Population mobility models of urban disease outbreak

描述(由申请者提供)：这个项目将根据个人在社会网络中的角色设计、建立和验证疾病流行病学和反应系统的模型。它将侧重于数学和计算方法，以模拟接触病原体及其产品以及不同干预策略的有效性和后果。特别是，它将探讨城市地区人类接触模式对疾病传播动态的影响，以及拟议的对故意或自然释放病原体的早期和中期反应的后果。 该项目将生产一个模拟系统，跟踪多种共同循环和相互作用的疾病在合成人口中的传播。灵活的用户指定方案将包括这样的参数要素：宿主-病原体的相互作用；疾病传播动态；人群的初始健康状态；病原体进入人群的模式；以及可能在人口统计和/或地理上有针对性的应对策略的选择和调度。模拟将依赖于现有对150万人的合成城市人口中接触模式的估计。研究主题将包括在爆发时这些模式可能会如何变化。 此外，该项目将对模拟的核心部分估计的接触模式进行数学、结构分析。这项分析的目标是：确定最能表征其对流行病的反应和缓解战略的社会网络的特征；开发在超大型网络中评估这些特征的有效算法；估计这些特征在不同城市地区和不同人口规模中的变异性；以及提供生成社会网络的随机但现实的实例的能力。 所有结果将及时传达给迈达斯信息学集团。该项目旨在提供软件(模拟本身和社交网络的分析工具)以及一组标准情景模拟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