IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v186y2023ipas0040162522006291.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522006291
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.122108?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    4. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Su, Dan & Zhang, Lijun & Peng, Hua & Saeidi, Parvaneh & Tirkolaee, Erfan Babaee, 2023. "Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    4. Behl, Abhishek & Jayawardena, Nirma & Ishizaka, Alessio & Gupta, Manish & Shankar, Amit, 2022. "Gamification and gigification: A multidimensional theoretical approach," Journal of Business Research, Elsevier, vol. 139(C), pages 1378-1393.
    5. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    6. Du, Juntao & Shen, Zhiyang & Song, Malin & Zhang, Linda, 2023. "Nexus between digital transformation and energy technology innovation: An empirical test of A-share listed enterprises," Energy Economics, Elsevier, vol. 120(C).
    7. Broccardo, Laura & Zicari, Adrián & Jabeen, Fauzia & Bhatti, Zeeshan A., 2023. "How digitalization supports a sustainable business model: A literature review," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    8. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    9. Li, Ding & Gao, Ming & Hou, Wenxuan & Song, Malin & Chen, Jiandong, 2020. "A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach," Energy Policy, Elsevier, vol. 147(C).
    10. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    11. Xie, Xuemei & Wu, Yonghui & Palacios-Marqués, Daniel & Ribeiro-Navarrete, Samuel, 2022. "Business networks and organizational resilience capacity in the digital age during COVID-19: A perspective utilizing organizational information processing theory," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    12. Chen, Jiandong & Xie, Qiaoli & Shahbaz, Muhammad & Song, Malin & Li, Li, 2022. "Impact of bilateral trade on fossil energy consumption in BRICS: An extended decomposition analysis," Economic Modelling, Elsevier, vol. 106(C).
    13. Md. Mokhlesur Rahman & Jean-Claude Thill, 2022. "Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries," IJERPH, MDPI, vol. 19(12), pages 1-31, June.
    14. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    15. Xinxin Ma, 2023. "Impact of Long Working Hours on Mental Health: Evidence from China," IJERPH, MDPI, vol. 20(2), pages 1-13, January.
    16. Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).
    17. Godé, Cécile & Brion, Sébastien, 2024. "The affordance-actualization process of predictive analytics: Towards a configurational framework of a predictive policing system," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    18. Shaker Salem Abuzawida & Ahmad Bassam Alzubi & Kolawole Iyiola, 2023. "Sustainable Supply Chain Practices: An Empirical Investigation from the Manufacturing Industry," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    19. Chen, Yantai & Luo, Haibei & Chen, Jin & Guo, Yanlin, 2022. "Building data-driven dynamic capabilities to arrest knowledge hiding: A knowledge management perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 1138-1154.
    20. Ahmadova, Gozal & Delgado-Márquez, Blanca L. & Pedauga, Luis E. & Leyva-de la Hiz, Dante I., 2022. "Too good to be true: The inverted U-shaped relationship between home-country digitalization and environmental performance," Ecological Economics, Elsevier, vol. 196(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:186:y:2023:i:pa:s0040162522006291. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.