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

这个IMPRESS-U项目由NSF、波兰国家科学中心(NCN)、美国国家科学院和全球海军研究办公室(DoD)联合资助。这项研究将在一个多边国际伙伴关系中进行，该伙伴关系由美国佐治亚州立大学、哈尔科夫国立医科大学和乌克兰卫生部哈尔科夫疾病控制与预防中心以及波兰罗兹理工大学组成。美国部分的合作工作将由NSF OISE/OD和MPS/DMS(数学生物学和计算数学计划)共同资助。该项目的目标是开发计算模型和算法，用于分析冲突和冲突后情景下的流行病学动态。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组合优势，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、活跃冲突和冲突后阶段的不同生物学和流行病学因素的流行病学模型。这些因素包括(A)人口被迫流动和移徙的动态;(B)人口集中，特别是在庇护所和难民营等高密度避难所;(C)供应网络的坚固和庞大，重点是医疗供应;(D)破坏保健服务和基础设施，包括故意以医疗机构为目标作为战时战术;(E)普遍的环境决定因素，包括卫生设施和水的可获得性;和(F)战时心理分叉，可影响社区的行为、复原力和遵守保健干预措施。该项目的科学成果将为研究和预测各种灾难性事件下的流行病学动态提供统一的建模方法，并为后续的决策制定方法。由此产生的综合建模框架和软件将有助于在冲突期间更快地分配资源，这将缓解流行病，节省社会资源和有关个人的生命。该项目包括多个相互关联的目标。目标1.为冲突地区量身定做流行病学模型，重点将是多方面数据来源的无缝整合。这些将包括监测记录、人口指标、人口密度数据、对卫生基础设施和医疗资源的洞察、环境决定因素、季节变化，以及概述与战争有关的心理痛苦和创伤的全面心理概况。值得注意的是，该战略还将引入明确为流行病背景量身定做的几种创新的分形方法，所有这些方法都嵌套在全球建模框架内。目的2.将以战争为中心的流行病学模型与种群遗传学结合在一个系统动力学框架内，目标是将目标1中概念化的建模框架与捕捉新发病毒进化轨迹的种群遗传学模型联系起来。这将嵌套在一个结构化的系统动力学和系统地理框架内。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。目的3.基于合并的以战争为中心的流行病学模型的公共卫生资源优化配置算法将采用多准则优化，以支持战争和战后环境下的卫生资源配置决策。例如，由于医院受损，迫切和持续的需要是在地理上分配便携式医院，以提供最大的覆盖范围和最高的人口可用性。这一奖励反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.