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National COVID-19 vaccination plan: using artificial spatial intelligence to overcome challenges in Brazil

机译:国家Covid-19疫苗接种计划:使用人工空间智能来克服巴西挑战

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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.
机译:本文探讨了空间人工智能来估计实施巴西Covid-19 Immu Nization活动所需的资源。使用二级数据,我们进行了一个横断面生态学研究,采用了一个时间序列设计。分析单位是巴西的初级保健中心(PCC)。进行了四步分析,以估算危机算法和卫星图像中的PCC集水区中的POPULATION。我们还评估了每个PCC中的互联网接入,并控制了在市政层面与Covid-19相关的SARS案例的时空聚类分析。大约18%的巴西老年人人口从疫苗接种点生活4公里。共有4,790个市在SARS案件中表现出上升趋势。距离北部和东北地区有超过5公里处的PCC的数量最大。需要创新的Stra Tegies来解决该国国家Covid-19疫苗接种计划的实施提出的挑战。使用空间人工智能的方法可以帮助改善国家的Covid-19回应。

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