Improving Network Analysis and Visualization for Infectious Disease Control

描述（由申请人提供）：本研究旨在改变传染病调查，控制和研究的社会网络模型的使用和解释。SARS或H1N1流感等流行病主要通过社会途径传播，但我们往往缺乏识别和阻断这种传播的工具。使用网络 疾病传播模型已广泛扩展到包括接触调查、疫苗接种政策、流行病模型和疾病监测。然而，这些网络对用户提出了认知挑战，并且通常是静态的和不可理解的。虽然有许多工具可以可视化和分析网络数据，但这些方法的有效性、一致性和对健康结果的贡献尚未得到广泛评估。与基因组、临床和地理数据的不一致整合进一步限制了这些工具的利基应用。我们建议，通过交互式用户界面，与临床，基因组和地理数据的集成将增加网络分析技术的可访问性。本研究的目的是提高网络分析的有效性、可解释性和实用性，以便将这些综合信息有效地纳入常规传染病控制。为了实现这一目标，我们将1）开发新的方法，将不同的流行病学数据（地理信息系统，基因组和临床）整合到社会网络分析，2）扩展爆发调查分析软件，以可视化这些综合数据，和3）评估网络可视化技术的效用，利用这些综合数据进行传染病控制。使用结核病、流感和百日咳等疾病的真实的和模拟爆发，我们的方法将评估交互式可视化、缺失数据、社会数据与临床/地理/基因组信息的联合显示以及动态网络显示的影响。测量的结果指标将包括爆发检测的效率（灵敏度、特异性和及时性）以及软件可用性测量。这些研究将控制疫情规模、信息缺失程度和个人用户影响等变量。通过这种系统的方法，研究旨在扩大网络模型对人类健康的影响。这项研究的最终结果将是：1）为在关键生物医学环境中使用网络分析提供更广泛和更深入的证据基础; 2）改善传染病控制的多样化和复杂数据的决策; 3）开发适用于广泛生物医学数据的新可视化算法。这项研究将通过开发和验证生物医学数据的创新数据集成，交互式可视化和协作技术来推进信息学领域。

DESCRIPTION (provided by applicant): This research aims to transform the use and interpretation of social network models for infectious disease investigation, control, and research. Epidemics such as SARS or H1N1 influenza are transmitted largely through social routes, yet we often lack the tools to identify and interrupt this transmission. The use of network models of disease transmission has expanded broadly to include contact investigation, vaccination policy, epidemic models, and disease surveillance. However, these networks present cognitive challenges to users and are often static and incomprehensible. While many tools exist to visualize and analyze network data, these methods have not been broadly evaluated for their validity, consistency, and contribution to health outcomes. Inconsistent integration with genomic, clinical, and geographic data further limits these tools to niche applications. We propose that through interactive user interfaces, integration with clinical, genomic, and geographic data will increase the accessibility of network analytic techniques. The goal of this research is to increase the validity, interpretability, and utility of network analyse so that this synthesized information can be effectively incorporated into routine infectious disease control. To accomplish this, we will 1) develop new methods to integrate diverse epidemiologic data (GIS, genomic, and clinical) into social network analyses, 2) extend the Outbreak Investigator analysis software to visualize this integrated data, and 3) evaluate the utility of network visualization techniques for infectious disease control utilizing this synthesized data. Using real and simulated outbreaks from diseases such as tuberculosis, influenza, and pertussis, our methods will assess the impact of interactive visualization, missing data, joint displays of social data with clinical/geographic/genomic information, and dynamic network displays. Outcome metrics measured will include the efficiency of outbreak detection (sensitivity, specificity and timeliness), and software usability measures. These studies will control for variables such as outbreak size, degree of missing information, and individual user effects. Through this systematic approach, the research aims to extend the reach and impact of network models on human health. The end result of this research will be to 1) provide a broader and deeper evidence base for use of network analysis in a key biomedical setting, 2) improve decision-making with diverse and complex data for infectious disease control, and 3) development of new visualization algorithms applicable to a broad array of biomedical data. This research will advance the field of informatics through the development and validation of innovative data integration, interactive visualization, and collaborative technologies for biomedical data.