利用实时预测和机器学习估计 COVID-19 的漏诊情况。
Estimating underdiagnosis of COVID-19 with nowcasting and machine learning.
机构信息
Prefeitura de Florianópolis - Florianópolis (SC), Brazil.
Information Sciences Center, Universidade Federal de Santa Catarina - Florianópolis (SC), Brazil.
出版信息
Rev Bras Epidemiol. 2021 Oct 29;24:e210047. doi: 10.1590/1980-549720210047. eCollection 2021.
OBJECTIVE
To analyze the underdiagnosis of COVID-19 through nowcasting with machine learning in a Southern Brazilian capital city.
METHODS
Observational ecological design and data from 3916 notified cases of COVID-19 from April 14th to June 2nd, 2020 in Florianópolis, Brazil. A machine-learning algorithm was used to classify cases that had no diagnosis, producing the nowcast. To analyze the underdiagnosis, the difference between data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms were compared.
RESULTS
The number of new cases throughout the entire period without nowcasting was 389. With nowcasting, it was 694 (95%CI 496-897). During the six-day period, the number without nowcasting was 19 and 104 (95%CI 60-142) with nowcasting. The underdiagnosis was 37.29% in the entire period and 81.73% in the six-day period. The underdiagnosis was more critical in the six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period.
CONCLUSION
The use of nowcasting with machine learning techniques can help to estimate the number of new disease cases.
目的
通过机器学习在巴西南部首府对 COVID-19 进行实时预测分析。
方法
观察性生态设计，使用巴西弗洛里亚诺波利斯市 2020 年 4 月 14 日至 6 月 2 日期间报告的 3916 例 COVID-19 确诊病例的数据。使用机器学习算法对无诊断病例进行分类，生成实时预测。为分析漏诊情况，将无实时预测数据与整个时期和症状出现后 6 天的实时预测中位数进行了比较。
结果
整个时期无实时预测的新病例数为 389 例，有实时预测的新病例数为 694 例（95%CI 496-897）。在 6 天的时期内，无实时预测的病例数为 19 例，有实时预测的病例数为 104 例（95%CI 60-142）。整个时期的漏诊率为 37.29%，6 天时期的漏诊率为 81.73%。在数据收集前症状出现到诊断的 6 天内，漏诊率比整个时期更为严重。
结论
使用机器学习技术进行实时预测可以帮助估计新发病例数。
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