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Sci Rep. 2017 Jan 9;7:40084. doi: 10.1038/srep40084.

A new method for assessing the risk of infectious disease outbreak.

Liao Y1,2, Xu B1,3,4, Wang J1,2, Liu X1,5.

Author information

1
The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
2
The Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
3
Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100190, China.
4
Sino-Danish Center for Education and Research, Beijing, 100190, China.
5
School of Information Engineering, China University of Geosciences, Beijing 100083, China.

Abstract

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.

PMID:
28067258
PMCID:
PMC5220355
DOI:
10.1038/srep40084
[Indexed for MEDLINE]
Free PMC Article

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