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. 2017 Jan 9:7:40084.
doi: 10.1038/srep40084.

A new method for assessing the risk of infectious disease outbreak

Yilan Liao  1   2 , Bing Xu  1   3   4 , Jinfeng Wang  1   2 , Xiaochi Liu  1   5
Affiliations

A new method for assessing the risk of infectious disease outbreak

Yilan Liao et al. Sci Rep. .

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.

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Figures

Figure 1
Figure 1. The framework for using Bayesian Belief Network to assess the disease outbreak risk.
Figure 2
Figure 2. The Bayesian Belief Network used to assess HFMD outbreak risk.
Figure 3
Figure 3. The accuracy of the BBN and rough set method used to assess HFMD outbreak risk.
Figure 4
Figure 4. The Bayesian Belief Network used to assess measles outbreak risk.
Figure 5
Figure 5. The accuracy of the BBN and the rough set method used to assess measles outbreak risk.
Figure 6
Figure 6. ROC curve for Bayesian Belief Network model, Logistic Regression model and Rough Set method prediction averaged on each 30 replicate runs.
(The 1:1 line indicates the condition if the prediction is completely out of random chance (AUC = 0.5)).
See this image and copyright information in PMC

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