A Visual Analytics and Multi-Objective Optimisation Approach for Balancing Economic and Public Health Objectives through Compartmental Models

通过区室模型平衡经济和公共卫生目标的可视化分析和多目标优化方法

• 批准号: EP/W01226X/1
• 负责人: Alma Rahat
• 金额: $ 23.57万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW01226X%2F1
• 关键词: Visual; Analytics; Multi; Objective; Optimisation

Modelling of disease spread continues to play a crucial role in the response to the COVID-19 pandemic. There is light at the end of the tunnel with effective vaccines, but it will take until the summer of 2021 for distribution to be widespread, and re-vaccination may be an ongoing requirement. In the meantime, hybrid solutions are required to manage non-pharmaceutical interventions (NPI), that minimise the restrictions to our daily lives while suppressing transmission and maintaining the integrity of our healthcare systems.Realistic models are publicly available to predict the spread of the virus. Varying the parameters of these models can be used to represent tentative policy actions, and the consequences are deduced in simulations of the model. Typically, the main objective for identifying effective policy actions has been to reduce the infection rate. However, often there are multiple, potentially conflicting, objectives that require optimisation in parallel. For instance, we may want policies that reduce the hospital occupancy, while simultaneouslyreducing economic impacts. Our goal is to provide a generic visual analytics framework to explore the parameter space of complex models as well as the trade-offs between objectives to inform policy makers. Specifically:1. A scalable visual analytics framework for parameter space exploration of feasible regions of the parameter space for complex compartmental models in order to identify effective policy actions.2. Extend this framework to handle multiple objectives: reduction of transmission to high risk groups, overall cases and deaths, hospital costs, thresholds for circuit breakers, and economic factors.

疾病传播模型在应对COVID-19疫情方面继续发挥关键作用。有效疫苗的隧道尽头有光明，但要到2021年夏天才能广泛分发，重新接种疫苗可能是一项持续的要求。与此同时，我们需要混合解决方案来管理非药物干预措施（NPI），以最大限度地减少对我们日常生活的限制，同时抑制传播并保持我们医疗系统的完整性。改变这些模型的参数可以用来代表试探性的政策行动，并在模型的模拟中推导出的后果。通常，确定有效政策行动的主要目标是降低感染率。然而，通常有多个潜在冲突的目标需要并行优化。例如，我们可能希望政策能够减少医院的占用率，同时不可避免地减少经济影响。我们的目标是提供一个通用的可视化分析框架，以探索复杂模型的参数空间以及目标之间的权衡，以告知政策制定者。具体而言：1.一个可扩展的可视化分析框架，用于复杂房室模型参数空间可行区域的参数空间探索，以确定有效的政策措施。扩展该框架以处理多个目标：减少向高危人群的传播、总体病例和死亡、医院成本、断路器阈值和经济因素。

项目成果

Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Dortmund, Germany, September 10-14, 2022, Proceedings, Part I

自然并行问题解决 - PPSN XVII - 第 17 届国际会议，PPSN 2022，德国多特蒙德，2022 年 9 月 10-14 日，会议记录，第一部分

• DOI: 10.1007/978-3-031-14714-2_7
• 发表时间: 2022
• 作者: Rahat A

Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic

解决大流行期间数据驱动决策中的健康与经济困境

• DOI: 10.1145/3583133.3590652
• 发表时间: 2023
• 作者: Hotchkiss L

Extrema Graphs: Fitness Landscape Analysis to the Extreme!

极值图：健身景观分析到极致！

• DOI: 10.1145/3583133.3596343
• 发表时间: 2023
• 作者: Sadler S