Cassão Victor, Alves Domingos, Mioto Ana Clara de Andrade, Mozini Mariana Tavares, Segamarchi Renan Barbieri, Miyoshi Newton Shydeo Brandão
São Carlos School of Engineering, University of São Paulo.
Ribeirao Preto Medical School, University of São Paulo.
Procedia Comput Sci. 2023;219:1453-1461. doi: 10.1016/j.procs.2023.01.435. Epub 2023 Mar 22.
Brazil is one of the countries with the worst response against the pandemic scenario of coronavírus. At the beginning we were on average with 4000 deaths in a 24 hours period. In the course of this situation, large amounts of health and medicine datasets were being generated in real time, requiring effective ways to extract information and discover patterns that can help in the fight against this disease. And even more important is to monitor the progress of prophylactic measures and whether they are being effective in reducing the spread of the virus. Thus, the aim of this study is to analyze how the coronavirus has different ways to evolve in each Brazilian state with the influences of the vaccination process. To achieve this goal, the time series Clustering Technique based on a K-Means variation was applied, with the similarity metric Dynamic Time Warping (DTW). We produced this study using the data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants and all vaccination data available. Our results indicate an unevenly occurring vaccination and the need to identify other associated patterns with human development indices and other socio-economic indicators, being this the first analysis developed in the country, under the goals above.
巴西是应对新冠疫情表现最差的国家之一。起初,我们平均每天有4000人死亡。在这种情况下,大量的健康和医学数据集实时生成,需要有效的方法来提取信息并发现有助于抗击这种疾病的模式。更重要的是监测预防措施的进展情况以及它们在减少病毒传播方面是否有效。因此,本研究的目的是分析在疫苗接种过程的影响下,新冠病毒在巴西每个州的演变方式有何不同。为实现这一目标,应用了基于K均值变体的时间序列聚类技术,并采用了动态时间规整(DTW)相似性度量。我们使用巴西卫生部报告的数据进行了这项研究,数据涉及每10万居民的死亡人数以及所有可用的疫苗接种数据。我们的结果表明疫苗接种情况不均衡,并且需要识别与人类发展指数和其他社会经济指标相关的其他模式,这是该国在上述目标下开展的首次分析。