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The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3–4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022. [ABSTRACT FROM AUTHOR]

The corona virus pandemic is one of the largest global health crises in recent times. Several studies are being proposed to demonstrate how Machine Learning techniques can be used for the prediction of the disease. While some of the studies focus on the prediction of the diseases in patients, some other ones focus on predicting its mode of spread across countries of the world. Corona virus was formally announced in China in December, 2019. Since then, the disease has been spreading in large number. More than 3.4 million people have been infected as at 3rd May, 2020. As parts of the efforts to help in predicting the presence of the disease in patients as well as its geographical spread, some Machine Learning-based techniques are being proposed. This study first explores the various promises of Machine Learning as a sub field of Artificial Intelligence in the prediction of corona virus disease. We provided information on how Machine Learning approaches can aid for predictive analytics. We equally emphasized the use of comprehensive and reliable datasets for reliable predictive modeling of the virus. However, since the disease is novel, it is of interest to study some of the datasets that are evolving. This is because having representative datasets for training and testing predictive COVID-19 models is very important. It is believed that future researchers in the area of corona virus classification and prediction can benefit immensely from the findings of this study. [ABSTRACT FROM AUTHOR]

Introduction: China has witnessed a new virus Corona, which is named COVID-19. It has become the world's most concern as this virus has spread over the worldat a higher speed;the world has witnessed more than one lakh cases and one thousand deaths in a span of few days. Methods: We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the datasets from Kaggle and other standard websites. Results: The proposed classifier, hybrid non-linear cellular automata (HNLCA), was trained with 23,078 datasets and tested with 6785 datasets. HNLCA is compared with conventional methods of long shortterm memory, AdaBoost, support vector machine, regression, and SVR and has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc. [ABSTRACT FROM AUTHOR]