Modeling and mining spatiotemporal patterns of infection risk from heterogeneous data for active surveillance planning

Bo Yang, Hua Guo, Yi Yang, Benyun SHI, Xiaonong Zhou, Jiming LIU*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Active surveillance is a desirable way to prevent the spread of infectious diseases in that it aims to timely discover individual incidences through an active searching for patients. However, in practice activc surveillance is difficult to implement especially when monitoring space is large but available resources are limited. Therefore, it is extremely important for public health authorities to know how to distribute their very sparse resources to high-priority regions so as to maximize the outcomes of active surveillance. In this paper, we raise the problem of active surveillance planning and provide an effective method to address it via modeling and mining spatiotemporal patterns of infection risks from heterogeneous data sources. Taking malaria as an example, we perform an empirical study on real-world data to validate our method and provide our new findings.

Original languageEnglish
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
PublisherAAAI press
Pages493-499
Number of pages7
ISBN (Electronic)9781577356776
ISBN (Print)9781577356615, 9781577356776
Publication statusPublished - Jul 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec, Canada, Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/TerritoryCanada
CityQuebec City
Period27/07/1431/07/14

Scopus Subject Areas

  • Software
  • Artificial Intelligence

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