Journal
INFECTION DISEASE & HEALTH
Volume 24, Issue 1, Pages 44-48Publisher
ELSEVIER INC
DOI: 10.1016/j.idh.2018.10.002
Keywords
Infectious diseases modelling; Emergency response; Artificial Intelligence; Machine learning
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Funding
- Japan Society for the Promotion of Science KAKENHI [18H03336]
- Research Grants Council Theme-Based Research Scheme [T32-102/14N]
- National Natural Science Foundation of China (NSFC) [71402157, 71672163]
- Grants-in-Aid for Scientific Research [18H03336] Funding Source: KAKEN
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Background: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place. Methods: This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age. Results and Conclusion: It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future. (C) 2018 Australasian College for Infection Prevention and Control. Published by Elsevier B.V. All rights reserved.
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