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

使用机器学习和地理空间分析识别 COVID-19 疫苗沙漠,以针对弱势农村人口进行社区参与测试,以防止局部疫情爆发

基本信息

  • 批准号:
    10544766
  • 负责人:
  • 金额:
    $ 113.89万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-01-01 至 2023-11-30
  • 项目状态:
    已结题

项目摘要

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.
项目摘要

项目成果

期刊论文数量(0)
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Brian Hendricks其他文献

Brian Hendricks的其他文献

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{{ truncateString('Brian Hendricks', 18)}}的其他基金

Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaks
使用机器学习和地理空间分析识别 COVID-19 疫苗沙漠,以针对弱势农村人口进行社区参与测试,以防止局部疫情爆发
  • 批准号:
    10446844
  • 财政年份:
    2022
  • 资助金额:
    $ 113.89万
  • 项目类别:

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