We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems poses an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive model eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent spread of infection and thus most of the deaths occurring in this group are avoided.

我们采用深度学习来提出一种基于人工神经网络（ANN）和数据流引导的实时增量学习算法，用于对Covid-19疾病的非介入，智能，自适应和在线分析模型进行参数估计。此类问题的建模和仿真带来了不断发展的训练数据的另一项挑战，其中模型参数会根据外部因素随时间变化。我们的主要贡献在于，与典型的深度学习技术不同，在训练数据不断发展的情况下，这种非侵入性模型消除了每次接收到新的训练数据集时从头开始重新训练或重建模型的需求。验证模型后，我们将其用于研究流行病控制的不同策略的影响。最后，我们提出并模拟了通过基于风险的人群区隔（PC）进行自然免疫控制的策略，其中根据风险因素（例如合并症和年龄）将人群分为低风险（LR）和高风险（HR）舱室，通过隔离HR隔室，同时允许LR隔室发展自然免疫力，实现疾病传播动态。从预防隔离中释放后，HR隔室发现自己被足够数量的免疫个体包围，以防止感染扩散，因此避免了这一组中大多数死亡。