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COVID-SGIS:一种用于新冠疫情动态监测和时间预测的智能工具。

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19.

机构信息

Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil.

Center for Informatics, Federal University of Pernambuco, Recife, Brazil.

出版信息

Front Public Health. 2020 Nov 17;8:580815. doi: 10.3389/fpubh.2020.580815. eCollection 2020.

Abstract

The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America.

摘要

新冠病毒 SARS-CoV-2 的全球负担正在以前所未有的速度增加。目前,新冠疫情在巴西的传播存在问题,给其人口和国家卫生保健服务带来了巨大的公共卫生负担。为了评估缓解这些问题的策略,有必要对病例和死亡人数进行预测,以便为利益相关者提供对抗疾病的决策过程提供帮助。我们提出了一种新的巴西新冠病毒累计病例实时预测系统。我们开发了一种名为 COVID-SGIS 的新应用程序,用于对巴西的新冠疫情进行实时监测、预测和空间可视化。该系统从巴西.io 数据库中捕获了来自 27 个联邦单位的常规报告的新冠疫情信息。它利用了自 2020 年 3 月至 5 月通过国家通报系统报告的所有新冠确诊病例数据。时间序列 ARIMA 模型被整合用于预测新冠病毒累计病例数和死亡人数。这些模型包括对巴西每个联邦单位的 6 天预测,作为图形输出,分别具有其相应的 95%CI 以确保统计显著性。此外,还呈现了最坏和最好的情景。我们的 ARIMA 模型标记了以下联邦单位(共 27 个),这些单位在指定的逐日时间段内显示出新冠病毒病例的时间模式具有统计学意义的增加:巴伊亚、马拉尼昂、皮奥伊、北里奥格兰德、阿马帕、朗多尼亚,其逐日预测均在 95%CI 范围内。同样,在圣埃斯皮里图、米纳斯吉拉斯、巴拉那和圣卡塔琳娜也观察到了同样的发现。预测值与实际值之间的总百分比误差在 2.56%至 6.50%之间。对于预测值落在预测区间之外的日子,与最坏情况相比,百分比误差低于 5%。动态预测的提出方法可以用于以具有成本效益、简洁和稳健的方式指导社会政策和直接干预。这种新工具可以为指导巴西和南美洲邻国对抗新冠疫情的行动方向发挥重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02db/7705350/2e9b58b5c605/fpubh-08-580815-g0001.jpg

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