Synthetic Populations for Epidemic Modeling

Infectious disease outbreaks can emerge without warning and spread rapidly through a population. They place a huge cost upon society, both directly through disease and mortality, and indirectly through impacts upon the food supply, the economy, and so on. The WHO estimates that, in lower income countries, almost one-third of all deaths are due to infectious diseases. In recent years, we have seen several examples of epidemics that quickly spread across large populations, such as the ongoing Ebola outbreak, the swine flu epidemic of 2009, and the SARS epidemic of 2003, in addition to more localized (but still large) epidemics such as the Cholera outbreak in Haiti in 2010 and seasonal flu and malaria outbreaks in various parts of the world. Epidemics typically spread through contact between infectious and susceptible persons. Therefore it is important to understand the social contact structure in a population in order the characterize or forecast the spread of an epidemic. Mathematical modeling of epidemics has contributed to epidemiology for a long time. These models typically use systems of ordinary differential equations to analyze the rate of progression of the disease through a population. More recently, these models have been extended to the network setting, leading to insights about how network topology affects epidemic characteristics. At the same time, with increasing availability of data an