TY - GEN
T1 - Modeling and mining spatiotemporal patterns of infection risk from heterogeneous data for active surveillance planning
AU - Yang, Bo
AU - Guo, Hua
AU - Yang, Yi
AU - SHI, Benyun
AU - Zhou, Xiaonong
AU - LIU, Jiming
N1 - Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014/7
Y1 - 2014/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84908216152&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84908216152
SN - 9781577356615
SN - 9781577356776
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 493
EP - 499
BT - Proceedings 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
PB - AAAI press
T2 - 28th 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
Y2 - 27 July 2014 through 31 July 2014
ER -