Alexiadis, A.Albano, A.Rahmat, A.Yildiz, M.Kefal, A.Ozbulut, M.Bakirci, N.Garzon-Alvarado, D. A.Duque-Daza, C. A.Eslava-Schmalbach, J. H.2023-02-212023-02-212021-01-0110.1098/rspa.2020.0653https://hdl.handle.net/11443/2350http://dx.doi.org/10.1098/rspa.2020.0653This study develops a modelling framework for simulating the spread of infectious diseases within real cities. Digital copies of Birmingham (UK) and Bogota (Colombia) are generated, reproducing their urban environment, infrastructure and population. The digital inhabitants have the same statistical features of the real population. Their motion is a combination of predictable trips (commute to work, school, etc.) and random walks (shopping, leisure, etc.). Millions of individuals, their encounters and the spread of the disease are simulated by means of high-performance computing and massively parallel algorithms for several months and a time resolution of 1 minute. Simulations accurately reproduce the COVID-19 data for Birmingham and Bogota both before and during the lockdown. The model has only one adjustable parameter calculable in the early stages of the pandemic. Policymakers can use our digital cities as virtual laboratories for testing, predicting and comparing the effects of policies aimed at containing epidemics.epidemiologyCOVID-19numerical modellingparticle-based methodsdiscrete epidemiologyArticleWOS:000613719800001