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: 10399134
• 负责人: ALISON P GALVANI
• 金额: $ 11.45万
• 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/10399134
• 关键词: Age; Antiviral Agents; Area; COVID-19; COVID-19 morbidity; COVID-19 mortality; COVID-19 risk; 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; data-driven model; 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.

抽象的 自2020年1月初以来，我们的跨学科研究团队已经进行了几项研究，以阐明 新兴的COVID-19威胁并支持美国的公共卫生响应，从而 经过同行评审的出版物，在线Covid-19预测工具以及与城市，州和州和州的广泛参与 国家决策者。在我们与CDC合作以开发国家建模资源 大流行准备，我们最近开发了一种用于评估多层干预措施的国家模型 遏制和减轻美国城市暴发的策略。我们通过合并将模型调整为COVID-19 对年龄和风险组的最新估计，疾病进展，无症状的传播率 感染和严重程度（包括住院，重症监护，通风和死亡的风险）。该模型是 旨在灵活地结合社会距离的组合，接触示踪性隔离，抗病毒预防 和治疗以及疫苗接种策略。 我们的补充目的建议建立更精细的和数据驱动的Covid-19，以阐明 传输，确定高风险人群，监视目标以及对此和未来的有效控制 美国城市的流行病。 AIM S1：最初专注于德克萨斯州的奥斯丁摇滚乐大都会地区 我们将应用这些模型来改善实时风险评估并优化分层的时间和程度 社交隔离措施。 AIM S2：我们将迅速评估推出抗病毒预防和 基于临床试验数据的治疗。 AIM 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努力减轻Covid-19并计划未来的大流行 威胁。这些目标与我们的父母赠款的特定目标2（R01 AI151176-01）是协同作用的，我们在其中 开发病毒传播的高分辨率模型，以改善对的早期检测和控制 异常呼吸道病毒，特别是在风险中。