Propagation analysis and prediction of the COVID-19
Lixiang Li, Zihang Yang, Zhongkai Dang, Cui Meng, Jingze Huang, Hao, Tian Meng, Deyu Wang, Guanhua Chen, Jiaxuan Zhang, Haipeng Peng
TL;DR
This paper models COVID-19 transmission using official data, achieving high accuracy in predictions and aiding decision-making for affected countries.
Contribution
It introduces a data-driven model for COVID-19 propagation with less than 3% error, enabling accurate forward and backward epidemic analysis.
Findings
Model error within 3% of official data
Effective forward prediction of epidemic trends
Useful backward inference for epidemic analysis
Abstract
Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is within 3%. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 diagnosis using AI · SARS-CoV-2 and COVID-19 Research