NDLI: Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model

Content Provider	Springer Nature : BioMed Central
Author	Zhou, Wanwan Huang, Daizheng Liang, Qiuyu Huang, Tengda Wang, Xiaomin Pei, Hengyan Chen, Shiwen Liu, Lu Wei, Yuxia Qin, Litai Xie, Yihong
Abstract	Background It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu’s search volume, in the early warning and predicting the epidemic trend of COVID-19. Methods The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable. Results The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February. Conclusion The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.
Related Links	https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-024-09940-7.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712334
DOI	10.1186/s12879-024-09940-7
Journal	BMC Infectious Diseases
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-19
Access Restriction	Open
Subject Keyword	Infectious Diseases Parasitology Medical Microbiology Tropical Medicine Internal Medicine COVID-19 Baidu search index Early warning Predicting Zero inflation negative binomial regression Negative binomial regression
Content Type	Text
Resource Type	Article
Subject	Infectious Diseases
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.3/2023

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

Early warning and monitoring of COVID-19 using the Baidu Search Index in China.

Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach

A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives

Application of longitudinal multilevel zero inflated Poisson regression in modeling of infectious diseases among infants in Ethiopia

Modelling and predicting the spatio-temporal spread of COVID-19 in Italy

Predicting progression to severe COVID-19 using the PAINT score

A novel method to detect the early warning signal of COVID-19 transmission

RT-PCR negative COVID-19

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model

Similar Documents

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

Early warning and monitoring of COVID-19 using the Baidu Search Index in China.

Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach

A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives

Application of longitudinal multilevel zero inflated Poisson regression in modeling of infectious diseases among infants in Ethiopia

Modelling and predicting the spatio-temporal spread of COVID-19 in Italy

Predicting progression to severe COVID-19 using the PAINT score

A novel method to detect the early warning signal of COVID-19 transmission

RT-PCR negative COVID-19

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model