Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models

当人们改变旅行的地点、时间和原因时，就会对传染病产生影响。人们的流动决定了谁有感染这种疾病的风险，以及当地公共卫生机构是否会统计新病例。例如，在2019冠状病毒病大流行期间，人们的流动发生了巨大变化，除了2019冠状病毒病，全国范围内的流感和莱姆病病例也有所下降。病例数的下降可能是因为人们在高风险地区停留的时间减少了，或者仅仅是因为人们去医疗机构的次数减少了，因此报告的病例减少了。区分这些选择对于了解疾病控制和预测疾病传播至关重要，但当旅行模式发生巨大变化时，就变得困难了。这一问题尤其具有挑战性，因为社区可能会根据当地疾病改变旅行模式，这反过来又会改变疾病在社区传播的方式以及公共卫生监测疾病的方式。为了确定随着人类活动发生变化而导致病例减少的原因，调查人员将开发新的数学模型，解释旅行对风险和发现的影响方式，利用移动电话数据通报传播风险，并利用当地调查通报未发现率。通过开发这一新的模型集合，研究人员将更好地了解各种非covid -19感染的传播和检测在整个大流行期间是如何变化的，认识到这如何取决于所考虑的疾病的生物学特性，并预测在未来社区层面旅行发生重大变化的时期，病例数可能会如何变化。社区一级的旅行模式对任何传染病的动态都有多因素影响。旅行模式的重大变化既会影响传播，因为人们在高风险地方停留的时间或多或少，也会影响检测，因为人们改变了前往医疗机构的倾向。这些因素也影响个人行为，因为当地报告病例的增加可能导致人们进一步改变旅行方式。这在传播、检测和传播之间形成了至关重要的反馈回路。根据这些因素之间的相互作用，旅行或传播方式的变化可能导致少计病例或有害的人群层面应对，从而导致社区暴露于更多感染。由于气候变化和新出现的传染病威胁等全球因素更有可能改变社区一级的旅行模式，因此，将其对检测和传播的影响整合到模型中变得越来越重要。该项目通过开发新的模型来解决这一需求，这些模型解释了旅行可能同时影响传播和发现的方式，并受到报告和感知的疾病风险的影响。研究人员将把这些模型与从SafeGraph获得的流动性数据结合起来，并利用当地调查来了解弗吉尼亚州新河谷卫生区主要法定疾病的未检出率，并开发一个框架，用于预测未来大规模旅行变化期间的传播和检出率变化。阿巴拉契亚中部是这项工作的关键地区，因为该地区呼吸道疾病和莱姆病的发病率相对较高，而且在COVID-19大流行的后期阶段，干预措施的依从性特别低。该项目由数学和物理科学理事会（MPS）的数学科学部（DMS）和社会、行为和经济科学理事会（SBE）的社会和经济科学部（SES）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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.