Digital cities and the spread of COVID-19: Characterizing the impact of non-pharmaceutical interventions in five cities in Spain

Front Public Health. 2023 Mar 23:11:1122230. doi: 10.3389/fpubh.2023.1122230. eCollection 2023.

Abstract

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

Keywords: COVID-19; digital twins; epidemic spreading; non-pharmaceutical interventions; pandemic control.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Cities
  • Humans
  • Models, Theoretical
  • SARS-CoV-2
  • Spain / epidemiology

Grants and funding

JR, AA, and YM acknowledge support from the Government of Aragon (FONDO–COVID19-UZ-164255). JR is supported by Juan de la Cierva Formacion program (Ref. FJC2019-040622-I) funded by MCIN/AEI/ 10.13039/501100011033. AA acknowledges support through the grant RYC2021-033226-I funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR. YM was partially supported by the Government of Aragon, Spain and ERDF A way of making Europe through grant E36-20R (FENOL), and by Ministerio de Ciencia e Innovación, Agencia Española de Investigación (MCIN/AEI/10.13039/501100011033) Grant No. PID2020-115800GB-I00. The authors acknowledge the use of the computational resources of COSNET Lab at Institute BIFI, funded by Banco Santander through grant Santander-UZ 2020/0274, and by the Government of Aragon (FONDO–COVID19-UZ-164255). JR and AA acknowledge funding from la Caixa Foundation under the project code SR20-00386 (COVID-SHINE). The funders had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.