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

这项研究调查了由于个人选择的行为变化以及将这些影响与自上而下的强制性变化分开而导致人群之间疾病传播差异的原因。自上而下的行为干预用于减少药物治疗（例如，药物和疫苗）。它们通常旨在尽量减少移动和接触，包括学校和工作场所关闭或活动取消。此外，个人可以选择暂时改变他们的行为，以减少感染的风险。这种个别变化难以预测或衡量，因此难以衡量其在减少疾病发病率和传播方面的有效性。SARS-CoV-2的出现促使人们在县一级采取广泛的限制措施，同时改变个人行为，如呆在家里和戴口罩。尽管经历了同样的县级限制，但大学生的人均COVID-19发病率非常高，而非学生的人均COVID-19病例很少。相比之下，在COVID-19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）随时间的持续性，以及3）自上而下的行为干预和个人行为变化对COVID-19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的行为变化。将自上而下干预期间的行为变化与其他时间进行比较，有助于将强制性行为变化与个人选择分开。纵向被动监测数据将提供SARS-CoV-2出现前后人口水平流动的频繁测量。交通摄像头将量化车辆和行人的交通，而移动的设备将提供对兴趣点的总访问量、在户外花费的时间、学生和非学生之间的混合等的代理测量。该项目还将纵向调查学生（N=684）和非学生（N=1313），以衡量个人坚持自上而下的干预措施和自愿采取其他行为变化。调查将询问有关运动、呆在家里、聚会、戴口罩、洗手、接种疫苗等问题。在每个时间步，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。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COVID-19和流感感染力的动态。这些模型将衡量特定行为对疾病传播的影响，并为未来的干预提供信息。该项目与CDC合作资助，以支持快速反应研究项目，进一步提高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知识价值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.