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Usefulness of open data to determine the incidence of COVID-19 and its relationship with atmospheric variables in Spain during the 2020 lockdown

Author

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  • Zubcoff, Jose-Jacobo
  • Olcina, Jorge
  • Morales, Javier
  • Mazón, Jose-Norberto
  • Mayoral, Asunción M.

Abstract

The SARS-CoV-2 pandemic and the spread of the COVID-19 disease led to a lockdown being imposed in Spain to minimise contagion from 16 March 2020 to 1 May 2020. Over this period, measures were taken to reduce population mobility (a key factor in disease transmission). The scenario thus created enabled us to examine the impact of factors other than mobility (in this case, meteorological conditions) on the incidence of the disease, and thus to identify which environmental variables played the biggest role in the pandemic's evolution. Worthy of note, the data required to perform the study was entirely extracted from governmental open data sources. The present work therefore demonstrates the utility of such data to conduct scientific research of interest to society, leading to studies that are also fully reproducible. The results revealed a relationship between temperatures and the spread of COVID-19. The trend was that of a slightly lower disease incidence as the minimum temperature rises, i.e. the lower the minimum temperature, the greater the number of cases. Furthermore, a link was found between the incidence of the disease and other variables, such as altitude and proximity to the sea. There were no indications, however, in the study's data, of a relationship between incidence and precipitation or wind.

Suggested Citation

  • Zubcoff, Jose-Jacobo & Olcina, Jorge & Morales, Javier & Mazón, Jose-Norberto & Mayoral, Asunción M., 2023. "Usefulness of open data to determine the incidence of COVID-19 and its relationship with atmospheric variables in Spain during the 2020 lockdown," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    • Handle: RePEc:eee:tefoso:v:186:y:2023:i:pa:s0040162522006291
    DOI: 10.1016/j.techfore.2022.122108
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    References listed on IDEAS

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    1. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    2. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Miranda, L.C.M. & Devezas, Tessaleno, 2022. "On the global time evolution of the Covid-19 pandemic: Logistic modeling," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Sahoo, Bijay Kumar & Sapra, Balvinder Kaur, 2020. "A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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