**A Multi-fidelity Approach for Efficient Network-based Simulation of
Disease Outbreaks**

Brian M. Adams

Optimization and Uncertainty Estimation Department

Computation, Computers, Information, and Mathematics Center

Sandia National Laboratories

Effective public health interventions during a disease outbreak depend
on rapid characterization of the initial outbreak and pathways for
pathogen spread. Epidemiologically-based modeling and simulation can
characterize both and enable practitioners to test intervention
strategies. While compartmental differential equation models are
often used to represent bulk epidemic properties, they are unsuitable
for early time simulations (first few days) when a small number of
people are infected (and even fewer symptomatic), nor are they capable
of representing spatial disease spread.
In this talk I will present social contact network-based models for
early epoch simulation of disease outbreaks and bioterror incidents.
They generate detailed predictions of disease spread, including
tracking symptomatic individuals, for use in a Bayesian inverse
problem context to determine the initial location, time, and size of
an outbreak. Our implementation allows for high-fidelity predictive
simulations, made efficient though scalable parallelism, as well as
reduced-order simulations through population and/or location sampling,
and static (time-projected) graph-based simulation, which are
practical for inverse problem solution. Efficiently inverting data on
early symptomatic presenters characterizes the original source, and
the forward simulation can then suggest others who may be at risk

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