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Data-Driven Epidemic Intelligence Strategies Based on Digital Proximity Tracing Technologies in the Fight against COVID-19 in Cities

Author

Listed:
  • Dario Esposito

    (Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Polytechnic University of Bari, 70125 Bari, Italy)

  • Giovanni Dipierro

    (Independent expert-consultant)

  • Alberico Sonnessa

    (Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Polytechnic University of Bari, 70125 Bari, Italy)

  • Stefania Santoro

    (Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Polytechnic University of Bari, 70125 Bari, Italy)

  • Simona Pascazio

    (Eunomia Studio di Avvocati, 70121 Bari, Italy)

  • Irene Pluchinotta

    (Institute for Environmental Design and Engineering, The Bartlett Faculty of the Built Environment, University College London, London WC1H 0NN, UK)

Abstract

In a modern pandemic outbreak, where collective threats require global strategies and local operational defence applications, data-driven solutions for infection tracing and forecasting epidemic trends are crucial to achieve sustainable and socially resilient cities. Indeed, the need for monitoring, containing, and mitigating the ongoing COVID-19 pandemic has generated a great deal of interest in Digital Proximity Tracing Technology (DPTT) on smartphones, as well as their function and effectiveness and insights of population acceptance. This paper introduces and compares different Data-Driven Epidemic Intelligence Strategies (DDEIS) developed on DPTTs. It aims to clarify to what extent DDEIS could be effective and both technologically and socially suitable in reaching the objective of a swift return to normality for cities, guaranteeing public health safety and minimizing the risk of epidemic resurgence. It assesses key advantages and limits in supporting both individual decision-making and policy-making, considering the role of human behaviour. Specifically, an online survey carried out in Italy revealed user preferences for DPTTs and provided preliminary data for an SEIR (Susceptible–Exposed–Infectious–Recovered) epidemiological model. This was developed to evaluate the impact of DDEIS on COVID-19 spread dynamics, and results are presented together with an evaluation of potential drawbacks.

Suggested Citation

  • Dario Esposito & Giovanni Dipierro & Alberico Sonnessa & Stefania Santoro & Simona Pascazio & Irene Pluchinotta, 2021. "Data-Driven Epidemic Intelligence Strategies Based on Digital Proximity Tracing Technologies in the Fight against COVID-19 in Cities," Sustainability, MDPI, vol. 13(2), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:644-:d:478609
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    References listed on IDEAS

    as
    1. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    2. Chung-Yuan Huang & Chuen-Tsai Sun & Ji-Lung Hsieh & Holin Lin, 2004. "Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(4), pages 1-2.
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    1. Przemysław Śleszyński & Paulina Legutko-Kobus & Mark Rosenberg & Viktoriya Pantyley & Maciej J. Nowak, 2022. "Assessing Urban Policies in a COVID-19 World," IJERPH, MDPI, vol. 19(9), pages 1-19, April.

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