EAGER: IMPRESS-U: Modeling and Forecasting of Infection Spread in War and Post War Settings Using Epidemiological, Behavioral and Genomic Surveillance Data

EAGER：IMPRESS-U：使用流行病学、行为和基因组监测数据对战争和战后环境中的感染传播进行建模和预测

• 批准号: 2412914
• 负责人: Alexander Kirpich
• 金额: $ 29.86万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412914&HistoricalAwards=false
• 关键词: EAGER; IMPRESS; Modeling; Forecasting; Infection; EAGER; IMPRESS; Modeling; Forecasting; Infection

This IMPRESS-U project is jointly funded by NSF, National Science Center of Poland (NCN), US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites the Georgia State University (US), Kharkiv National Medical University and Kharkiv Oblast Center for Diseases Control and Prevention of the Ministry of Health of Ukraine (Ukraine), andLodz University of Technology (Poland). US portion of the collaborative effort will be co-funded by NSF OISE/OD and MPS/DMS (Mathematical Biology and Computational Mathematics programs).The goal of the project is to develop computational models and algorithms for analyzing epidemiological dynamics under conflict and post-conflict scenarios. The proposed approach harnesses the combined strengths of computational biology, mathematical epidemiology, statistics, and machine learning, aligning with modern trends of incorporating human behavior into epidemiological models. The primary goal is to develop epidemiological models that encompass the diverse biological and epidemiological factors of pre-conflict, active conflict, and post-conflict stages. These factors include (a) the dynamics of forced population movements and migrations; (b) population concentrations, particularly in high-density refuges such as shelters and refugee camps; (c) the robustness and expanse of supply networks, with an emphasis on medical provisions; (d) disruptions to healthcare services and infrastructure, including the deliberate targeting of medical establishments as a wartime tactic; (e) prevailing environmental determinants, inclusive of sanitation and water accessibility; and (f) wartime psychological ramifications, which can impact community behaviors, resilience, and compliance with health interventions. The scientific results of the project will provide a unified modeling approach to study and predict epidemiological dynamics under various catastrophic events and develop methods for the subsequent decision making. The resulted integrated modeling framework and software will aid faster resources allocation during conflict which will mitigate pandemics, save social resources and lives of individuals involved. The project includes multiple interconnected aims. Aim 1. Developing Epidemiological Models Tailored to Conflict Zones where the focus will be the seamless integration of multi-faceted data sources. These will include surveillance records, demographic metrics, population density data, insights on health infrastructure and medical resources, environmental determinants, seasonal variations, and comprehensive psychological profiles outlining war-related psychological distress and trauma. Notably, the strategy will also introduce several innovative fractal methodologies tailored explicitly for epidemic contexts, all nested within the global modeling framework. Aim 2. Merging War-centric Epidemiological Models with Population Genetics Within a Phylodynamics Framework where the objective is to link the modeling framework conceptualized in Aim 1 with population genetics models that capture evolutionary trajectories of emergent viruses. This will be nested within a structured phylodynamics and phylogeographic framework. The approach will be one of the pioneering efforts towards development of host behavior-based phylodynamic models. Aim 3. Algorithms for Optimized Public Health Resource Location-Allocation based on merged war-centric epidemiological models will employ a multi-criterion optimization to support decision making for healthcare resource allocation in war and post-war settings. For example, due to hospital damages, the immediate and ongoing necessity is to geographically allocate portable hospitals in ways that provide maximum coverage and the highest availability for the population.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.

这个印象-U项目由NSF，波兰国家科学中心（NCN），美国国家科学院和海军研究办公室（DOD）共同资助。这项研究将在多边国际合作伙伴关系中进行，该伙伴关系将佐治亚州立大学，哈尔基夫国家医科大学和哈尔基夫州医学院疾病控制与预防乌克兰卫生部（乌克兰）（乌克兰）和洛兹大学（Lodz University of Technology）（波兰）（波兰）。合作工作的美国部分将由NSF OISE/OD和MPS/DMS（数学生物学和计算数学计划）共同资助。该项目的目的是开发计算模型和算法，用于分析在冲突和综合后现场下的流行性动态。提出的方法利用了计算生物学，数学流行病学，统计学和机器学习的综合优势，与将人类行为纳入流行病学模型的现代趋势保持一致。主要目标是开发涵盖冲突前，主动冲突和冲突后阶段的潜水员生物学和流行病学因素的流行病学模型。这些因素包括（a）强迫人口运动和迁移的动态； （b）人口浓度，特别是在避难所和难民营等高密度避难所中； （c）供应网络的鲁棒性和扩展，重点是医疗规定； （d）对医疗服务和基础设施的中断，包括故意将医疗机构作为战术策略； （e）盛行的环境决定者，包括卫生和水的可及性； （f）战时心理影响，可能会影响社区行为，韧性和遵守健康干预措施。该项目的科学结果将提供一种统一的建模方法来研究和预测各种灾难性事件下的流行病学动态，并为后续决策开发方法。所得的集成建模框架和软件将在冲突期间有助于更快的资源分配，这将减轻大流行，拯救社会资源和所涉及的个人的生活。该项目包括多个相互联系的目标。 AIM 1。开发针对冲突区域的流行病学模型，其中重点将是多面数据源的无缝集成。这些将包括监视记录，人口指标，人口密度数据，有关健康基础设施和医疗资源的见解，环境决定者，季节性变化以及概述与战争相关的心理困扰和创伤的全面心理概况。值得注意的是，该策略还将针对流行病环境明确量身定制的几种创新分形方法，这些方法都嵌套在全球建模框架内。目标2。将以战争为中心的流行病学模型与人群遗传学合并在系统动力学框架内，其目的是将AIM 1中概念化的建模框架与捕获新出现病毒进化轨迹的模型的建模框架联系起来。这将嵌套在结构化的系统动力学和植物地理框架中。该方法将是开发基于宿主行为的系统动力学模型的开创性努力之一。目标3。基于以战争为中心的流行病学模型优化公共卫生资源位置分配的算法将采用多准则优化，以支持战争和战后环境中医疗保健资源分配的决策。例如，由于医院的损害，直接和持续的必要是在地理位置上分配便携式医院，以提供最大的覆盖范围和人口最高可用性的方式。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来评估的珍贵支持。