Skip to main content
SpringerLink
Log in

The Influence of Stringency Measures and Socio-Economic Data on COVID-19 Outcomes

  • Conference paper
  • First Online:
Mediterranean Forum – Data Science Conference (MeFDATA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1343))

Included in the following conference series:

Abstract

The COVID-19 pandemic has changed the way that society functions. The state governments have chosen different measures to respond to the spread of the virus as there was no clear evidence on what the best way is to respond. In this paper, we gather the evidence of governments’ stringency measures and couple it with countries’ socio-economic data (including the amounting of tests that each country performed) to explore how they influenced the COVID-19 spread. We evaluated how well seven different regression models can predict the spread of the virus. The spread of the virus is expressed through the number of positively identified individuals as well as deaths reported due to the virus. For the prediction of the number of new cases, ElasticNet algorithm was the best performing, followed by Linear Regression and LASSO. LASSO algorithms predicted the best number of new deaths. The metrics used to evaluate were: R-squared score, Adjusted R-squared Score, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The most effective measure was the stay-at-home measure followed by workplace and school closures. With the availability of new and more accurate data and variables, the models can be further improved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmed ben, S., Abdelkarim, E., Hussein, A., Abdelmonem, M.: A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases. arXiv:2009.05481v1 (2020)

  2. Islind, A.S., Mar√ ≠ a, √ì., Harpa, S.: Changes in mobility patterns in Europe during the COVID-19 pandemic: Novel insights using open source data. arXiv:2008.10505 (2020)

  3. Ellen, K.: Data-driven modeling of COVID-19-Lessons learned. Extreme Mech. Lett. 40, 100921 (2020). ISSN 2352-4316

    Google Scholar 

  4. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/covid-19-pandemic. Accessed 29 Sep 2020

  5. GBD 2017 Risk Factor Collaborators: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1923–1994 (2018). https://doi.org/10.1016/S0140-6736(18)32225-6

    Article  Google Scholar 

  6. Gutierrez, D.D.: Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R. Technics Publications, New Jersey (2015)

    Google Scholar 

  7. Hannah, R., et al.: Coronavirus Pandemic (COVID-19). Our World in Data (2020). (https://ourworldindata.org/coronavirus)

  8. He, P.: Study on epidemic prevention and control strategy of COVID -19 based on personnel flow prediction. In: 2020 International Conference on Urban Engineering and Management Science (ICUEMS), pp. 688–691. Zhuhai, China (2020)

    Google Scholar 

  9. Hsieh, W.W.: Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  10. International Standardization Organization [ISO]: ISO 3166-1: 2020(en) Codes for the representation of names of countries and their subdivisions—Part 1: Country code (2020)

    Google Scholar 

  11. James, C.R.: Estimates of regional and global life expectancy, 1800–2001. Popul. Dev. Rev. 31(3), 537–543 (2005)

    Google Scholar 

  12. Kamran, S., Ghader, R.: A New Dynamic Model to Predict the Effects of Governmental Decisions on the Progress of the CoViD-19 Epidemic. arXiv:2008.11716 (2020)

  13. Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W.: Applied Linear Statistical Models. McGraw-Hill Irwin, New York (2005)

    Google Scholar 

  14. Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Tyson, G., et al.: Leveraging data science to combat COVID-19: a comprehensive review. TechRxiv. Preprint (2020)

    Google Scholar 

  15. Nikolopoulos, K., et al.: Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research (2020). 10.1016/j.ejor.2020.08.001

    Google Scholar 

  16. Paiva, H.M., Afonso, R.J.M., de Oliveira, I.L., Garcia, G.F.: A data-driven model to describe and forecast the dynamics of COVID-19 transmission. Plos One 15(7), e0236386 (2020)

    Article  Google Scholar 

  17. Rustam, F., et al.: COVID-19 future forecasting using supervised machine learning models. IEEE Access 8, 101489–101499 (2020)

    Article  Google Scholar 

  18. Sina, F., et al.: COVID-19 outbreak prediction with machine learning. medRxiv 2020.04.17.20070094 (2020)

    Google Scholar 

  19. Thomas, H., et al.: Variation in government responses to COVID-19. Oxford COVID-19 Government Response Tracker, Blavatnik School of Government (2020)

    Google Scholar 

  20. United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects: The 2017 Revision. https://population.un.org/wpp/Publications/Files/WPP2017_DataBooklet.pdf. Accessed 28 Sep 2020

  21. United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects: The 2019 Revision. https://population.un.org/wpp/. Accessed 28 Sep 2020

  22. Yves, M.S., Mintodê, N.A.: The influence of passenger air traffic on the spread of COVID-19 in the world. Transp. Res. Interdisc. Perspect. 8, 100213 (2020). ISSN 2590-1982

    Google Scholar 

  23. World Bank. https://data.worldbank.org/. Accessed 29 Sep 2020

  24. Worldometer. http://web.archive.org/web/*/https://www.worldometers.info/coronavirus/. Accessed 29 Sep 2020

Download references

Author information

Authors and Affiliations

  1. University Sarajevo School of Science and Technology, Hrasnicka Cesta 3a, Sarajevo, Bosnia and Herzegovina

    Azra Musić, Jasminka Hasić Telalović & Dženita Đulović

Authors
  1. Azra Musić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jasminka Hasić Telalović
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dženita Đulović
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jasminka Hasić Telalović .

Editor information

Editors and Affiliations

  1. University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina

    Jasminka Hasic Telalovic

  2. University of Louisville, Louisville, KY, USA

    Mehmed Kantardzic

Appendix

Appendix

The figures fellow visualize changes in levels of measures applied over time for top five countries with the highest number of cases and deaths (Spain- ESP, France- FRA, Great Britain - GBR, Germany DEU, Italy - ITA) and countries of Western Balkans (Albania – ALB, Bosnia and Herzerovina – BiH, Croatia – HRV, Serbia - SRB) (Figs. 5–15).

Fig. 5.
figure 5

A.1 Testing policy

Full size image
Fig. 6.
figure 6

A.2 Contact tracing

Full size image
Fig. 7.
figure 7

A.3 Internal movement

Full size image
Fig. 8.
figure 8

A.4 International movement

Full size image
Fig. 9.
figure 9

A.5 Public campaigns

Full size image
Fig. 10.
figure 10

A.6 Public events

Full size image
Fig. 11.
figure 11

A.7 Public athering

Full size image
Fig. 12.
figure 12

A.8 Public transport

Full size image
Fig. 13.
figure 13

A.9 School closures

Full size image
Fig. 14.
figure 14

A.10 Workplace closures

Full size image
Fig. 15.
figure 15

A.11 Stay-at-home measure

Full size image

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Musić, A., Telalović, J.H., Đulović, D. (2021). The Influence of Stringency Measures and Socio-Economic Data on COVID-19 Outcomes. In: Hasic Telalovic, J., Kantardzic, M. (eds) Mediterranean Forum – Data Science Conference. MeFDATA 2020. Communications in Computer and Information Science, vol 1343. Springer, Cham. https://doi.org/10.1007/978-3-030-72805-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72805-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72804-5

  • Online ISBN: 978-3-030-72805-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation