University of Washington - Fred Hutchison Cancer Research Center
The overall objective of this research is to develop, validate, and implement mathematical models for the transmission and within-host dynamics of bioterrorism agents or naturally occurring infectious diseases. These models will be used to assess the effectiveness and efficacy of various interventions to aid the distribution and allocation of resources in response to such outbreaks.
Specifically, we plan to develop epidemic simulation models for the transmission of bioterrorism agents or naturally occurring infectious diseases. We will develop stochastic epidemic simulation models for a typical American community structured for mixing in households, neighborhoods, preschool groups, schools, work places, hospitals, and other settings as needed. We will use the epidemic simulation models to evaluate the effectiveness of interventions involving surveillance and containment, vaccination, antimicrobials, closing of key institutions, and other control strategies. In order to understand how to use scarce disease control resources, we will develop stochastic optimization methods to find the best intervention strategy, constrained by the resources available, for the infectious diseases in question.
We plan to adapt the epidemic simulation models for smallpox, pandemic influenza, SARS, and other possible bioterrorism agents or naturally occurring infectious diseases. This will provide a simulation resource to help determine the best control strategy should an actual attack or naturally occurring biological threat arise.
We plan to use graph theoretic methods to develop techniques for constructing simulation model populations that have similar contact connectivity to actual urban populations in the US. We plan to exploit the theory in order to calculate critical quantities from our simulation populations such as reproductive numbers. This will involve joint work with investigators at Los Alamos National Laboratory.
With respect to epidemiologic studies, we plan to use the epidemic simulation models to determine the important parameters for infection transmission and to use this information to help design field studies and intervention studies for the infectious diseases in question. This will include the use and development of statistical methods to estimate the important parameters and variables from data available for the infectious diseases in question.
A second important area of our research is to develop models of the within-host dynamics of pathogens which cause acute infections in vertebrates. We plan to construct general exploratory models of how the interplay between the pathogen and host immune response is responsible for the pathogenesis observed during infection. We will further refine, develop further, and test these models of pathogenesis including the use of existing data on lymphocytic choriomeningitis virus (LCMV) and listeria monocytogenes (LM) infections of mice. We then plan to use this experience to extend the models to examine the more challenging acute infections of humans and, in particular, of pathogens of importance as emerging infectious diseases and potential agents of bioterrorism.
Part of the second area of research will be to model the development of resistance under selective pressure by antimicrobial and antiviral treatment and prophylaxis by antimicrobials or vaccination. These models will be combined with the stochastic epidemic simulation models to examine the spread of resistance within the host population. We will begin this work with an application to the problem of using influenza antiviral agents to contain pandemic influenza.
MIDAS Project Website: University of Washington-Fred Hutchinson Cancer Research Center
Elizabeth Halloran, DSc.
Ira M. Longini, Jr., Ph.D.
(University of Florida)
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