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

TL;DR

This study develops a detailed agent-based model to analyze how various non-pharmaceutical interventions affected COVID-19 spread in five Spanish cities, aiding policy decisions with scenario simulations.

Contribution

It introduces a high-resolution, data-driven agent-based modeling framework tailored to specific cities, enabling quantitative assessment of intervention impacts.

Findings

Interventions significantly reduced COVID-19 transmission.

Model accurately predicts outbreak dynamics under different scenarios.

Framework can inform targeted policy measures.

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.