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

合作研究：IHBEM：这里的恐惧：将基于地点的旅行行为和检测整合到新型传染病模型中

• 批准号: 2327797
• 负责人: Nick Ruktanonchai
• 金额: $ 62.98万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327797&HistoricalAwards=false
• 关键词: Collaborative; Research; IHBEM; fear; here

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.

当人们改变何时，何时和为什么旅行时，会对感染疾病产生影响。人们的运动决定了谁有疾病的风险，以及当地公共卫生机构是否计算了新病例。例如，在COVID-19大流行期间，人们的运动发生了巨大变化，除了Covid-19，影响力和莱姆病病例在全国范围内也下降了。这些情况下的下降可能是因为人们在高风险领域的花费较少，或者仅仅是因为人们前往医疗机构的频率较低，因此报告的情况很少。区分这些替代方案对于理解疾病控制和预测疾病扩散至关重要，但是当旅行模式发生巨大变化时，很难。这个问题尤其具有挑战性，因为社区可能会根据当地疾病来改变旅行模式，从而改变社区中疾病的传播方式以及公共卫生如何监测疾病。为了确定案件减少的原因，随着人类运动的变化，研究人员将开发新的数学模型，以说明旅行影响风险和检测的方式，并使用手机数据来指导传输风险并使用本地调查来告知理解率。通过开发新的模型集合，研究人员将更好地了解整个大流行期间各种非旋转19感染的传播和检测如何发生变化，认识到这如何取决于所考虑的疾病的生物学，并预测病例数在未来的社区级别的重大变化期间如何变化。旅行级别的旅行模式对任何感染性疾病的多种影响会影响任何感染性疾病。旅行模式的重大变化会影响传播，因为人们在高风险的地方花费或多或少地花费时间，并且随着人们改变访问医疗保健设施的诺言而检测。这些因素也会影响个人行为，因为报告案例的局部增加会导致人们进一步改变旅行。这会在传输，检测和旅行之间产生至关重要的反馈回路。根据这些因素之间的相互作用，旅行或传播的变化可能导致案件降低案件或有害的人群水平的反应，从而导致社区受到更多感染。随着社区水平的旅行模式的变化变得更有可能随气候变化和新兴感染威胁等全球因素而变得越来越可能，对于模型来说，将其对检测和传播的影响整合在一起变得越来越重要。该项目通过开发新型模型来解决这一需求，这些模型可以说明旅行很容易影响传播和检测的方式，并受到报告和感知的疾病风险的影响。研究人员将将模型与从Safegraph获得的移动性数据相结合，并使用当地调查为弗吉尼亚州新河谷健康区关键通知疾病的检测率不足，并为未来大规模旅行变化期间预测传播和检测变化的框架开发一个框架。阿巴拉契亚中部是这项工作的关键区域，因为它经历了呼吸道和莱姆病的相对较高的事件，并且在Covid-19-19大流行期的后期，干预依从性尤其低。该项目是由数学科学（DMS）在数学和身体科学局（MPS）（MPS）和社会和经济科学（SES）的部门共同资助的。社会，行为和经济科学局（SBE）（SBE）（SBE）授予了NSF的法规宣教士，并审查了范围的范围。