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Background <p>The COVID-19 pandemic has significantly impacted human society at many levels, from public health to economics and transports, highlighting the need of approaches integrating all available information to better understand and model similar phenomena, also in order to develop early detection and responses.</p> Methods <p>In this paper we present an analysis of COVID-19 pandemic in the metropolitan area of Bologna, Italy, integrating an epidemiological mathematical model, SARS-CoV-2 virus quantification in wastewater, clinical hospitalization, vaccination campaign, virus genotypization and human mobility data in the period 2020-2022.</p> Results <p>We were able to follow the evolution of epidemic, observing the effect of vaccination and other factors that produced significant changes in hospitalizations. Moreover, by considering a mathematical model of COVID-19 epidemics spread, with parameters selected partly from literature and partly adapted to the local situation on a weekly basis, we identified a strict relation between human mobility at mesoscopic level and a sociability rate (related to model reproduction number).</p> Conclusion <p>Our results demonstrate the value of a interdisciplinary approach in monitoring and modeling epidemic trends. The observed relationships between mobility and sociability reveals the mutual impact of health issues on human activity and vice versa, providing insights for the implementation of effective response strategies in future pandemics.</p>

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Human mobility and sewage data correlate with COVID-19 epidemic evolution in a 3-year surveillance of the metropolitan area of Bologna

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Francesco Durazzi,
  • Enrico Lunedei,
  • Giulio Colombini,
  • Giulia Gatti,
  • Vittorio Sambri,
  • Alessandra De Cesare,
  • Cecilia Crippa,
  • Frédérique Pasquali,
  • Gastone Castellani,
  • Daniel Remondini,
  • Armando Bazzani

摘要

Background

The COVID-19 pandemic has significantly impacted human society at many levels, from public health to economics and transports, highlighting the need of approaches integrating all available information to better understand and model similar phenomena, also in order to develop early detection and responses.

Methods

In this paper we present an analysis of COVID-19 pandemic in the metropolitan area of Bologna, Italy, integrating an epidemiological mathematical model, SARS-CoV-2 virus quantification in wastewater, clinical hospitalization, vaccination campaign, virus genotypization and human mobility data in the period 2020-2022.

Results

We were able to follow the evolution of epidemic, observing the effect of vaccination and other factors that produced significant changes in hospitalizations. Moreover, by considering a mathematical model of COVID-19 epidemics spread, with parameters selected partly from literature and partly adapted to the local situation on a weekly basis, we identified a strict relation between human mobility at mesoscopic level and a sociability rate (related to model reproduction number).

Conclusion

Our results demonstrate the value of a interdisciplinary approach in monitoring and modeling epidemic trends. The observed relationships between mobility and sociability reveals the mutual impact of health issues on human activity and vice versa, providing insights for the implementation of effective response strategies in future pandemics.