Tarrataca Luís, Dias Claudia Mazza, Haddad Diego Barreto, De Arruda Edilson Fernandes
Department of Computer Engineering, Celso Suckow da Fonseca Federal Center for Technological Education, Petrópolis, Brazil.
Department of Technologies and Languages Multidisciplinary Institute, Federal Rural University of Rio de Janeiro, Nova Iguaçu, Brazil.
J Math Ind. 2021;11(1):2. doi: 10.1186/s13362-020-00098-w. Epub 2021 Jan 6.
The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.
The online version contains supplementary material available at 10.1186/s13362-020-00098-w.
当前的新冠疫情正以不同方式影响着不同国家。各种报告技术以及其他问题,如报告不足和预算限制,使得预测病毒的传播和致死率成为一项具有挑战性的任务。这项工作试图更好地了解新冠疫情将如何影响研究最少的国家之一——巴西。目前,巴西的几个州处于封锁状态。然而,存在解除这类措施的政治压力。这项工作考虑了这种解除措施对病毒在当地演变的影响。这是通过用开/关策略扩展SEIR模型来完成的。鉴于SEIR模型的简单性,我们还试图通过开发一个神经回归器来获得更多见解。我们选择采用当前临床研究已确定与新冠致死率有关的特征。我们讨论了如何处理这些数据以获得可靠的评估。
在线版本包含可在10.1186/s13362-020-00098-w获取的补充材料。