This project will design, build, and validate models of disease epidemiology and response systems based on individuals’ roles in a social network. It will focus on mathematical and computational methods for modeling exposure to pathogens and their products as well as the effectiveness and consequences of different intervention strategies. In particular, it will explore the effects of human contact patterns in urban areas on disease transmission dynamics and the consequences of proposed early and mid-term responses to intentional or natural releases of pathogens.
The project will produce a simulation system tracking the transmission of multiple co-circulating and interacting diseases in a synthetic population. Flexible user-specified scenarios will include such parameterized elements as: host-pathogen interactions; disease transmission dynamics; initial health state of the population; the mode of a pathogen’s introduction into the population; and choice and scheduling of response strategies, which might be demographically and/or geographically targeted. The simulation will rely on existing estimates of contact patterns in a synthetic urban population of 1.5 million individuals. Research topics will include how these patterns might change in the presence of an outbreak.
In addition, the project will undertake a mathematical, structural analysis of the estimated contact patterns at the heart of the simulation. The goals of this analysis are: to determine the features of social networks that best characterize their response to epidemics and mitigation strategies; to develop efficient algorithms for evaluating these features in very large networks; to estimate variability in these features among different urban areas and different size populations; and to provide the capability for generating stochastic but realistic instances of social networks.
All results will be communicated in a timely fashion to the MIDAS Informatics Group. The project intends to provide software (both the simulation itself and analysis tools for social networks) and also epidemiological data arising from simulation of a set of standard scenarios.