Explaining COVID-19 dynamics through user activity data from digital platforms with Yandex’s self-isolation index as a case study

21005 6 111799 2 337847 document 111799 s41598-025-24240-z.pdf application/pdf d4af884742877f50236b545929be4a94 MD5 3974052 2025-11-20 14:06:10 https://pure.iiasa.ac.at/id/eprint/21005/1/s41598-025-24240-z.pdf 21005 1 1 application/pdf text en public cc_by_nc_nd_4

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published 111801 1 337861 document 111801 indexcodes.txt text/x-c 71fcd3970bf82106c5a1c43afbb4d05b MD5 15279 2025-11-20 14:16:12 https://pure.iiasa.ac.at/id/eprint/21005/2/indexcodes.txt 21005 2 2 text/x-c other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/111799 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/111799 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/111799 archive 353 disk0/00/02/10/05 2025-11-20 14:09:43 2025-11-20 14:09:43 2025-11-20 14:09:43 article show Żebrowski P. 2086 0000-0001-5283-8049 Boyarshinov G. Odintsova A. Rovenskaya E. 8206 0000-0002-2761-3443 Explaining COVID-19 dynamics through user activity data from digital platforms with Yandex’s self-isolation index as a case study pub ASA EM Autoregressive distributed lag models; COVID-19 dynamics; Cointegration; Digital platforms; Social distancing monitoring Social-distancing measures were among the very few available policy responses to the initial outbreak of COVID-19, and they remain an important tool for containing recurring wavers of this and possible future pandemics. However, policies aiming at limiting the intensity of people-to-people contacts incur substantial socio-economic costs while their effectiveness varies over time and across locations. Having a robust way of measuring the level of people-to-people contacts and monitoring compliance with social-distancing policies would greatly aid governments in better calibrating their responses to future pandemic outbreaks. In this paper we use the case example of the Yandex's self-isolation index to explore the potential of composite indices that aggregate multiple sources of activity data collected by digital platforms as proxies for evaluating the people-to-people contact intensity. To this end, we propose two error-corrected autoregressive distributed-lag models, inspired by the classical SIR model of infectious disease dynamics, and use them in testing for cointegration between the self-isolation index and the official data on the numbers of new COVID-19 cases and deaths, for the two largest cities in Russia, Moscow and St. Petersburg. We have found evidence for such cointegration, which confirms that the COVID-19 epidemic curve can be explained by the level of people-to-people contact intensity as measured by the self-isolation index. Our findings suggest that the self-isolation index is a useful real-time indicator of the level of compliance with social distancing measures in the population and thus can serve as a reliable tool for informing policymaking. 2025-11-19 published NPG doi:10.1038/s41598-025-24240-z 343 260 public Scientific Reports 15 1 e40709 TRUE 2045-2322 info:eu-repo/semantics/article