NDLI: Prediction of influenza epidemics at the province level in China using search query from “Haosou”

Content Provider	IEEE Xplore Digital Library
Author	Yuanqiang Zou Yousong Peng Li Lu Taijiao Jiang Lizong Deng
Copyright Year	2015
Description	Author affiliation: Suzhou Inst. of Syst. Med., Chinese Acad. of MedicalSciences, Beijing, China (Lizong Deng) \|\| Coll. of Comput. Sci. & Electron. Eng., Hunan Univ., Changsha, China (Yuanqiang Zou; Yousong Peng; Li Lu; Taijiao Jiang)
Abstract	Influenza (Flu) has caused and will continue to cause large morbidity and mortality to human society. Surveillance is critical for prevention and control of it. Traditional methods for influenza surveillance are based on hierarchical surveillance networks, which are costly and time-consuming. The Internet-based method is a useful supplement to traditional methods in influenza surveillance. Here, we attempted to predict the daily influenza epidemics, represented as the number of influenza-like illness (ILI) cases, in three representative provinces in China (Beijing, Shanghai and Guangdong) based on the “Haosou” search engine. The search queries for several keywords related to influenza were observed to have medium to weak correlations to influenza epidemics in these provinces. Two kinds of statistical model, the multiple linear regression and regression tree, were built to predict the influenza epidemics based on the search queries of these keywords. Both of them achieved moderate performance in the cross-validation and retrospective-testing for each province, with the Pearson Correlation Coefficients between the predicted and observed influenza epidemics ranging from 0.35 to 0.78. Further, the unified models were built for prediction of influenza epidemics for all three provinces. Their poor performances suggest the difficulty of building a unified model for prediction of influenza epidemics for all provinces in China. Overall, this work shows the potential of predicting the daily influenza epidemics at the province level in China based on the search engine data. More efforts are needed to improve the statistical models for their use in practical influenza surveillance.
Starting Page	1450
Ending Page	1454
File Size	1090729
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Surveillance search engine Linear regression Influenza Search engines Predictive models statistical modeling Internet influenza epidemic internet
Content Type	Text
Resource Type	Article

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

A neural netwok based approach to detect influenza epidemics using search engine query data

Web Query Prediction by Unifying Model

Business Cycle Indication Using Query Logs of Search Engines

Detecting influenza epidemics using search engine query data

Forecasting the Unemployment Rate by Neural Networks Using Search Engine Query Data

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease.

Epidemic models using Resource Prediction mechanism for optimal provision of multimedia services

Paid Search: Modeling Rank Dependent Behavior

Prediction of influenza epidemics at the province level in China using search query from “Haosou”

Similar Documents

A neural netwok based approach to detect influenza epidemics using search engine query data

Web Query Prediction by Unifying Model

Business Cycle Indication Using Query Logs of Search Engines

Detecting influenza epidemics using search engine query data

Forecasting the Unemployment Rate by Neural Networks Using Search Engine Query Data

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease.

Epidemic models using Resource Prediction mechanism for optimal provision of multimedia services

Paid Search: Modeling Rank Dependent Behavior

Prediction of influenza epidemics at the province level in China using search query from “Haosou”