Computationally Efficient Methods for Control of Epidemics on Networks

控制网络流行病的计算有效方法

• 批准号: 2240848
• 负责人: Alexander Olshevsky
• 金额: $ 35.24万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240848&HistoricalAwards=false
• 关键词: Computationally; Efficient; Methods; Control; Epidemics

The COVID-19 epidemic has brought the difficult tradeoffs of epidemic control to the forefront of public attention. While epidemics can be mitigated through a variety of interventions such as lockdowns, travel restrictions, mandated social distancing, and vaccine allocations, many of these methods can be extraordinarily costly, both in terms of creating hardship and unemployment as well as in terms of reducing access to crucial services for vulnerable populations. Furthermore, popular backlash against harsh epidemic control interventions can make their long term sustainability impossible. This project will address the problem of designing such interventions optimally. The goal will be to design a mix of interventions such as those mentioned above to achieve a target rate for how fast an epidemic should decay while imposing the least hardship upon society at large. This project will develop interventions that are both spatially heterogeneous and coordinated among different locations. Optimal epidemic control strategies will be obtained by treating different locations differently, based both on the number of cases at each location as well as the geographic importance of each location for future epidemic spread. The methods developed for this purpose will be robust across different epidemic models and likely applicable to future pandemics, which may not share key features of COVID-19. The technical approach will take into account uncertainty in disease parameters, which can vary not only depending on location but are constantly evolving in time depending on interventions and human behavior. Finally, solutions will also be developed that respect certain fairness constraints, such as ensuring that locations with fewer cases do not face harsher lockdowns, which is a counter-intuitive feature of some optimal lockdowns. The newly developed methods will be guaranteed to work in a number of operations that is polynomial, and often linear, in the size of the spatial epidemic model, ensuring that the final results are applicable to large-scale epidemic models in the United States.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.

2019冠状病毒病疫情使疫情控制的艰难权衡成为公众关注的焦点。虽然可以通过封锁、旅行限制、强制保持社会距离和分配疫苗等各种干预措施来缓解流行病，但其中许多方法可能代价高昂，不仅会造成困难和失业，还会减少弱势群体获得关键服务的机会。此外，公众对严厉的流行病控制干预措施的强烈反对可能使其无法长期持续。本项目将解决如何以最佳方式设计此类干预措施的问题。目标将是设计诸如上述各种干预措施的组合，以达到流行病应以多快的速度消退的目标率，同时使整个社会遭受的困难最少。该项目将开发在不同地点之间既具有空间异质性又具有协调性的干预措施。根据每个地点的病例数以及每个地点对未来流行病传播的地理重要性，对不同地点进行区别对待，将获得最优的流行病控制策略。为此目的开发的方法将在不同的流行病模型中具有鲁棒性，并可能适用于未来的大流行，这些大流行可能不具有COVID-19的关键特征。技术方法将考虑到疾病参数的不确定性，这些参数不仅因地点而异，而且随着干预措施和人类行为而不断演变。最后，还将制定尊重某些公平约束的解决方案，例如确保病例较少的地点不会面临更严厉的封锁，这是一些最优封锁的反直觉特征。新开发的方法将保证适用于空间流行病模型大小的多项式运算，而且往往是线性运算，从而确保最终结果适用于美国的大规模流行病模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。