A systematic approach for COVID-19 predictions and parameter estimation

Abstract

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19

“…In the formula, σ is the kernel function, which defines the nonlinear mapping from the original space to the highdimensional feature space. Moreover, each basis function center corresponds to a support vector, and their output weights are automatically determined by the algorithm [1,28].…”

“…In the context of the new crown epidemic, people need to reduce the chance of going out and stay at home as much as possible to reduce the probability of infection. The best way to care for stroke patients is home care, and stroke patients need to formulate nursing methods with the assistance of intelligent equipment [1,2].…”

What Can COVID-19 Teach Us about Using AI in Pandemics?

“…The incorporation of contact tracing further augments the power of AI prediction in the spread of a pandemic [ 23 ]. The effect of mitigating measures can be studied to identify the most effective ones [ 43 , 44 , 45 ]. However, the accuracy of AI predictions varies in part with how well its pre-training aligns with actual events [ 38 , 46 ].…”

“…Rapid identification of treatment and vaccine candidates Erroneous delegation of decisions to AI with insufficient human oversight, e.g., of clinical trials or the role of social disparities [41][42][43]45,46,48,73] Not surprisingly, the notion of incorporating AI decision-making in healthcare stirs considerable controversy, including about responsibility and ethics [13,32]. However, the point here is not that AI will make more and better decisions, but rather that it could facilitate more targeted and ethical decision-making.…”

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

“…Each tissue of the human body has different absorption and transmittance rates for X-rays. After a series of processing, an image is finally generated, and professional doctors can know the patient's physical condition by observing the image [21,22]. The improvement of artificial intelligence technology can improve the quality of system imaging to a certain extent.…”