Background: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective: The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. Methods: In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. Results: We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. Conclusions: The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

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中文翻译：

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通过深度学习对具有时间相关参数的快速响应COVID-19传播模型的现实意义：模型开发和验证。

背景：自2020年3月以来，COVID-19大流行已在世界范围内造成了重大破坏。1918年流感大流行的经验表明，COVID-19的感染率下降并不能保证趋势的连续性。目的：本研究的目的是通过深度学习来开发具有时间相关参数的精确的COVID-19传播模型，以对疫情的动态情况做出快速响应并积极地将损失降至最低。方法：在这项研究中，我们通过基于正反问题的深度学习研究了具有时间相关参数的数学模型。我们分别使用了韩国约翰霍普金斯大学和其他国家的韩国疾病控制与预防中心（KCDC）和系统科学与工程中心（CSSE）的数据。由于数据由已确诊，已恢复和已故的病例组成，因此我们选择了易感感染恢复（SIR）模型，并找到了近似解和模型参数。具体来说，我们将完全连接的神经网络应用于解决方案和参数，并设计了合适的损失函数。结果：我们通过深度学习方法开发了一个具有时变参数的全新SIR模型。此外，我们使用常规的Runge-Kutta四阶模型验证了该模型，以确认其收敛性。此外，我们根据KCDC，韩国政府和新闻媒体报道的现实情况评估了我们的模型。我们还使用来自CSSE的意大利，瑞典和美国的数据对模型进行了交叉验证。结论：这项研究的方法和新模型可以用于COVID-19的短期预测，这可以帮助政府为新的爆发做好准备。此外，从衡量医疗资源的角度来看，我们的模型具有强大的优势，因为它假定所有参数都是时间依赖性的，这反映了病毒传播的确切状态。

这仅仅是抽象的。阅读JMIR网站上的全文。JMIR是互联网时代电子健康和医疗保健领域领先的开放获取期刊。