The U.S. Centers for Disease Control have asked each of the U.S. states to maintain an influenza surveillance network. The network consists of a number of primary healthcare providers who report weekly to the state on the number of patients that visited their office, and the number of those patients that exhibited influenza-like-illness symptoms. Each state has recruited its own providers into its surveillence network.

In this talk, we discuss how to create a well-performing influenza surveillence network. There are thousands of possible providers which the state could recruit into the surveillance network. Which of them should the state recruit? How should those providers be spread out geographically across the state? And can new technologies like Google Flu Trends replace or augment traditional surveillance networks?

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Different radioactive materials and their associated decay chains produce different radiation energy spectra.  Naturally, this has lead to the study of identifying a radioactive material based on its energy spectrum, called radiation spectroscopy.  One of the central problems in radiation spectroscopy is the resolution of the radiation detector.  As radiation hits the detector, the noise from the detection equipment and the random process of radiation transportation smears out the radiation spectrum of the radioactive material, making wide energy peaks instead of sharp ones.

Seeded Localized Averaging (SLA) is a real-time spectrum aquisition method that allows us to sharpen the peaks in aquired energy spectra.  SLA was patented by my co-author, Valentin Jordanov, in 2007.  In this talk, presented in ANIMMA 2011, for the first time, we prove some basic theoretical properties of the SLA transformation.  We also describe the transformation and show some practical examples.

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My talk from the Epidemics² conference in Athens describes our recent application of bandit-based search for computing antiviral distributions for the U.S. National Antiviral Stockpile.

At the beginning of the summer in 2009, the U.S. government learned of the swine-origin influenza outbreak.  Immediately, the government released 11 million courses of antivirals from the national stockpile for immediate use by the states.  The states themselves had purchased some antivirals, totaling 35 million courses available for immediate use.  Additionally, the U.S. government had 50 million courses in reserve in the national stockpile.  We address the question of how and when antivirals from U.S. National Antiviral Stockpile should be released to best control an influenza outbreak.  We do this quantitatively, using a national scale disease model and recent simulation optimization techniques.

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Lauren Ancel Meyers, Sebastian Goll, and I have developed a simulation and optimization software bundle targeted at computing disease control strategies.  The bundle is called the Disease Control System (DiCon) and comes with a ready-made national scale disease simulator, an optimization platform for computing intervention strategies, and a visualization toolkit.  The visualization toolkit was developed by Greg Johnson at TACC and the simulator was developed by Priyank Patel.

Talks detailing both the features of DiCon and a recent application of the system to antiviral control of H1N1 have recently been uploaded.

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My talk from the San Diego INFORMS, 2009, describes a general model for vector-borne disease modeling and control.  But, more interestingly, the talk covers our application of that model to the spead of Leishmaniasis in Texas.

Leishmaniasis has been spreading north, from the border with Mexico and into Texas for the past several decades.  The disease made a jump in around the year 2000 from west Texas to east Texas.  We found that this was due to the underlying reservoir populations.  In addition, in this talk, I discuss computational techniques for computing control locations to stop the spread of Leishmaniasis.

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