recruit their friends). Despite the numerous biases inherent in this sample selection process, an estimation procedure is developed which, under specified (and quite general) conditions, can be used to make unbiased estimates about the proportion of the population with a specific trait -- for example the percentage of injection drug users in a city with HIV. The estimation procedure uses the sample to make inference about the social network connecting the subpopulation. This network information is then used to make inference about the characteristics of the subpopulation. It is also the case that these estimates are asymptotically unbiased no matter how the seeds (initial members of the sample) are selected.