DIGITAL EPIDEMIOLOGY IN CENTRAL ASIA: USING SEARCH DATA TO MONITOR INFLUENZA-LIKE ILLNESS TRENDS
Abstract
Seasonal influenza continues to pose a substantial burden on health systems worldwide, with an estimated 1 billion infections each year, including 3-5 million severe cases and hundreds of thousands of deaths. In Central Asia, this viral landscape is further complicated by the co circulation of multiple respiratory pathogens, heterogeneous climates and unequal access to laboratory diagnostics. At the same time, internet penetration and smartphone use have grown rapidly across the region, creating dense streams of search queries and other digital traces that potentially mirror population level concern about respiratory symptoms. Digital epidemiology uses such nontraditional data streams to complement, rather than replace, established surveillance networks. This article develops a regional framework for harnessing web search data to track influenza-like illness trends in Central Asia in close alignment with existing laboratory-based systems. The approach integrates global experience from search-based influenza surveillance with the specific institutional, linguistic and infrastructural features of Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan. The results present a structured set of design outcomes: a data source matrix, a multilingual query taxonomy, and a maturity index for integrating digital indicators into public health decision making. The article concludes that search data can enrich influenza-like illness surveillance in Central Asia if embedded in transparent analytic workflows, governed by robust ethical safeguards and continuously validated against clinical data.
References
Shih, D. H., Wu, Y. H., Wu, T. W., Chang, S. C., & Shih, M. H. (2024). Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance. Journal of Clinical Medicine, 13(7), 1946. https://www.mdpi.com/2077-0383/13/7/1946
World Health Organization. (2023). Global Influenza Surveillance and Response System (GISRS). Retrieved from https://www.who.int/initiatives/global-influenza-surveillance-and-response-system
Klivleyeva, N., Lukmanova, G., Glebova, T., Shamenova, M., Ongarbayeva, N., Saktaganov, N., Baimukhametova, A., Baiseiit, S., Ismagulova, D., Kassymova, G., et al. (2023). Spread of pathogens causing respiratory viral diseases before and during COVID-19 pandemic in Kazakhstan. Indian Journal of Microbiology, 63(1), 129-138. https://doi.org/10.1007/s12088-023-01064-x
Usmanova Z. A., Kurbanov B. J. (2025). Application of sentinel epidemiological surveillance in influenza and ARVI monitoring: Comparative analysis of experience, effectiveness principles, and criteria in neighboring and foreign countries. Bulletin of the Association of Pulmonologists of Central Asia, 12(7), 217-222. https://journals.tnmu.uz/index.php/bapca/article/view/2316
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., et al. (2012). Digital epidemiology. PLOS Computational Biology, 8(7), e1002616. https://doi.org/10.1371/journal.pcbi.1002616
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014. https://doi.org/10.1038/nature07634
Cho S., Sohn C. H., Jo M. W., Shin S. Y., Lee J. H., Ryoo S. M., Kim W. Y. Correlation between national influenza surveillance data and Google Trends in South Korea // PLOS ONE. 2013. Vol. 8. No. 12. e81422 https://pmc.ncbi.nlm.nih.gov/articles/PMC3855287/
Santillana M., Nguyen A. T., Dredze M., Paul M. J., Nsoesie E. O., Brownstein J. S. Combining search, social media, and traditional data sources to improve influenza surveillance // PLOS Computational Biology. 2015. Vol. 11. No. 10. e1004513. https://doi.org/10.1371/journal.pcbi.1004513
Yuan Q., Nsoesie E. O., Lv B., Peng G., Chunara R., Brownstein J. S. Monitoring influenza epidemics in China with search engine query data // Journal of Medical Internet Research. 2013. Vol. 15. No. 11. e206. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064323
Nsoesie E. O., Oladeji O., Abah Abah A. S., Ndeffo Mbah M. L. Forecasting influenza-like illness trends in Cameroon using Google Search Data // Scientific Reports. 2021. Vol. 11. Article 6713. https://www.nature.com/articles/s41598-021-85987-9
Momynaliev, K. T., Khoperskaya, L. L., Pshenichnaya, N. Yu., Abuova, G. N., & Akimkin, V. G. (2021). Infodemiological study of coronavirus epidemic using Google Trends in Central Asian Republics of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan. Medical Alphabet, (34), 47-53. https://doi.org/10.33667/2078-5631-2020-34-47-53
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203-1205. https://doi.org/10.1126/science.1248506
Althouse, B. M., Scarpino, S. V., Meyers, L. A., Ayers, J. W., Bargsten, M., Baumbach, J., et al. (2015). Enhancing disease surveillance with novel data streams: Challenges and opportunities. EPJ Data Science, 4(1), 17. https://doi.org/10.1140/epjds/s13688-015-0054-0
Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google Trends research: Systematic review. Journal of Medical Internet Research, 20(11), e270. https://doi.org/10.2196/jmir.9366
Nsoesie, E. O., Oladeji, O., Celik, C., Akinwumi, R., & Davila, J. (2021). Forecasting influenza-like illness trends in Cameroon using Google search data. Scientific Reports, 11(1), 6713. https://doi.org/10.1038/s41598-021-85987-9
Shih, D.-H., Wu, Y.-H., Wu, T.-W., Chang, S.-C., & Shih, M.-H. (2024). Infodemiology of influenza-like illness: Utilizing Google Trends' big data for epidemic surveillance. Journal of Clinical Medicine, 13(7), 1946. https://doi.org/10.3390/jcm13071946
Glebova, T., Klivleyeva, N., Baimukhametova, A., Lukmanova, G., Saktaganov, N., Ongarbayeva, N., Baimakhanova, B., Kassymova, G., Sagatova, M., Rachimbayeva, A., Zhanuzakova, N., Naidenova, T., Rakhmonova, N., & Webby, R. (2025). Acute respiratory and influenza viruses circulating in Kazakhstan during 2018-2024. Pathogens, 14(5), 493. https://doi.org/10.3390/pathogens14050493
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