Original Article

International Journal of Public Health

, Volume 59, Issue 5, pp 841-849

First online:

Demand-based web surveillance of sexually transmitted infections in Russia

  • Alexander DomnichAffiliated withDepartment of Health Sciences, University of Genoa Email author 
  • , Eva K. ArbuzovaAffiliated withState Budgetary Healthcare Institution “Specialized Clinical Hospital for Infectious Diseases”, Healthcare Department of Krasnodar Region
  • , Alessio SignoriAffiliated withDepartment of Health Sciences, University of Genoa
  • , Daniela AmiciziaAffiliated withDepartment of Health Sciences, University of Genoa
  • , Donatella PanattoAffiliated withDepartment of Health Sciences, University of Genoa
  • , Roberto GaspariniAffiliated withDepartment of Health Sciences, University of Genoa

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Abstract

Objectives

To investigate the possibility of using HIV- and syphilis-related web queries to predict incident diagnosis rates of sexually transmitted infections in Russia.

Methods

The regional volume of HIV/syphilis queries, normalized to the total number of queries submitted to the most popular search engine, was used to predict the notification rates of HIV/syphilis in each region by applying both global non-spatial and spatial statistics.

Results

Nationwide, both search volumes and regional HIV/syphilis diagnosis rates were positively spatially auto-correlated, indicating a clustered pattern of spatial distribution. A high positive correlation between notification rates and search volume was observed. Compared with linear models, spatially explicit geographically weighted models adjusted for broadband Internet diffusion proved superior in predicting the regional level of the HIV/syphilis epidemic on the basis of their search volume.

Conclusions

Timeliness, easy availability, low cost, and transparency make HIV- and syphilis-related web queries a promising addition to traditional methods of disease surveillance in Russia. Geographically weighted regression provides useful insights, as it is able to capture the spatial heterogeneity of the relationship between search volume and disease incidence.

Keywords

Disease surveillance HIV Search engine Search volume Infodemiology