Network Intervention Planning without Actual Network Data for Infectious Disease Control

没有实际网络数据的传染病控制网络干预规划

• 批准号: 10580083
• 负责人: Akihiro Nishi
• 金额: $ 13.48万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-25 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580083
• 关键词: 2019-nCoV; Address; Age; Agreement; Application procedure; Area Under Curve; Behavior; Behavioral Sciences; Binding; Businesses; COVID-19; COVID-19 pandemic; California; Case Fatality Rates; Cause of Death; Cessation of life; Circulation; Cities; Communicable Diseases; Communities; Country; County; Crowding; Data; Development; Diagnosis; Disease; Disease Outbreaks; Early Diagnosis; Early Intervention; Economics; Elderly; Employee; Epidemiology; Ethnic Origin; Event; Exhibits; Face; Failure; Fatigue; France; Friends; Friendships; Goals; Home; Hour; Household; Individual; Infection; Influenza; Intervention; Investigation; Japan; Knowledge; Location; Masks; Mathematics; Measures; Mental Health; Modeling; Network-based; Nonlinear Dynamics; Pattern; Persons; Pilot Projects; Play; Predisposition; Public Health; Quarantine; RNA vaccine; Race; Reproduction; Research; Role; Sampling; Schools; Signal Transduction; Social Distance; Social Network; Social Policies; South Korea; Specific qualifier value; Structure; Students; Subgroup; Techniques; Time; Tuberculosis; Vaccinated; Vaccines; Variant; Workplace; disorder control; dynamic system; epidemic response; ethnic disparity; high risk; improved; infection risk; mathematical model; novel; novel vaccines; operation; pandemic preparedness; pathogen; physical conditioning; public health relevance; racial disparity; simulation; social group; social norm; social structure; socioeconomics; sound; stay-at-home order; success; theories; transmission process; vaccine development; vaccine distribution; vaccine strategy

PROJECT SUMMARY (ABSTRACT) Contact network epidemiology is a compelling epidemiologic framework that aims to model dynamic interactions of people over their social networks in order to track infection cascades, especially for communicable diseases. Network-based simulations in contact network epidemiology can incorporate variations in people’s attributes and behaviors (e.g. age, race/ethnicity, wearing a facial mask), their interaction patterns (e.g. homophily or assortativity), and social structures (e.g. social norms and policies including non-pharmaceutical interventions [NPIs]). Although obtaining precise network data is challenging, it can guide us to identify potential working network intervention strategies, which may prove beneficial in addressing the COVID-19 pandemic. Using the framework of network interventions, a pilot simulation study proposed alternative NPI strategies to the stay-at-home order, in which transmission is mitigated while people’s socioeconomic activities are sustained (Nishi et al, 2020, PNAS). In the most effective dividing + balancing groups strategy, a social group (e.g. employees of the same workplace and students of the same school) is divided randomly into two subgroups with an equal number to reduce the number of physical contacts. If it is operated in a spatial manner, additional space for the subgroups is prepared; if it is operated in a temporal manner, the two subgroups will engage in their activities during different business hours. Therefore, the strategy would allow people to engage in the same magnitude of economic activities. The strength of the proposed strategy is that it does not require actual network data, which is difficult to obtain in most cases. Following the pilot study, this research seeks to create other novel NPI strategies for infectious disease control (the targets are both COVID-19 and other emerging diseases) (Aim 1). This research also seeks to create novel network intervention strategies for vaccine allocation (Aim 2). The proposed strategies for mitigating an epidemic and optimizing vaccine allocation will not, in principle, require actual network data. Therefore, their potential effect needs to be examined using network-based simulations with realistic assumptions or using other approaches, including mathematical modeling. The utilized social network will be based on a sample city of 10,000 individuals (Nishi et al, 2020, PNAS) and various network structures that are publicly available (the use of secondary data). Moreover, this research will analyze the role of early warning signals (EWS), which has been developed in non-linear dynamical systems in the infectious disease control context. I plan to use the 76 California County COVID-19 data (Aim 3).

项目概要（摘要） 接触网络流行病学是一个引人注目的流行病学框架，旨在模拟动态相互作用 人们通过他们的社交网络来追踪感染级联，特别是传染病。 接触网络流行病学中基于网络的模拟可以结合人的属性的变化， 行为（例如年龄、种族/民族、戴口罩）、他们的互动模式（例如同性恋或 社会结构（如社会规范和政策，包括非药物干预） [NPI]）。虽然获得精确的网络数据具有挑战性，但它可以指导我们识别潜在的工作 网络干预策略，这可能有助于应对COVID-19大流行。 利用网络干预的框架，一项试点模拟研究提出了替代NPI战略， 居家秩序，在人们的社会经济活动得以维持的同时，传播得到缓解 （Nishi等人，2020，PNAS）。在最有效的划分+平衡群体的策略中，一个社会群体（例如， 同一工作地点的雇员及同一学校的学生）随机分为两个小组， 以减少物理接触的数量。如果以空间方式操作， 对于子组是准备好的;如果它是在一个时间的方式操作，两个子组将从事他们的 不同营业时间的活动。因此，该战略将允许人们从事同样的工作， 经济活动的规模。所提出的策略的优势在于它不需要实际的网络 数据，这在大多数情况下是很难获得的。 在试点研究之后，本研究旨在为传染病控制创造其他新的NPI策略 (the新冠肺炎及其他新出现的疾病）（目标1）。这项研究还试图创造新的 疫苗分配的网络干预战略（目标2）。拟议的减轻流行病的战略 优化疫苗分配原则上不需要实际的网络数据。因此，其潜力 需要使用具有现实假设的基于网络的模拟或使用其他 方法，包括数学建模。使用的社交网络将基于一个样本城市， 10，000个个体（Nishi等人，2020，PNAS）和公开可用的各种网络结构（使用 次要数据）。此外，本研究将分析预警信号（EWS）的作用， 在传染病控制背景下的非线性动力系统中开发。我打算用76 加州县COVID-19数据（Aim 3）。