RAPID: Measuring Uptake, Persistence, and Impact of Behavioral Interventions On Respiratory Viruses

这项研究调查了个体选择的行为变化导致人群之间疾病传播差异的原因，并将这些影响与自上而下的强制变化分开。当药物治疗(例如药物和疫苗)不可用时，使用自上而下的行为干预措施来减少病原体的传播。它们通常旨在最大限度地减少流动和接触，包括学校和工作场所的关闭或活动取消。此外，个人可能会选择暂时改变自己的行为，以降低感染的风险。这种个体变化很难预测或衡量，这使得衡量它们在减少疾病发病率和传播方面的有效性具有挑战性。SARS-CoV-2的出现引发了广泛的县级限制，以及个人行为的改变，比如呆在家里和戴口罩。尽管经历了同样的县级限制，大学生的人均新冠肺炎患病率非常高，而非学生人均报告的新冠肺炎病例很少。相比之下，在新冠肺炎的第一个冬天，两个人群都没有经历季节性流感。通过比较这些人群的行为和疾病发病率，这项研究将有助于规划和设计更有效和更高效的暴发管理策略，以应对重新出现的或新出现的病原体。该项目支持对早期职业科学家的培训。这项研究调查了1)吸收，2)随时间的坚持，以及3)自上而下的行为干预和个体行为改变对新冠肺炎和流感发病率的影响。通过整合被动和主动数据收集，研究人员将量化宾夕法尼亚州中部行为的变化。将自上而下干预期间的行为变化与其他时间进行比较，有助于将强制行为变化与个人选择分开。纵向被动监测数据将为SARS-CoV-2出现之前和之后的人口水平流动提供频繁的测量。交通摄像头将量化车辆和行人的交通，而移动设备将提供对兴趣点的总访问量、外出时间、学生和非学生之间的混合等的替代测量。该项目还将对学生(N=684)和非学生(N=1313)进行纵向调查，以衡量个人对自上而下干预措施的遵守情况以及自愿接受其他行为改变的情况。调查将询问有关活动、呆在家里、聚会、蒙面、洗手、接种疫苗等问题。在每个时间步骤，调查参与者将提供血液进行血清学分析，以检测先前的感染情况。研究人员将把行为干预和个人行为的动态测量纳入疾病模型，以估计学生和非学生中新冠肺炎和流感感染力的动态变化。这些模型将测量特定行为对疾病传播的影响，并将为未来的干预提供信息。该项目是与疾控中心合作资助的，以支持快速反应研究项目，以进一步推进联邦传染病建模能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This study investigates the reasons for differences in disease transmission between populations as a result of behavioral changes chosen by individuals and separating those effects from top-down, mandated changes. Top-down behavioral interventions are used to reduce the transmission of pathogens when pharmaceutical treatments (e.g., drugs and vaccines) are not available. They are often designed to minimize movement and contacts and include school and workplace closures or event cancellations. In additionally, individuals may choose to temporarily change their behavior to reduce their risk of infection. Such individual changes are difficult to predict or measure, making it challenging to measure their effectiveness in reducing disease incidence and spread. The emergence of SARS-CoV-2 prompted widespread county-level restrictions alongside individual behavioral changes, such as staying at home and wearing masks. Despite experiencing the same county level restrictions, university students experienced very high rates of COVID-19 per capita, while non-students reported very few COVID-19 cases per capita. In contrast, neither population experienced seasonal influenza during the first winter of COVID-19. By comparing behaviors and disease incidence in these population, this research will help plan and design more effective and efficient outbreak management strategies for re-emerging or novel pathogens. This project supports training of early-career scientists. This study investigates the 1) uptake, 2) persistence over time, and 3) impact of top-down behavioral interventions and individual behavioral changes on COVID-19 and influenza incidence. By integrating passive and active data collection, researchers will quantify changes in behaviors in central Pennsylvania. Comparing behavioral changes during top-down interventions to other times helps disentangle mandated behavioral changes from individual choices. Longitudinal passive surveillance data will provide frequent measures of population-level movements before and after SARS-CoV-2 emerged. Traffic cameras will quantify vehicular and pedestrian traffic while mobile devices will provide proxy measures of total visits to points of interest, time spent outside the home, mixing between students and non-students, and so forth. This project also will longitudinally survey students (N=684) and non-students (N=1313) to measure individual adherence to top-down interventions and the voluntary adoption of other behavioral changes. Surveys will ask about movement, staying home, gatherings, masking, hand washing, vaccination, and so forth. At each time step, survey participants will provide blood for serological analyses to detect prior infection. Researchers will incorporate dynamic measures of behavioral interventions and individual behaviors into disease models to estimate the dynamics in the force of infection for COVID-19 and influenza in students and non-students. These models will measure the impacts of specific behaviors on disease transmission and will inform future interventions.This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.