Optimizing Flu Shot Design Under Uncertainity

在不确定性下优化流感疫苗设计

• 批准号: 0826141
• 负责人: Andrew Schaefer
• 金额: $ 32.68万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0826141&HistoricalAwards=false
• 关键词: Optimizing; Flu; Shot; Design; Under

Flu impacts between 5-15% of the world's population annually. The first line of defense against seasonal flu is the annual ?flu shot,? which is comprised of three strains. This proposal focuses on mathematical models for determining the optimal flu shot composition as well as the timing of its production. This decision is greatly complicated by its stochastic and dynamic nature. The flu shot composition must be determined many months before the start of the flu season. The flu shot manufacturing process is inherently uncertain, as it relies on cultivating strains from chicken eggs. Furthermore, the future prevalence of various flu viruses is uncertain, so that the flu shot must be designed without knowing what strains are threats. Currently, three strains are incorporated into the flu shot. By selecting these strains early, society can increase the probability that enough vaccine can be produced. However, this comes at the cost of reduced surveillance, which increases the probability that the wrong strains will be selected. Conversely, delaying the decision will improve surveillance at the expense of decreased manufacturing time. We propose a class of multi-stage stochastic mixed-integer programming (SMIP) models that fully explore these trade-offs and determine the best flu shot composition, along with the timing of its manufacturing. Such models enjoy a well deserved reputation for computational difficulty. We will develop state-of-the-art optimization methods for solving these difficult problems. The potential broader impacts of the proposed research are immense, as seasonal flu has enormous social and economic costs, and an optimized flu shot can mitigate much of these costs. Once successfully calibrated and developed, the proposed mathematical models can inform flu shot policy resulting in better flu shots and more reliable production. The educational impacts of this proposal will be felt by four groups. We will educate medical researchers through short courses and mentoring. Graduate students will benefit, as the results of this research will be taught in PhD courses in Industrial Engineering. Undergraduate students will take part directly in this research through REU programs. Finally, we will continue our outreach to K-12 schools in inner-city Pittsburgh, developing several matching games to illustrate the benefits of mathematical modeling.

流感每年影响世界人口的5%至15%。预防季节性流感的第一道防线是每年接种一次流感疫苗。它由三个菌株组成。这项建议侧重于确定最佳流感疫苗成分及其生产时机的数学模型。由于其随机性和动态性，这一决策非常复杂。流感疫苗的成分必须在流感季节开始前几个月确定。流感疫苗的制造过程本质上是不确定的，因为它依赖于从鸡蛋中培养菌株。此外，各种流感病毒未来的流行情况还不确定，因此必须在设计流感疫苗时不知道哪些毒株是威胁。目前，流感疫苗中加入了三种毒株。通过及早选择这些毒株，社会可以增加生产足够疫苗的可能性。然而，这是以减少监测为代价的，这增加了选择错误菌株的可能性。相反，推迟决策将以减少制造时间为代价改善监控。我们提出了一类多阶段随机混合整数规划(SMIP)模型，它充分探索了这些权衡，并确定了最佳流感疫苗成分，以及它的制造时间。这类模型因计算困难而享有当之无愧的声誉。我们将开发最先进的优化方法来解决这些难题。这项拟议研究的潜在更广泛影响是巨大的，因为季节性流感会带来巨大的社会和经济成本，而优化的流感疫苗可以减轻大部分这些成本。一旦成功校准和开发，提出的数学模型可以为流感疫苗接种政策提供信息，从而产生更好的流感疫苗接种和更可靠的生产。这项提议的教育影响将由四个群体感受到。我们将通过短期课程和辅导来教育医学研究人员。研究生将受益，因为这项研究的结果将在工业工程博士课程中教授。本科生将通过REU项目直接参与这项研究。最后，我们将继续与匹兹堡市中心的K-12学校进行接触，开发几个配对游戏来说明数学建模的好处。