Epidemiology System (EPISIMS)

EpiSims is a tool for simulating the spread of epidemics at the level of individuals in a large urban region, taking into account realistic contact patterns and disease transmission characteristics. It knits together  parameterized models for the progress of a disease within a host, transmission between hosts, and contact patterns of the hosts. It provides estimates of both the geographic and demographic distribution of disease as a function of time.

Because of its extremely high level of resolution, EpiSims makes possible research into a wide variety of topics.

• Parameter values for the within-host disease model can be based on the demographics of each person.  Thus, as just one example, age or occupation-related susceptibility or  vaccination history can be easily incorporated into the simulation.
• Similarly, transmission parameters can be based not only on the demographics of the individuals involved, but also the nature of the activities they are engaged in and their location. That is, the transmission rate among elementary school children in a classroom can differ from the transmission rate among the same children on a soccer field or from adults in an office setting.
• Furthermore, disease models based on a concept of "load" (amount of virus or bacteria present) apply to locations as well as humans, and can be used to model the presence of non-human vectors in a location or the history of visits to a location by an infected person.

• Finally, EpiSims will simulate  the introduction of counter-measures such as quarantine, vaccination or  antibiotic use.

Achieving this level of resolution requires having good estimates of the contact patterns among individuals in a large urban area. Others have tried an individual-based epidemiology simulation, but have not been able to scale to large populations because of the lack of contact data tied to the demographics of individuals.

EpiSims will take advantage of human mobility information derived from TRANSIMS. TRANSIMS estimates the movement of people as  constrained by transportation infrastructure based on census data and activity surveys taken from a small sample  (~2000 households) of the population. The resulting mobility estimates, and hence the epidemiological estimates, are of course specific to the  census, survey, and infrastructure of a particular city. Part of the research in the EpiSims project will be to characterize the estimates of contact patterns (the "social network") both to understand how  they might vary from city to city and to identify how the epidemiology  depends on those characteristics.

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