Population mobility models of urban disease outbreak

描述（由申请人提供）：本项目将设计，建立和验证疾病流行病学模型和基于个人在社会网络中的角色的反应系统。 它将侧重于模拟接触病原体及其产品的数学和计算方法，以及不同干预战略的有效性和后果。 特别是，它将探讨城市地区人类接触模式对疾病传播动力学的影响，以及对故意或自然释放病原体的拟议早期和中期应对措施的后果。 该项目将建立一个模拟系统，跟踪多种共同传播和相互作用的疾病在合成人群中的传播。 灵活的用户特定情景将包括以下参数化要素：宿主-病原体相互作用;疾病传播动态;人口的初始健康状况;病原体进入人口的模式;以及应对战略的选择和时间安排，这些战略可能是人口统计和/或地理目标。 模拟将依赖于对150万人的合成城市人口的接触模式的现有估计。 研究主题将包括这些模式在爆发时可能如何变化。 此外，该项目还将对模拟核心的估计接触模式进行数学和结构分析。 本分析的目标是：确定社交网络的功能，最好的特点，他们的反应流行病和缓解策略;开发有效的算法，在非常大的网络评估这些功能;估计这些功能之间的差异不同的城市地区和不同规模的人口;并提供生成随机但现实的社交网络的实例的能力。 所有结果将及时传达给MIDAS信息组。 该项目打算提供软件（模拟本身和社交网络的分析工具）以及通过模拟一套标准情景而产生的流行病学数据

DESCRIPTION (provided by applicant): 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 modelling 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