RAPID: Developing Advanced Modeling and Analysis Tools to track Human Movement Patterns and Coronavirus / Infectious Disease Spread Dynamics in Geographical Networks

这一快速提案的更广泛影响是为与大流行病毒相关的政策提供信息，如冠状病毒病2019年(新冠肺炎)，因为它们显著影响国家健康和经济。为了保护社会免受病毒的侵害，每个国家/州/地区都需要动态调整卫生政策，规划近期卫生保健能力，并在较短的时间延迟内控制人口流动。对病毒传播的准确实时预测对于使卫生系统对疾病变化和中断事件(例如，人员配备和供应短缺)做出快速和主动的反应至关重要。该项目致力于基础研究，以开发人员流动和病毒传播动力学的模拟模型，预测感染人群在空间地理网络中的实时位置，并开发决策支持工具，用于在破坏性事件和过程中设计医疗保健政策。拟议的研究处于工程学和公共卫生的交界处，以从这两个角度更好地了解疾病传播动力学。对地理区域的病毒位置进行有效的模拟分析和预测，不仅有助于优化医疗保健政策的设计，以控制传染病的传播，还有助于保护美国人口，使卫生系统对破坏性事件更具弹性。这个快速项目将利用数据分析和模拟模型，更好地了解病毒传播的动态。本研究项目的目标是开发空间网络中人员移动/交通和病毒传播动力学的连续流仿真建模和分析。具体地说，我们将调查卫生政策的制定和传染病控制，以便医疗系统能够对破坏性事件做出快速和有效的反应。拟议的项目将研究对理解公共干预措施对病毒传播的影响至关重要的三组与政策有关的特征，即提供保健服务的区域基础设施、减缓病毒传播的监管措施以及信息透明度的有效性。由于传染病的动态经常随着时间的推移而变化，因此该项目的模拟模型和分析算法有助于实时支持决策。建议的方法一般适用于广泛的传染病。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this RAPID proposal is to to inform policy associated with pandemic viruses, such as coronavirus disease 2019 (COVID-19), as they significantly impact the national health and economy. To safeguard society from viruses, each country/state/locality needs to dynamically adjust health policies, plan near-term health care capacity, and control population movement with little time latency. Accurate real-time prediction of virus spread is essential for making the health system respond in a fast and proactive manner to disease variations and disruption events (e.g., staffing and supply shortage). This project pursues fundamental research to develop simulation models of human movement and virus spread dynamics, prediction of real-time positions of infected population in the spato-geographic network, and development of decision support tools for the design of healthcare policies under disruptive events and processes. The proposed research is at the interface of engineering and public health to gain a better understanding of disease spread dynamics from both perspectives. Effective simulation analysis and prediction of virus positions in geographic regions will not only help optimize the design of healthcare policies to control the propagation of infectious diseases, but also help safeguard the US population and make health systems more resilient to disruptive events.This RAPID project will leverage data analytics and simulation models to gain a better understanding of virus spreading dynamics. The objective of this research project is to develop continuous flow simulation modeling and analysis of human movement/traffic and virus spread dynamics in spatial networks. Specifically, we will investigate the derivation of health policies and infectious disease control so that the healthcare system can respond expeditiously and effectively to disruptive events. The proposed project will study three sets of policy-relevant characteristics that are central to the understanding of the impact of public interventions on virus spread, namely regional infrastructure of health care delivery, regulatory measures to slow down the virus spread, and effectiveness of information transparency. As the dynamics of infectious diseases often change over time, simulation models and analytical algorithms from this project help support decision-making in real time. The proposed methodology is generally applicable to a wide range of infectious diseases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.