A new pandemic attack happened over the world in the last month of the year 2019 which disrupt the lifestyle of everyone around the globe. All the related research communities are trying to identify the behaviour of pandemic so that they can know when it ends but every time it makes them surprise by giving new values of different parameters. In this paper, support vector regression (SVR) and deep neural network method have been used to develop the prediction models. SVR employs the principle of a support vector machine that uses a function to estimate mapping from an input domain to real numbers on the basis of a training model and leads to a more accurate solution. The long short-term memory networks usually called LSTM, are a special kind of RNN, capable of learning long-term dependencies. And also is quite useful when the neural network needs to switch between remembering recent things, and things from a long time ago and it provides an accurate prediction to COVID-19. Therefore, in this study, SVR and LSTM techniques have been used to simulate the behaviour of this pandemic. Simulation results show that LSTM provides more realistic results in the Indian Scenario.

2019 年的最后一个月，全球发生了一场新的流行病袭击，扰乱了全球每个人的生活方式。所有相关的研究团体都在试图确定大流行的行为，以便他们知道大流行何时结束，但每次都通过给出不同参数的新值让他们感到惊讶。在本文中，支持向量回归 (SVR) 和深度神经网络方法已被用于开发预测模型。SVR 采用支持向量机的原理，该原理使用函数在训练模型的基础上估计从输入域到实数的映射，从而得出更准确的解。长短期记忆网络通常称为 LSTM，是一种特殊的 RNN，能够学习长期依赖关系。当神经网络需要在记住最近的事情和很久以前的事情之间切换时，它也非常有用，它为 COVID-19 提供了准确的预测。因此，在本研究中，SVR 和 LSTM 技术已被用于模拟这种流行病的行为。仿真结果表明，LSTM 在印度场景中提供了更真实的结果。