Duke Global Health Institute, Duke University, Global Emergency Medicine Innovation and Implementation Research. 310, Trent Drive, Durham North Carolina USA.
Organizacão Pan-Americana da Saúde / Organização Mundial da Saúde. Brasília DF Brasil.
Cien Saude Colet. 2021 May;26(5):1885-1898. doi: 10.1590/1413-81232021265.02312021.
This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.
这篇文章探讨了利用空间人工智能来估计巴西 COVID-19 免疫接种运动所需的资源。我们使用二次数据,采用时间序列设计进行了横断面生态研究。分析单位是巴西的基层医疗中心(PCC)。我们使用人工智能算法和卫星图像对 PCC 集水区的人口进行了四步分析。我们还评估了每个 PCC 的互联网接入情况,并对市级与 COVID-19 相关的 SARS 病例的趋势进行了时空聚类分析。约 18%的巴西老年人口居住在离接种点超过 4 公里的地方。共有 4790 个市政府的 SARS 病例呈上升趋势。离手机信号塔超过 5 公里的 PCC 数量在北部和东北部地区最多。需要创新策略来应对国家 COVID-19 疫苗接种计划实施带来的挑战。使用基于空间人工智能的方法可以帮助改善该国对 COVID-19 的应对。