Modeling HIV and STD in Drug User and Social Networks

Source: NIDA , 7R01DA012831

Active: 6/22/01-5/31/06

Investigator: Martina Morris, PI

Mark Handcock (University of Washington)

Richard Rothenberg (Emory University)

David Hunter (Penn State University)

James Moody (Ohio State University)

Because infectious diseases are transmitted from person to person, our
understanding of disease transmission and prevention are rooted in a theory of
population transmission dynamics.
The epidemiology of sexually transmitted diseases (STD) like HIV – how quickly
they spread and who gets infected – is driven by the network of
person-to-person contacts. Early
epidemiological studies and mathematical models of this process provided a
number of insights that led to changes in STD control strategies during the
1980s. With the advent of HIV,
however, new challenges have emerged.
Like other incurable infections, HIV has the potential to spread very
broadly in a population under the right circumstances. This makes the “core group” concept
from the 1980s somewhat less effective for HIV prevention. Much work has been done during the last
15 years to identify which aspects of the partnership network structure matter
for the spread of HIV, and to collect data on partnership networks in many
populations. Simulation studies
have played a crucial role in this effort, by identifying the type of network
structures that have large impacts on transmission dynamics. The confluence of data, theory, and
methods has created a clear agenda for quantifying the influence of networks on
HIV transmission risks. While many
of the pieces of the emerging research program are now in place, there is a
wide gulf between the network data and the current simulation modeling
frameworks. Simulations typically
create network effects indirectly, by varying parameters of some convenient
function to produce a change in simulated networks. The observable network measures are thus *outcomes* of the model, rather than
inputs. While this strategy has
been very useful for orienting initial research, it has hamstrung our ability
to evaluate the empirical transmission risk in observed networks. We propose a
solution here that is based on statistical models for random graphs: it can be used to estimate network
parameters from data, and then simulate networks with those properties. Specifically, we propose to: (1)
Develop random graph models for estimating network parameters and simulating
evolving networks, with epidemiologically relevant formal tests for
goodness-of-fit. Both the
estimation and simulation methods will be based on a common Markov Chain Monte
Carlo (MCMC) algorithm, which will enable researchers for the first time to
simulate networks that have the same statistical properties as those observed
in real data. (2) Use these
methods to identify the networks structures that matter most for the
transmission of HIV. In
particular, we will examine the independent and joint effects of needle sharing
and sexual transmission, and we will test whether assortative mixing and
concurrency determine the bulk of the transmission potential in a network. The methods developed will provide a
systematic empirical basis for individual-level prevention strategies that
focus on partnership interventions.
It will enable public health professionals to identify population-level
prevention strategies that make a network less vulnerable to spread. And it will identify the type of
network data needed to inform such efforts.

**Please send comments or suggestions to
morrism@u.washington.edu.**

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