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Using internet search data to predict new HIV diagnoses in China: a modelling study.

Qingpeng Zhang1,2, Yi Chai1,3, Xiaoming Li4

  • 1Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong SAR, China.

BMJ Open
|October 20, 2018
PubMed
Summary

Internet search data can predict new HIV diagnoses in China. This study shows using Baidu search queries improves forecasting and can aid HIV prevention efforts.

Keywords:
health informaticsinternetpredictive modelsearch querysurveillance

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Internet searches offer valuable data on HIV epidemics and risk factors.
  • Previous studies indicate a link between internet search trends and infectious disease outbreaks, including HIV.

Purpose of the Study:

  • To assess the feasibility of using internet search query data for predicting new Human Immunodeficiency Virus (HIV) diagnoses in China.
  • To identify specific search queries associated with HIV diagnoses in China.

Main Methods:

  • Utilized Baidu search query data and official HIV diagnosis statistics from 2010 to 2016 for China and Guangdong province.
  • Developed statistical models, including negative binomial generalized linear models and their Bayesian variations, to estimate new HIV diagnoses.

Main Results:

  • A positive association was found between internet search query volume and the number of new HIV diagnoses in China and Guangdong province.
  • Incorporating search query data significantly enhanced the accuracy of nowcasting and forecasting models for HIV diagnoses.

Conclusions:

  • Baidu search data effectively predicts new HIV diagnoses at both national and provincial levels in China.
  • This approach demonstrates the potential of leveraging internet search data to complement traditional HIV surveillance and inform public health decision-making for prevention programs.