TY - GEN
T1 - HMES
T2 - 2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022
AU - Geng, Haoyu
AU - Zheng, Guanjie
AU - Han, Zhengqing
AU - Wei, Hua
AU - Li, Zhenhui
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, the current methods suffer from three issues. First, most of the current studies focus on epidemic prediction, which can not provide adequate support for intervention policy making. Second, most of the current interventions are based on population groups rather than fine-grained individuals, which can not make the measures towards the infected people and may cause waste of medical resources. Third, current simulations are not efficient and flexible enough for large-scale complex systems.In this paper, we propose a new epidemic simulation framework called HMES to address above three challenges. The proposed framework covers a full pipeline of epidemic simulation and enables comprehensive fine-grained control in large scale. In addition, we conduct experiments on real COVID-19 data. HMES demonstrates more accurate modeling of disease transmission up to 300 million people and up to 3 times acceleration compared to the state-of-the-art methods.
AB - Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, the current methods suffer from three issues. First, most of the current studies focus on epidemic prediction, which can not provide adequate support for intervention policy making. Second, most of the current interventions are based on population groups rather than fine-grained individuals, which can not make the measures towards the infected people and may cause waste of medical resources. Third, current simulations are not efficient and flexible enough for large-scale complex systems.In this paper, we propose a new epidemic simulation framework called HMES to address above three challenges. The proposed framework covers a full pipeline of epidemic simulation and enables comprehensive fine-grained control in large scale. In addition, we conduct experiments on real COVID-19 data. HMES demonstrates more accurate modeling of disease transmission up to 300 million people and up to 3 times acceleration compared to the state-of-the-art methods.
UR - https://www.scopus.com/pages/publications/85168125529
UR - https://www.scopus.com/pages/publications/85168125529#tab=citedBy
U2 - 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00085
DO - 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00085
M3 - Conference contribution
AN - SCOPUS:85168125529
T3 - Proceedings - 2022 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022
SP - 468
EP - 475
BT - Proceedings - 2022 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2022 through 18 December 2022
ER -