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.

当人们改变旅行地点、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该疾病的风险，以及当地公共卫生机构是否统计新病例，例如，在新冠肺炎 (COVID-19) 大流行期间。人们的活动发生了巨大变化，除了 COVID-19 之外，全国范围内的流感和莱姆病病例也有所下降，这些病例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前往医疗机构的频率减少了，因此报告的病例较少。区分这些替代方案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，这个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，这反过来又会改变出行方式。疾病在社区传播以及公共卫生如何监测疾病随着人类流动的变化而减少的原因，研究人员将开发新的数学模型，以解释旅行影响风险和检测的方式，使用手机数据来告知传播情况。风险并利用当地调查来告知检测不足的情况通过开发这组新模型，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如何变化，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。社区层面的旅行模式对任何传染病的动态都会产生多因素影响，因为人们在高风险中度过的时间或多或少，因此旅行模式的重大变化也会影响传播。地点和检测，因为人们改变了他们的倾向这些因素也会影响个人行为，因为当地报告病例的增加可能会导致人们进一步改变他们的出行方式，这在传播、检测和出行之间形成了至关重要的反馈循环，具体取决于这些因素之间的相互作用。由于气候变化和新出现的传染病威胁等全球因素，社区层面的旅行模式更有可能发生变化，因此，旅行或传播可能会导致病例计数不足，或导致社区受到更多感染。对于模型来说，整合其对检测和预测的影响变得越来越重要。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影响传播和检测的方式，以及受报告和感知的疾病风险影响的方式。研究人员将把这些模型与从 SafeGraph 和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漏检率，并制定一个框架来预测未来大规模旅行变化期间阿巴拉契亚中部的传播和检测变化，这是这项工作的关键区域。 ，因为它的发生率相对较高该项目由数学和物理科学局 (MPS) 下的数学科学部 (DMS) 和该司共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。