A complex systems approach to infectious disease surveillance and response

Benyun SHI; Shang Xia; Jiming LIU

doi:10.1007/978-3-319-02753-1_53

A complex systems approach to infectious disease surveillance and response

Benyun SHI
, Shang Xia
, Jiming LIU^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

6 Citations (Scopus)

Abstract

The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.

Original language	English
Title of host publication	Brain and Health Informatics - International Conference, BHI 2013, Proceedings
Pages	524-535
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-319-02753-1_53
Publication status	Published - 2013
Event	International Conference on Brain and Health Informatics, BHI 2013 - Maebashi, Japan Duration: 29 Oct 2013 → 31 Oct 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8211 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Brain and Health Informatics, BHI 2013
Country/Territory	Japan
City	Maebashi
Period	29/10/13 → 31/10/13

UN SDGs

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

SDG 3 Good Health and Well-being

User-Defined Keywords

Complex systems modeling
Data-driven computational intelligence
Policy-level decision making

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10.1007/978-3-319-02753-1_53

Cite this

SHI, B., Xia, S., & LIU, J. (2013). A complex systems approach to infectious disease surveillance and response. In Brain and Health Informatics - International Conference, BHI 2013, Proceedings (pp. 524-535). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8211 LNAI). https://doi.org/10.1007/978-3-319-02753-1_53

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title = "A complex systems approach to infectious disease surveillance and response",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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SHI, B, Xia, S & LIU, J 2013, A complex systems approach to infectious disease surveillance and response. in Brain and Health Informatics - International Conference, BHI 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8211 LNAI, pp. 524-535, International Conference on Brain and Health Informatics, BHI 2013, Maebashi, Japan, 29/10/13. https://doi.org/10.1007/978-3-319-02753-1_53

A complex systems approach to infectious disease surveillance and response. / SHI, Benyun; Xia, Shang; LIU, Jiming.
Brain and Health Informatics - International Conference, BHI 2013, Proceedings. 2013. p. 524-535 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8211 LNAI).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.

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KW - Data-driven computational intelligence

KW - Policy-level decision making

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A complex systems approach to infectious disease surveillance and response

Abstract

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UN SDGs

User-Defined Keywords

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