Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks

项目摘要 截至2021年6月30日，西弗吉尼亚州55个县中有23%的县排名在前20% 美国易受新冠肺炎影响的县。该州极度脆弱的核心是更高的 医疗合并症的流行，农村人口获得护理的机会较少，疫苗接种减少 与城市同行相比，这一比例更高。值得关注的是，全州范围内的检测已经减少，以允许 疫苗的积极传播。不幸的是，低检测化合物在医学上对新冠肺炎的脆弱性 疫苗接种率低的服务不足的人群，因为他们极易受到持续本地化的影响 病毒的暴发和随后更高的发病率和死亡率。我们的RADx-UP第二阶段建议 在之前资助的RADx-UP第一阶段的基础上，通过识别和瞄准疫苗沙漠社区 然后根据各个社区的需求定制测试事件服务，并根据他们对 重要的是。为持续测试提供动态解决方案至关重要。我们定义了疫苗沙漠 使用总疫苗接种率和两周内疫苗接种量的变化。机器学习与 时间序列建模被用来表征县级传播率，这是第一次在这里纳入 疫苗接种率。县一级的风险评估覆盖着邮政编码，疫苗沙漠 根据14天内的总接种率和疫苗接种变化的最低十进制来确定。 一旦确定了一个社区，研究联络将把研究人员与倡导者联系起来，进行半结构化 面谈以确定举办测试活动的合作伙伴站点，并收集数据以定制促销、食品和媒体 向每个目标社区的特定需求发送消息。测试活动将包括抽样和调查 数据收集，通过促销和机会赠送来激励社区参与。我们建造 在RADX-UP一项活动中，重点关注每个社区的第一响应者，以帮助托管测试 事件，以及基于信仰和利润(如适用)。我们让有很强联系的合作调查员 西弗吉尼亚州南部，RADx-UP第一阶段资源有限的地区。此外，我们还进行了一项初步研究，以 在600名参与者中检查雅培ID NOW等温聚合酶链式反应系统的性能。的效果。 采用空间回归的方法对该县的干预措施进行监测，评价干预效果 分析。我们提出的用来评估我们的测试策略的统计框架的一个独特属性是能力 描述除了目标社区外，对附近县的影响。这个项目将利用 现有并发展自己与地方和州机构的独特伙伴关系，以实施 社区参与测试疫苗沙漠内的投放模式。我们方法的一个关键和新颖的方面 是否建立了一个基层急救人员研究网络，可以利用该网络来实施 在孤立的医疗服务不足的社区进行筛查计划或研究急救者的健康结果。

PROJECT ABSTRACT As of June 30, 2021, 23% of West Virginia’s (WV) 55 counties were ranked within the top 20% of most vulnerable counties to Covid-19 in the United States. Central to the state’s extreme vulnerability is higher prevalence of medical comorbidities, lower access to care among rural populations, and decreased vaccine uptake compared to urban counterparts. Of considerable concern, testing has decreased statewide to allow for active dispersal of the vaccines. Unfortunately, low testing compounds vulnerability to Covid-19 in medically underserved populations where vaccine uptake is low, as they are extremely susceptible to persistent localized outbreaks of the virus and subsequently higher morbidity and mortality. Our RADx-UP Phase Two proposal builds upon previously funded RADx-UP Phase One by identifying and targeting vaccine desert communities then tailoring testing event services to the needs of individual communities building upon their perceptions of what is important. Providing a dynamic solution for continued testing is critical. We define vaccine deserts using overall vaccination rate and the change in vaccine uptake over a two-week period. Machine learning with time series modeling is used to characterize county level transmissibility, incorporating here for the first-time vaccination rates. Risk estimates at the county level are overlaid with zip codes where vaccine deserts have been identified using bottom decile for overall vaccination rate and change in vaccination over a 14-day period. Once a community is identified study liaisons will connect study staff to advocates to conduct semi-structured interviews to identify partner sites to host testing events and collect data to tailor promotions, food, and media messaging to the specific needs of each community targeted. Testing events will involve sample and survey data collection, with promotions and chance giveaways to incentivize communities to participate. We build upon RADx-UP one activity by focusing heavily on first responders in each community to aid in hosting testing events, and faith based and on profits where applicable. We involve co-investigators with strong connections to southern WV, an area with limited resources for RADx-UP Phase One. Additionally, we conduct a pilot study to examine the performance of the ABBOTT ID Now isothermal PCR system in 600 participants. Effect of the intervention is evaluated through monitoring of pre and post testing rate for the county using spatial regression analyses. A unique attribute of the statistical framework we propose to evaluate our testing strategy is an ability to describe the impact on nearby counties in addition to the targeted community. This project will leverage existing and develop its own unique partnerships with local and state agencies for implementation of a community engaged testing delivery model within vaccine deserts. A critical and novel aspect of our approach is establishment of a grass roots first responders research network which can be leveraged to implement screening programs in isolated medically underserved communities or study first responder health outcomes.