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: 10449891
• 负责人: Akihiro Nishi
• 金额: $ 13.45万
• 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/10449891
• 关键词: 2019-nCoV; Address; Application procedure; Area Under Curve; Behavior; Behavioral Sciences; Blood Circulation; Businesses; COVID-19; COVID-19 pandemic; California; Case Fatality Rates; Cause of Death; Cessation of life; Cities; Communicable Diseases; Communities; Conflict (Psychology); Country; County; Crowding; Data; Development; Diagnosis; Disease; Disease Outbreaks; Early Diagnosis; Early Intervention; Economics; Elderly; Employee; Epidemic; Epidemiology; Equilibrium; 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; Public Health; Quarantine; RNA vaccine; Race; Reproduction; Research; Role; Sampling; Schools; Signal Transduction; Social Network; Social Policies; South Korea; Specific qualifier value; Structure; Students; Subgroup; Techniques; Time; Tuberculosis; Vaccinated; Vaccines; Variant; Workplace; base; disorder control; dynamic system; high risk; improved; infection risk; mathematical model; novel; novel vaccines; operation; pandemic preparedness; pathogen; physical conditioning; public health relevance; racial and ethnic; racial and ethnic disparities; simulation; social group; social norm; social structure; socioeconomics; sound; 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 人们通过社交网络追踪感染级联，特别是传染病。 接触网络流行病学中基于网络的模拟可以纳入人们属性的变化和 行为（例如年龄、种族/民族、戴口罩）、他们的互动模式（例如同质性或 相配性）和社会结构（例如社会规范和政策，包括非药物干预措施） [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。 利用网络干预框架，一项试点模拟研究提出了替代 NPI 策略 居家令，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+平衡群体策略中，一个社会群体（例如， 同一工作场所的员工和同一学校的学生）被随机分为两个小组 如果以空间方式操作，则需要额外的空间。 对于小组已准备好；如果以临时方式操作，则两个小组将进行各自的工作 因此，该策略将允许人们在不同的工作时间从事相同的活动。 所提出的策略的优点在于它不需要实际的网络。 数据，在大多数情况下很难获得。 继试点研究之后，本研究旨在创建其他新颖的 NPI 策略来控制传染病 （目标是 COVID-19 和其他新出现的疾病）（目标 1）。 疫苗分配的网络干预策略（目标 2）。 原则上，优化疫苗分配不需要实际的网络数据，因此它们的潜力很大。 需要使用基于网络的模拟和现实假设或使用其他方法来检查效果 方法，包括数学建模，所使用的社交网络将基于样本城市。 10,000 个人（Nishi 等人，2020，PNAS）以及公开可用的各种网络结构（使用 此外，本研究将分析早期预警信号（EWS）的作用。 我计划使用 76 在传染病控制领域的非线性动力系统中开发。 加利福尼亚县 COVID-19 数据（目标 3）。