论文标题
半监督神经网络解决了建模COVID-19的逆问题
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
论文作者
论文摘要
研究COVID-19的动力学对于理解限制性措施的效率和制定抗衡即将到来的传染波的策略至关重要。在这项工作中，我们使用半监督神经网络研究了Covid-19的传播，并假设人口的被动部分仍与病毒动力学隔离。我们从一个无监督的神经网络开始，该网络了解不同建模参数和初始条件的微分方程解决方案。然后，一种监督方法通过估计生成功能的最佳条件来解决逆问题，以适应被COVID-19造成的人感染，从中恢复和死亡的人的数据。这种半监督的方法结合了实际数据，以确定差异，被动人口的演变以及不同国家的基本繁殖数。
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling parameters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.
