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?:abstract
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COVID-19 began to break out in China in early 2020, characterized by rapid transmission and a high fatality rate In the face of the outbreak, it is particularly important and urgent to predict the number of infections in each region Even now, some countries are still in the early stages of an outbreak The existing traditional mechanistic models such as SIR have a high demand for data For example, parameters such as exit rate need a large number of data to ensure the accuracy of the model;otherwise, the prediction effect is poor, while the models or algorithms related to artificial intelligence have a higher demand for data Moreover, both of them have a large amount of work to solve, which has poor effect on the prediction of early epidemic prevention and control The problem of epidemic situation prediction in the absence of data urgently needs to be solved After data preprocessing, this paper used the Logistic model (the growth retardation model) to predict the number of infections of China Meanwhile, error analysis and stability analysis were carried out, and the data of Italy were used for checking the universality of the model The results show that the Logistic model is easy to be solved, has good stability and accuracy, and is suitable for early prediction of COVID-19 © Published under licence by IOP Publishing Ltd
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