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).

项目摘要（摘要） 接触网络流行病学是一个引人入胜的流行病学框架，旨在建模动态互动 在社交网络上的人们，以跟踪感染级联，尤其是用于传播的疾病。 联系网络流行病学中的基于网络的模拟可以在人们的属性和 行为（例如年龄，种族/种族，戴着面膜），它们的相互作用模式（例如 分类性）和社会结构（例如社会规范和政策，包括非药物干预措施 [npis]）。尽管获得精确网络数据是挑战，但它可以指导我们确定潜在的工作 网络干预策略，这可能证明有益于解决COVID-19-19的大流行。 使用网络干预的框架，一项试点模拟研究提出了针对 在家中持续待定的秩序，在人们的社会经济活动中减轻传播 （Nishi等，2020，PNA）。在最有效的分裂 +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所学校的学生）随机分为两个子组 相等的数字以减少物理接触的数量。如果以空间方式进行操作，请额外空间 对于亚组准备；如果以临时方式进行操作，则两个子组将参与他们的 在不同工作时间的活动。因此，该策略将允许人们参与相同的 经济活动的幅度。提议的策略的优势在于它不需要实际网络 数据，在大多数情况下很难获得。 在试点研究之后，该研究旨在创建其他新型的NPI策略，以控制感染性疾病控制 （目标既是COVID-19和其他新兴疾病）（AIM 1）。这项研究也旨在创造小说 疫苗分配的网络干预策略（AIM 2）。提出的减轻流行病的策略 并且优化疫苗分配原则上不需要实际的网络数据。因此，他们的潜力 效果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。利用的社交网络将基于 10,000个人（Nishi等人，2020年，PNA）和各种网络结构，可公开使用（使用 辅助数据）。此外，这项研究将分析预警信号（EWS）的作用， 在传染病控制环境中在非线性动态系统中开发。我打算使用76 加利福尼亚县Covid-19数据（AIM 3）。