NDLI: Real-time risk ranking of emerging epidemics based on optimized moving average prediction limit—taking the COVID-19 pandemic as an example

Content Provider	Springer Nature : BioMed Central
Author	Wang, Zhongliang Liu, Bin Luan, Jie Lu, Shanshan Zhang, Zhijie Ba, Jianbo
Abstract	Background Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time. Methods The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing. Results The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium–high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually. Conclusions The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-023-15835-0.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-023-15835-0
Journal	BMC Public Health
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-06-01
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Op-MAPL Risk ranking Math model COVID-19 SARS-CoV-2 Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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