TY - GEN
T1 - Prediction of COVID-19 infection spread through agent-based simulation
AU - An, Taegun
AU - Kim, Hyogon
AU - Joo, Changhee
N1 - Funding Information:
This research was supported in part by the MSIT, Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01819) supervised by the IITP, and in part by the NRF grant (MSIT, Korea) (No. NRF-2021R1A2C2013065)
Publisher Copyright:
© 2022 ACM.
PY - 2022/10/3
Y1 - 2022/10/3
N2 - In this work, we develop an agent-based model to predict the infection spread with accuracy. Under COVID-19 pandemic, we faced difficult decision-making of disease control including social distancing and shutdown, which significantly restricted our daily living. Without strong evidences to its effect, however, it was very hard to implement necessary policies in a timely manner. To this end, it is imperative to design a computationally efficient simulation model that can predict the infection spread with accuracy, taking into account the changes of important control policies. We develop an agent-based model that can incorporate individual behaviors and interactions, while capturing large-scale features of the infection spread. We verify the accuracy of our model by comparing the prediction results with the traces of COVID-19 confirmed cases in several countries.
AB - In this work, we develop an agent-based model to predict the infection spread with accuracy. Under COVID-19 pandemic, we faced difficult decision-making of disease control including social distancing and shutdown, which significantly restricted our daily living. Without strong evidences to its effect, however, it was very hard to implement necessary policies in a timely manner. To this end, it is imperative to design a computationally efficient simulation model that can predict the infection spread with accuracy, taking into account the changes of important control policies. We develop an agent-based model that can incorporate individual behaviors and interactions, while capturing large-scale features of the infection spread. We verify the accuracy of our model by comparing the prediction results with the traces of COVID-19 confirmed cases in several countries.
KW - agent-based model
KW - control policy
KW - COVID-19
KW - infection spread
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85139699848&partnerID=8YFLogxK
U2 - 10.1145/3492866.3557735
DO - 10.1145/3492866.3557735
M3 - Conference contribution
AN - SCOPUS:85139699848
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 247
EP - 252
BT - MobiHoc 2022 - Proceedings of the 2022 23rd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
PB - Association for Computing Machinery
T2 - 23rd ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2022
Y2 - 17 October 2022 through 20 October 2022
ER -