Simulation of pandemics in real cities: enhanced and accurate digital laboratories

A. Alexiadis, A. Albano, A. Rahmat, M. Yildiz, A. Kefal, M. Ozbulut, N. Bakirci, D. A. Garzón-Alvarado, C. A. Duque-Daza, J. H. Eslava-Schmalbach

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This study develops a modelling framework for simulating the spread of infectious diseases within real cities. Digital copies of Birmingham (UK) and Bogotá (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 Bogotá 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. [Abstract copyright: © 2021 The Authors.]
Original languageEnglish
Article number20200653
Number of pages28
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2245
Publication statusPublished - 27 Jan 2021

Bibliographical note

Funding Information:
Data accessibility. The code used for the simulations is freely available under the GNU General Public License v3 and can be downloaded from the University of Birmingham repository Competing interests. We declare we have no competing interests. Funding. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/S019227/1. The computations described in this paper were performed using (i) University of Birmingham’s BlueBEAR HPC service and (ii) Athena at HPC Midlands+ (funded by the EPSRC with grant no. EP/P020232/1). Acknowledgements. The authors would like to thank Prof. Mikhail Prokopenko (Centre for Complex Systems, University of Sydney), Prof. Philip Kuchel (School of Life and Environmental Sciences, University of Sydney) and Dr Lucy Gabriela Delgado (Public Health Laboratories Subdirectorate, Bogota’s District Health Secretary) for their advice and comments. The authors would also like to acknowledge the support with Athena of Dr Simon Branford at Birmingham’s IT Research Group. Simulation for the sensitivity and statistical analysis of the Bogotá model was carried out on the ARCHER UK National Supercomputing Service (http://www.

Publisher Copyright:
© 2021 The Authors.


  • COVID-19
  • discrete epidemiology
  • epidemiology
  • numerical modelling
  • particle-based methods

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)
  • Physics and Astronomy(all)


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