Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites
Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites
复制标题
DOI：
10.1101/2021.12.15.21267736
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Z. Mehrab;M. Wilson;S. Chang;G. Harrison;B. Lewis;A. Telionis;J. Crow;D. Kim;S. Spillmann;K. Peters;J. Leskovec;M. Marathe
中科院分区：
文献类型：
--
作者：
Z. Mehrab;M. Wilson;S. Chang;G. Harrison;B. Lewis;A. Telionis;J. Crow;D. Kim;S. Spillmann;K. Peters;J. Leskovec;M. Marathe
在美国各地部署疫苗为预防COVID-19导致的严重疾病和死亡提供了重要的保障。超过70%的符合接种条件的美国人至少部分接种了疫苗，但也有一些人口接种不足，例如在农村地区和某些人口统计群体（例如年龄，种族，民族）。这些未接种疫苗的口袋极易受到Delta变异的影响，加剧了医疗危机并增加了新变异的风险。在本文中，我们描述了一个数据驱动的模型，通过推荐移动的疫苗接种点的位置，以针对接种不足的人群，为弗吉尼亚州的公共卫生官员提供实时支持。我们的策略使用细粒度的流动性数据，沿着美国人口普查和疫苗接种数据，以确定最有可能被未接种疫苗的人访问的地点。我们进一步扩展了我们的模型，以选择在犹豫不决的群体中最大限度地提高疫苗接种率的地点。我们发现，在一些人口统计中，最受推荐的网站差异很大，这表明了开发集成细粒度异构数据源的自定义推荐模型的价值。此外，我们使用了统计上等效的合成人口来研究组合人口统计学的影响（例如，特定种族和年龄的人），这是不可能单独使用美国人口普查数据的。我们通过分析部署疫苗地点的成功率来验证我们的建议，并表明靠近我们推荐区域的地点接种的剂量更高。我们的模型是第一个考虑实时变化的流动模式，为不同的目标人口群体提供定制的安置策略。我们的研究结果将在IAAI-22上提交，但考虑到这一流行病的严重性，我们提供了该文件的扩展版本，以便更及时地考虑我们的方法并涵盖更多的发现。
The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.