Current Visitors:
H Shi, Q Tian, J Wang, and J Cheng
in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM'22)
With increased human mobility and the introduction of NPIs, the complex, dynamic spread of COVID-19 has diverged significantly from SEIR’s single, static assumption. At the same time, the ability to obtain front-line data also limits the modeling capabilities of SEIR. For researchers who cannot program, they must find suitable collaborators to implement their research. Even for researchers who can program, they need to repeat the principle and application process of the infectious disease model. LibEpidemic provide an open-source framework for modeling infectious disease, especially COVID-19, with bigdata. Researchers can implement subdivided, multi-stage or even metapopulation with the support of LibEpi- demic.
@inproceedings{shi2022libepidemic,
title={LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata},
author={Shi, Honghao and Tian, Qijian and Wang, Jingyuan and Cheng, Jiawei},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management}, pages={4980--4984},
year={2022}
}