Anticipating and rapidly responding to respiratory virus outbreaks with continuous air sampling in K-12 schools

通过 K-12 学校的连续空气采样来预测和快速应对呼吸道病毒爆发

• 批准号: 10658581
• 负责人: David H. O'Connor
• 金额: $ 78.87万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-18 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658581
• 关键词: 2019-nCoV; Absenteeism; Age; Air; Air Movements; Awareness; Behavior Therapy; COVID-19; COVID-19 detection; COVID-19 diagnostic; COVID-19 pandemic; COVID-19 surveillance; COVID-19 testing; Censuses; Child; Collection; Communities; Consumption; County; Data; Detection; Diagnostic tests; Disease; Disease Outbreaks; Fatigue; Foundations; Future; Household; Illness impact; Individual; Infection; Influenza; Laboratories; Learning; Location; Masks; Measures; Modeling; Oregon; Pattern; Persons; Proxy; Rapid diagnostics; Research Personnel; Respiratory Disease; Risk; SARS-CoV-2 infection; Schools; Seasons; Sentinel; Sentinel Surveillance; Site; Students; Surveillance Program; Testing; Time; United States; Variant; Viral; Virus; Wisconsin; air sampler; air sampling; commune; community transmission; high risk; improved; infection risk; influenza infection; influenzavirus; innovation; programs; promote resilience; respiratory; respiratory pathogen; respiratory virus; risk mitigation; risk prediction; school district; surveillance strategy; teacher; tool; transmission process; viral detection; viral transmission; wastewater samples

Project summary/abstract COVID-19 revealed weaknesses in respiratory pathogen surveillance. Tools to detect SARS-CoV-2 were initially limited. Now that SARS-CoV-2 is prevalent, schools and other congregate settings struggle to perform systematic surveillance. As influenza virus and other respiratory pathogens return to co-exist alongside SARS-CoV-2, pandemic fatigue undermines risk mitigation strategies that rely on behavior modification, creating an unprecedented risk of illness-caused absenteeism in schools. We hypothesize that frequent air sampling in K-12 schools is a simple, inexpensive, and accurate sur- veillance strategy for identifying when schools and the communities where they are located are at high risk for SARS-CoV-2 and influenza virus transmission. In this study, we will place an air sampler inside each of 16 K-12 schools and collect continuous air sam- ples twice weekly throughout the school year. We will test air samples for SARS-CoV-2 and influenza vi- rus, the two leading causes of respiratory virus absenteeism. A key question is whether detecting these viruses in air samples is non-inferior to comprehensive individual testing of people with influenza-like illnesses. We are partnering with the Oregon (Wisconsin) School District which already performs in- school rapid diagnostic testing of symptomatic individuals. Within this unique setting, we can determine if the detection rates of SARS-CoV-2 and influenza are similar between air surveillance and diagnostic testing of individuals. If they are similar, this would suggest that simple, inexpensive, and anonymous air sampling programs could be the foundation of school-based sentinel virus surveillance programs. We will also determine whether detecting and sequencing SARS-CoV-2 in air samples forecasts broader community disease spread. Using SARS-CoV-2 diagnostic testing, wastewater, environmental, and mo- bility data alongside in-school air sampler detection and sequencing data, we will develop and iterate a model using air sampler data to forecast community transmission. We will extend this model to forecast the risk for influenza viruses that are not currently surveilled systematically. Successfully developing precision air sampler surveillance for SARS-CoV-2 and influenza virus in a large public school setting would provide a model for a nationwide surveillance program. Such a program would lead to safer classrooms, improve awareness of respiratory viruses that threaten specific commu- nities, and increase resilience to respiratory pathogens that threaten schools in the future.

项目概要/摘要 COVID-19 暴露了呼吸道病原体监测的弱点。检测 SARS-CoV-2 的工具包括 最初有限。现在 SARS-CoV-2 流行，学校和其他聚集场所很难 进行系统监测。随着流感病毒和其他呼吸道病原体重新共存 与 SARS-CoV-2 一样，大流行疲劳破坏了依赖行为的风险缓解策略 修改，造成学校因病缺勤的前所未有的风险。 我们假设 K-12 学校频繁进行空气采样是一种简单、廉价且准确的补充方法。 用于识别学校及其所在社区何时处于高水平的监视策略 SARS-CoV-2 和流感病毒传播的风险。 在这项研究中，我们将在 16 所 K-12 学校的每所学校内放置一个空气采样器，并收集连续的空气样本 整个学年每周两次。我们将测试空气样本中的 SARS-CoV-2 和流感病毒 - rus，呼吸道病毒缺勤的两个主要原因。一个关键问题是是否能够检测到这些 空气样本中病毒的检测并不逊色于对流感样患者进行全面的个体检测 疾病。我们正在与俄勒冈州（威斯康星州）学区合作，该学区已经在- 学校对有症状的个体进行快速诊断检测。在这个独特的环境中，我们可以确定 空气监测和诊断之间 SARS-CoV-2 和流感的检出率是否相似 对个人的测试。如果它们相似，则表明简单、廉价且匿名 空气采样计划可以成为基于学校的哨兵病毒监测计划的基础。 我们还将确定空气样本中 SARS-CoV-2 的检测和测序是否可以更广泛地预测 社区疾病传播。使用 SARS-CoV-2 诊断测试、废水、环境和空气 能力数据以及校内空气采样器检测和测序数据，我们将开发和迭代 使用空气采样器数据来预测社区传播的模型。我们将扩展这个模型来预测 目前尚未系统监测的流感病毒的风险。 成功开发大规模监测 SARS-CoV-2 和流感病毒的精密空气采样器 公立学校环境将为全国范围的监控计划提供一个模型。这样的节目 将带来更安全的教室，提高对威胁特定社区的呼吸道病毒的认识 并提高对未来威胁学校的呼吸道病原体的抵御能力。