Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data

使用机械模型、机器学习和多样化地理空间数据加速美国城市的病毒爆发检测

• 批准号: 10265769
• 负责人: ALISON P GALVANI
• 金额: $ 48.23万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-07 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10265769
• 关键词: Age; Antiviral Agents; Area; COVID-19; COVID-19 morbidity; COVID-19 mortality; Categories; Centers for Disease Control and Prevention (U.S.); Cessation of life; Cities; Clinical Trials; Collaborations; Contact Tracing; Critical Care; Data; Decision Making; Detection; Development; Disease Outbreaks; Disease Progression; Dose; Early Diagnosis; Economics; Epidemic; Epidemiology; Funding; Future; Health Care Costs; Hospitalization; Human; Individual; Infection; Influenza; Interdisciplinary Study; Intervention; Leadership; Machine Learning; Measures; Methods; Modeling; Neighborhoods; Peer Review; Pharmacologic Substance; Policy Maker; Population Surveillance; Populations at Risk; Prophylactic treatment; Public Health; Publications; Readiness; Research; Research Support; Resolution; Resources; Risk; Risk Assessment; SARS-CoV-2 antiviral; SARS-CoV-2 transmission; Science; Scientist; Severities; Social Distance; Testing; Texas; Time; United States; Update; Vaccines; Viral; Visualization; austin; base; cost; design; evidence base; flexibility; high risk; high risk population; improved; metropolitan; multi-component intervention; pandemic disease; pandemic preparedness; parent grant; prevent; respiratory virus; response; risk prediction; social; socioeconomics; tool; transmission process; user-friendly; vaccination strategy; ventilation; viral transmission

ABSTRACT Since early January 2020, our interdisciplinary research team has conducted several studies to elucidate the emerging threat of COVID-19 and support public health responses throughout the United States, resulting in peer-reviewed publications, online COVID-19 forecasting tools, and extensive engagement with city, state and national decision makers. In our collaboration with the CDC to develop a national modeling resource for pandemic preparedness, we had recently developed a national model for evaluating multi-layered intervention strategies to contain and mitigate outbreaks in US cities. We adapted the model to COVID-19 by incorporating the latest estimates for age- and risk-group specific rates of transmission, disease progression, asymptomatic infections, and severity (including risks of hospitalization, critical care, ventilation and death). The model is designed to flexibly incorporate combinations of social distancing, contact tracing-isolation, antiviral prophylaxis and treatment, as well as vaccination strategies. Our Supplementary Aims propose to build a more granular and data-driven model of COVID-19 to elucidate the transmission, identify high-risk populations, surveillance targets and effective control of this and future epidemics within US cities. Aim S1: Focusing initially on the Austin-Round Rock metropolitan area in Texas, we will apply these models to improve real-time risk assessments and optimize the timing and extent of layered social distancing measures. Aim S2: We will rapidly evaluate strategies for rolling out antiviral prophylaxis and therapy based on clinical trial data. Aim S3: We will develop user interfaces for our Austin and national models to support both scientific research and public health efforts to mitigate COVID-19 and plan for future pandemic threats. These Aims are synergistic with Specific Aim 2 of our parent grant (R01 AI151176-01), in which we are developing high-resolution models of viral transmission to improve the early detection and control of anomalous respiratory viruses, particularly in at risk populations.

摘要 自2020年1月初以来，我们的跨学科研究团队进行了多项研究，以阐明 新出现的COVID-19威胁，并支持美国各地的公共卫生应对措施， 同行评审的出版物，在线COVID-19预测工具，以及与城市，州和 国家决策者。在我们与疾病预防控制中心合作开发国家建模资源， 为了做好大流行病的准备，我们最近开发了一个评估多层次干预措施的国家模型 控制和缓解美国城市疫情的战略。我们将模型调整为COVID-19， 年龄和风险组特定传播率、疾病进展、无症状 感染和严重程度（包括住院、重症监护、通气和死亡的风险）。该模型 旨在灵活地结合社交距离、接触者追踪-隔离、抗病毒预防 和治疗，以及疫苗接种策略。 我们的补充目标建议建立一个更精细和数据驱动的COVID-19模型，以阐明 传播，确定高危人群，监测目标和有效控制这一和未来 在美国城市流行。目标S1：最初集中在得克萨斯州的奥斯汀-朗德罗克大都市区， 我们将应用这些模型来改进实时风险评估，并优化分层评估的时间和范围。 社会距离措施。目标S2：我们将快速评估开展抗病毒预防的策略， 基于临床试验数据的治疗。目标S3：我们将为奥斯汀和国家模型开发用户界面 支持科学研究和公共卫生工作，以缓解COVID-19并为未来的大流行做好规划 威胁这些目标与我们的母基金（R 01 AI 151176 -01）的具体目标2具有协同作用， 开发高分辨率的病毒传播模型，以改善早期发现和控制 异常呼吸道病毒，特别是在高危人群中。