Digital twins to fight against COVID-19 pandemic

Abstract

This study is aimed to explore the anti-epidemic effect of artificial intelligence (AI) algorithms such as digital twins on the COVID-2019 (novel coronavirus disease 2019), so that the information security and prediction accuracy of epidemic prevention and control (P & C) in smart cities can be further improved. It addresses the problems in the current public affairs governance strategy for the outbreak of the COVID-2019 epidemic, and uses digital twins technology to map the epidemic P & C situation in the real space to the virtual space. Then, the blockchain technology and deep learning algorithms are introduced to construct a digital twins model of the COVID-2019 epidemic (the COVID-DT model) based on blockchain combined with BiLSTM (Bi-directional Long Short-Term Memory). In addition, performance of the constructed COVID-DT model is analyzed through simulation. Analysis of network data security transmission performance reveals that the constructed COVID-DT model shows a lower average delay, its data message delivery rate (DMDR) is basically stable at 80%, and the data message disclosure rate (DMDCR) is basically stable at about 10%. The analysis on network communication cost suggests that the cost of this study does not exceed 700 bytes, and the prediction error does not exceed 10%. Therefore, the COVID-DT model constructed shows high network security performance while ensuring low latency performance, enabling more efficient and accurate interaction of information, which can provide experimental basis for information security and development trends of epidemic P & C in smart cities.