论文标题
优先考虑市政铅缓解项目作为轻松的背包优化：一种方法和案例研究
Prioritizing municipal lead mitigation projects as a relaxed knapsack optimization: a method and case study
论文作者
论文摘要
铅管补救预算是有限的，应该最大程度地发挥公共卫生影响。这个目标意味着一个非平凡的优化问题；铅服务线将水管连接到各个房屋，但是任何现实的替代策略都必须更大规模批量替换。此外，计划人员通常缺乏比较潜在干预措施的相对公共健康价值的原则方法，并且通常基于非健康因素计划项目。本文描述了一个简单的过程，可以通过清洁和合成通常可用但由于数据质量问题而很少加入的城市数据集来估算包裹级别的儿童健康影响。使用地理编码作为核心记录连锁机制，可以将包裹级的毒性数据与学校入学记录结合使用，以指示幼儿和铅线在哪里共存。在包裹水平上描述了估计暴露年度的损害度量，然后可以将其汇总到项目水平，并通过将项目选择作为0/1 knapsack问题进行最小化。用贪婪算法进一步简化了非专家使用，即直观地求解了隐含的线性编程松弛。按福利成本比率订购项目会产生优先级清单，然后计划者可以与更努力地量化因素一起考虑，以量化因素。一项案例研究表明，该框架成功地应用于美国城市的现有数据，以优先考虑联邦基础设施资金。尽管本文着重于饮用水中的铅，但该方法很容易将其推广到其他住宅毒性来源，对儿童的影响不成比例。
Lead pipe remediation budgets are limited and ought to maximize public health impact. This goal implies a non-trivial optimization problem; lead service lines connect water mains to individual houses, but any realistic replacement strategy must batch replacements at a larger scale. Additionally, planners typically lack a principled method for comparing the relative public health value of potential interventions and often plan projects based on non-health factors. This paper describes a simple process for estimating child health impact at a parcel level by cleaning and synthesizing municipal datasets that are commonly available but seldom joined due to data quality issues. Using geocoding as the core record linkage mechanism, parcel-level toxicity data can be combined with school enrollment records to indicate where young children and lead lines coexist. A harm metric of estimated exposure-years is described at the parcel level, which can then be aggregated to the project level and minimized globally by posing project selection as a 0/1 knapsack problem. Simplifying further for use by non-experts, the implied linear programming relaxation is solved intuitively with the greedy algorithm; ordering projects by benefit cost ratio produces a priority list which planners can then consider holistically alongside harder to quantify factors. A case study demonstrates the successful application of this framework to a small U.S. city's existing data to prioritize federal infrastructure funding. While this paper focuses on lead in drinking water, the approach readily generalizes to other sources of residential toxicity with disproportionate impact on children.
