Toward a gridMathematica Transformative Tool for Understanding Complexity in Infectious Disease Epidemics

Diglio A. Simoni

RTI International

Abstract

The threat of infectious disease epidemics continues to be of utmost concern. The World Health Organization estimates that approximately 18 million persons worldwide died in 2007 from infectious diseases, and the CDC estimates that a US influenza epidemic could result in 100,000-200,000 deaths, 700,000 hospitalized patients, and 100-200 billion dollars in economic impact. For example, this year alone, from April 15, 2009 to July 24, 2009, US states reported a total of 43,771 confirmed and probable cases of novel influenza A (H1N1) infection. Of these cases reported, 5,011 people were hospitalized and 302 people died. Understanding the spread of disease requires the construction of predictive multiscale computational models of the associated processes. The predictions of these models can be used to suggest appropriate containment strategies to prevent disease outbreaks by assisting policymakers, public health workers, and other researchers in making better-informed decisions about emerging infectious diseases in times of crisis, due to both naturally-occurring and man-made pathogens. At the scale of social networks, we use agent based models (ABM) on synthetic populations derived from real census data to assist us in understanding the complexity of epidemiological phenomena. In this talk we will describe our initial attempts at using the rich semantics of gridMathematica to hide some of the complexities of programming distributed simulations on HPC clusters that can given simulation designers the freedom to more easily develop highly resolved models of the dynamics of disease spread in mobile population networks.