The project "Systems analysis of Patterns of COVID-19 spread in Europe and Russia" aims at developing an improved understanding of effectiveness of government policies in limiting the transmission of the COVID-19 virus in the population, and at assessing impacts of relevant socio-economic events on the dynamics of COVID-19 infections.

Deterministic models of infectious diseases are the main tool for informing policymakers about the likely results of various policy options aiming at slowing down the spread of COVID-19 virus in the population. Thus, the bulk of research is focused on improving and calibrating such prognostic models in order to increase accuracy of analyzed policy scenarios. Diagnostic models, which allow to retrospectively assess the effectiveness of implemented policies, were initially hampered by the lack of data and did not attracted much attention.

Yet, since the beginning of the COVID-19 pandemic sufficient data has been collected to draw conclusions about effectiveness of implemented policies. To support such assessment, in this project we assemble a data set containing: (1) information on the spread of COVID-19 in large cities of Europe and Russia; (2) information on important socio-economical events, e.g., lockdowns, vaccination campaigns or mass events; and (3) data on patterns of population movements (expressed in terms of the self-isolation index) which reflect how strictly the lockdown measures are observed.

To analyze the assembled data we develop a diagnostic time series model, the structure of which is inspired by the widely used SIR model of infectious disease spreading. This allows for meaningful interpretation of our results in terms of pandemic mechanics on the one hand, and, on the other hand, enables assessment of statistical significance of policy effects on the spread of COVID-19 virus in the population.