DIGITAL EPIDEMIOLOGY IN CENTRAL ASIA: USING SEARCH DATA TO MONITOR INFLUENZA-LIKE ILLNESS TRENDS - Makhmudova Aktoty Meirzhankyzy (International Journal of Innovative Technologies in Social Science, 2025)
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.