A new method for assessing the risk of infectious disease outbreak

Yilan Liao*, Bing Xu, Jinfeng Wang, Xiaochi Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)
20 Downloads (Pure)


Over the past few years, emergent threats posed by infectious diseases and bioterrorism have become public health concerns that have increased the need for prompt disease outbreak warnings. In most of the existing disease surveillance systems, disease outbreak risk is assessed by the detection of disease outbreaks. However, this is a retrospective approach that impacts the timeliness of the warning. Some disease surveillance systems can predict the probabilities of infectious disease outbreaks in advance by determining the relationship between a disease outbreak and the risk factors. However, this process depends on the availability of risk factor data. In this article, we propose a Bayesian belief network (BBN) method to assess disease outbreak risks at different spatial scales based on cases or virus detection rates. Our experimental results show that this method is more accurate than traditional methods and can make uncertainty estimates, even when some data are unavailable.

Original languageEnglish
Article number40084
Pages (from-to)1-12
Number of pages12
JournalScientific Reports
Publication statusPublished - 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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