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Research Article
Yan Pang
Department of Analytics and Operations, National University of Singapore, 119613, SG
Corresponding Author
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Influenza is an infectious disease that leads to an estimated 5 million severe illness cases and 650,000 respiratory deaths worldwide each year. Early detection and prediction of influenza outbreaks are crucial to efficient resource planning to save patients’ lives and healthcare costs. This paper proposes a novel data-driven methodology for influenza outbreaks detection and prediction. The doctor’s diagnosis-based prescription dataset of Influenza-Like Illness (ILI) from more than 3,000 clinics in Malaysia is used in this study because the prescription data are reliable and can be captured timely. A new Region Index (RI) of the influenza outbreak is proposed based on the prescription dataset. With the newly proposed RI metric, statistical and machine learning models are developed to detect and predict influenza outbreaks. Cross-validation is conducted to evaluate the prediction model performance. The proposed methods are also validated by real-world evidence. It is proved to be sensitive and accurate in influenza outbreak prediction with 80-90% accuracy, 70-80% recall, and 70-80% precision scores.
Keywords
Influenza outbreak detection, Influenza outbreak prediction, Public health, Machine learning, Data-driven method
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CURRENT STATUS: Under Review
Version 1
Posted 24 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 18 Jan, 2021
Reviews received
Received 16 Jan, 2021
Reviews received
Received 16 Jan, 2021
Reviewers agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 02 Dec, 2020
Editor assigned
On 02 Dec, 2020
Editor invited
On 17 Nov, 2020
Submission checks complete
On 17 Nov, 2020
First submitted
On 13 Nov, 2020
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Yan Pang
Department of Analytics and Operations, National University of Singapore, 119613, SG
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 24 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 18 Jan, 2021
Reviews received
Received 16 Jan, 2021
Reviews received
Received 16 Jan, 2021
Reviewers agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 02 Dec, 2020
Editor assigned
On 02 Dec, 2020
Editor invited
On 17 Nov, 2020
Submission checks complete
On 17 Nov, 2020
First submitted
On 13 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Influenza is an infectious disease that leads to an estimated 5 million severe illness cases and 650,000 respiratory deaths worldwide each year. Early detection and prediction of influenza outbreaks are crucial to efficient resource planning to save patients’ lives and healthcare costs. This paper proposes a novel data-driven methodology for influenza outbreaks detection and prediction. The doctor’s diagnosis-based prescription dataset of Influenza-Like Illness (ILI) from more than 3,000 clinics in Malaysia is used in this study because the prescription data are reliable and can be captured timely. A new Region Index (RI) of the influenza outbreak is proposed based on the prescription dataset. With the newly proposed RI metric, statistical and machine learning models are developed to detect and predict influenza outbreaks. Cross-validation is conducted to evaluate the prediction model performance. The proposed methods are also validated by real-world evidence. It is proved to be sensitive and accurate in influenza outbreak prediction with 80-90% accuracy, 70-80% recall, and 70-80% precision scores.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The full text of this article is available to read as a PDF.
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