Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting.
Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting.
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Covid-19动态监测和实时时空预测。
DOI：
10.3389/fpubh.2021.641253
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发表时间：
2021
影响因子：
5.2
通讯作者：
da Silva Filho AG
中科院分区：
文献类型：
--
作者：
da Silva CC;de Lima CL;da Silva ACG;Silva EL;Marques GS;de Araújo LJB;Albuquerque Júnior LA;de Souza SBJ;de Santana MA;Gomes JC;Barbosa VAF;Musah A;Kostkova P;Dos Santos WP;da Silva Filho AG
背景：人类经常周期性地面临新的和正在出现的病毒，这些病毒可能是一个重大的全球威胁。西班牙流感大流行已经过去了世纪，我们正在目睹一种新型冠状病毒SARS-CoV-2，它是COVID-19的罪魁祸首。它于2019年12月从武汉市（中国）出现，在几个月内，该病毒在全球范围内传播，目前造成超过5000万例病例，超过100万人死亡。高感染率加上人口动态流动，需要工具，特别是在巴西的情况下，支持卫生管理人员制定控制和防治新病毒的政策。方法：在这项工作中，我们提出了一种使用机器学习方法进行实时时空分析的工具。COVID-SGIS系统汇集了分布在巴西公共卫生系统中的常规收集的关于COVID-19的健康数据，并考虑到COVID-19的地理和时间依赖特征，以便进行时空预测。数据按联邦单位和市政府细分。在我们的案例研究中，我们对巴西和每个联邦单位的病例和死亡分布进行了时空预测。研究了四种回归方法：线性回归、支持向量机（多项式核和RBF）、多层感知器和随机森林。我们使用百分比RMSE和相关系数作为质量度量。结果：对于定性评估，我们对2020年5月25日至27日期间进行了时空预测。从定性和定量的角度考虑伯南布哥州和巴西作为一个整体的情况，线性回归提出了最好的预测结果（专题地图具有良好的数据分布，相关系数>0.99，RMSE（%）<4%，伯南布哥和巴西约5%），训练时间较短：[0.00; 0.04 ms]，CI 95%。结论：时空分析为COVID-19累计确诊病例集中的地区提供了更广泛的评估。在专题地图上可以区分病例最集中的区域、低集中的区域和过渡范围内的区域。这种方法对于支持卫生管理人员和流行病学家制定控制新冠肺炎大流行的政策和计划至关重要。
Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.
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影响因子：
4.6
作者：
Khajanchi, Subhas;Bera, Sovan;Roy, Tapan Kumar
通讯作者：
Roy, Tapan Kumar
影响因子：
7.8
作者：
Gondim, Joao A. M.;Machado, Larissa
通讯作者：
Machado, Larissa
影响因子：
--
作者：
de Freitas Barbosa VA;Gomes JC;de Santana MA;Albuquerque JE;de Souza RG;de Souza RE;dos Santos WP
通讯作者：
dos Santos WP
影响因子：
2
作者：
Apostolopoulos, Ioannis D.;Aznaouridis, Sokratis I.;Tzani, Mpesiana A.
通讯作者：
Tzani, Mpesiana A.
DOI：
10.1063/5.0016240
发表时间：
2020-07
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Khajanchi S;Sarkar K
通讯作者：
Sarkar K