A New Method for Assessing the Risk of Infectious Disease Outbreak

Overview

Journal Sci Rep

Specialty Science

Date 2017 Jan 10

PMID 28067258

Citations 15

Authors

Yilan Liao

Bing Xu

Jinfeng Wang

Xiaochi Liu

Affiliations

Soon will be listed here.

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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