LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata

Honghao Shi; Qijian Tian; Jingyuan Wang; Jiawei Cheng

doi:10.1145/3511808.3557183

LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata

Honghao Shi
, Qijian Tian
, Jingyuan Wang^*
, Jiawei Cheng

^*Corresponding author for this work

School of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 LibEpidemic.

Original language	English
Title of host publication	CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	4980-4984
Number of pages	5
ISBN (Electronic)	9781450392365
DOIs	https://doi.org/10.1145/3511808.3557183
State	Published - 17 Oct 2022
Event	31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States Duration: 17 Oct 2022 → 21 Oct 2022

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
ISSN (Print)	2155-0751

Conference

Conference	31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/Territory	United States
City	Atlanta
Period	17/10/22 → 21/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

bigdata embedding
epidemic modeling
no-code
open-source framework

Access to Document

10.1145/3511808.3557183

Cite this

Shi, H., Tian, Q., Wang, J., & Cheng, J. (2022). LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata. In CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management (pp. 4980-4984). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery . https://doi.org/10.1145/3511808.3557183

Shi, Honghao ; Tian, Qijian ; Wang, Jingyuan et al. / LibEpidemic : An Open-source Framework for Modeling Infectious Disease with Bigdata. CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Association for Computing Machinery , 2022. pp. 4980-4984 (International Conference on Information and Knowledge Management, Proceedings).

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title = "LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata",

abstract = "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 LibEpidemic.",

keywords = "bigdata embedding, epidemic modeling, no-code, open-source framework",

author = "Honghao Shi and Qijian Tian and Jingyuan Wang and Jiawei Cheng",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 ; Conference date: 17-10-2022 Through 21-10-2022",

year = "2022",

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language = "英语",

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Shi, H, Tian, Q, Wang, J & Cheng, J 2022, LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata. in CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery , pp. 4980-4984, 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, Atlanta, United States, 17/10/22. https://doi.org/10.1145/3511808.3557183

LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata. / Shi, Honghao; Tian, Qijian; Wang, Jingyuan et al.
CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Association for Computing Machinery , 2022. p. 4980-4984 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - LibEpidemic

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AU - Shi, Honghao

AU - Tian, Qijian

AU - Wang, Jingyuan

AU - Cheng, Jiawei

PY - 2022/10/17

Y1 - 2022/10/17

N2 - 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 LibEpidemic.

AB - 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 LibEpidemic.

KW - bigdata embedding

KW - epidemic modeling

KW - no-code

KW - open-source framework

UR - https://www.scopus.com/pages/publications/85140832766

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DO - 10.1145/3511808.3557183

M3 - 会议稿件

AN - SCOPUS:85140832766

T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 4980

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BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery

Y2 - 17 October 2022 through 21 October 2022

ER -

Shi H, Tian Q, Wang J, Cheng J. LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata. In CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Association for Computing Machinery . 2022. p. 4980-4984. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/3511808.3557183

LibEpidemic: An Open-source Framework for Modeling Infectious Disease with Bigdata

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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