Special Issue 2022 – Call for Papers
A New Role for Statistics: The Joint Special Issue of "Statistics in Transition New Series" (SiTns) and "Statystyka Ukraïny" (SU)
Sergiusz Herman https://orcid.org/0000-0002-2753-1982

© Sergiusz Herman. Article available under the CC BY-SA 4.0 licence

ARTICLE

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ABSTRACT

The COVID-19 pandemic has had a substantial impact on public health all over the world. In order to prevent the spread of the virus, the majority of countries introduced restrictions which entailed considerable economic and social costs. The main goal of the article is to study how the lockdown introduced in Poland affected the spread of the pandemic in the country. The study used synthetic control method to this end. The analysis was carried on the basis of data from the Local Data Bank and a government website on the state of the epidemic in Poland.
The results indicated that the lockdown significantly curbed the spread of the COVID-19 pandemic in Poland. Restrictions led to the substantial drop in infections – by 9500 cases – in three weeks. The results seem to stay the same despite the change of assumptions in the study. Such conclusion can be drawn from the performance of the placebo-in-space and placebo-in-time analyses.

KEYWORDS

COVID-19, coronavirus, lockdown, synthetic control method, treatment effect

REFERENCES

Abadie, A., (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), pp. 391–425, doi: 10.1257/jel.20191450.

Abadie, A., Diamond, A. and Hainmueller, A. J., (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s Tobacco control program. Journal of the American Statistical Association, 105(490), pp. 493–505, doi: 10.1198/jasa.2009.ap08746.

Abadie, A., Diamond, A. and Hainmueller, J., (2011). Synth: An R package for synthetic control methods in comparative case studies. Journal of Statistical Software, 42(13), pp. 1–17, doi: 10.18637/jss.v042.i13.

Abadie, A., Diamond, A., and Hainmueller, J., (2015). Comparative Politics and the Synthetic Control Method. American Journal of Political Science, 59(2), pp. 495– 510, doi: 10.1111/ajps.12116.

Abadie, A., and Gardeazabal, J., (2003). The economic costs of conflict: A case study of the Basque country. American Economic Review, 93(1), pp. 113–132, doi: 10.1257/000282803321455188.

Abouk, R. and Heydari, B. (2021). The Immediate Effect of COVID-19 Policies on Social- Distancing Behavior in the United States, Public Health Reports, 136(2), pp. 245– 252, doi: 10.1177/0033354920976575.

Alfano, V., and Ercolano, S., (2020). The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis. Applied Health Economics and Health Policy, 18(4), pp. 509–517, doi: 10.1007/s40258-020-00596-3.

Alfano, V., Ercolano, S. and Cicatiello, L., (2021). School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method, Health Policy, 125(9), pp. 1200–1207, doi: 10.1016/j.healthpol.2021.06.010.

Bayat, N., Morrin, C., Wang, Y., and Misra, V., (2020). Synthetic control, synthetic interventions, and COVID-19 spread: Exploring the impact of lockdown measures and herd immunity, arXiv preprint arXiv:2009.09987.

Bo, Y., Guo, C., Lin, C., Zeng, Y., Li, H. B., Zhang, Y., Hossain, M. S., Chan, J., Yeung, D. W., Kwok, K. O., Wong, S. Y. S, Lau, A. K. H., and Lao, X. Q., (2021). Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020. International Journal of Infectious Diseases, 102, pp. 247–253, doi: 10.1016/j.ijid.2020.10.066.

Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., Schmidt, A. L., Valensise, C. M., Scala, A., Quattrociocchi, W., and Pammolli, F., (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, 117(27), 15530–15535, https://doi.org/10.1073/pnas.2007658117

Born, B., Dietrich, A. M., and Müller, G. J., (2021). The lockdown effect: A counterfactual for Sweden. PLoS ONE, 16(4 April 2021), pp. 1–13, doi: 10.1371/journal.pone.0249732.

Chernozhukov, V., Kasahara, H., and Schrimpf, P., (2021). Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S. Journal of Econometrics, 220(1), pp. 23–62, https://doi.org/10.1016/j.jeconom.2020.09.003

Cho, S.W., (2020). Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden. The Econometrics Journal, 23(3), pp. 323–344, doi: 10.1093/ectj/utaa025.

Coccia, M., (2021). The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system. Science of The Total Environment, 775, pp. 145801, doi: https://doi.org/10.1016/j.scitotenv.2021.145801.

Courtemanche, C., Garuccio, J., Le, A., Pinkston, J., and Yelowitz, A., (2020). Strong social distancing measures in the United States reduced the COVID-19 growth rate. Health Affairs, 39(7), pp. 1237–1246, https://doi.org/10.1377/hlthaff.2020.00608

Fountoulakis, K. N., Fountoulakis, N. K., Koupidis, S. A., and Prezerakos, P. E., (2020). Factors determining different death rates because of the COVID-19 outbreak among countries. Journal of Public Health, 42(4), pp. 681–687, https://doi.org/10.1093/pubmed/fdaa119

Goodman-Bacon, A., and Marcus, J., (2020). Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies. Survey Research Methods, 14(2 SEDesign proposals), pp. 153–158. doi: 10.18148/srm/2020.v14i2.7723.

GUS, (2021). Bank Danych lokalnych, URL https://bdl.stat.gov.pl/BDL/start [Accessed 31 July 2021]

Mendez-Brito, A., El Bcheraoui, C. and Pozo-Martin, F., (2021). Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19. Journal of Infection, 83(3), pp. 281–293, doi: 10.1016/j.jinf.2021.06.018.

Ke, X., and Hsiao, C., (2021). Economic impact of the most drastic lockdown during COVID-19 pandemic—The experience of Hubei, China. Journal of Applied Econometrics, March, 1–23, https://doi.org/10.1002/jae.2871

Lai, S., Ruktanonchai, N. W., Zhou, L., Prosper, O., Luo, W., Floyd, J. R., Wesolowski, A., Santillana, M., Zhang, C., Du, X., Yu, H., and Tatem, A. J., (2020). Effect of nonpharmaceutical interventions to contain COVID-19 in China. Nature, 585(7825), pp. 410–413, https://doi.org/10.1038/s41586-020-2293-x

Li, Y., Li, M., Rice, M., Zhang, H., Sha, D., Li, M., Su, Y., and Yang, C., (2021). The impact of policy measures on human mobility, COVID-19 cases, and mortality in the US: A spatiotemporal perspective. International Journal of Environmental Research and Public Health, 18(3), pp. 1–25, https://doi.org/10.3390/ijerph18030996

Mendez-Brito, A., El Bcheraoui, C. and Pozo-Martin, F. (2021). Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19. Journal of Infection, 83(3), pp. 281–293, doi: 10.1016/j.jinf.2021.06.018.

Ministry of Health, (2021). Raport zakażeń koronawirusem (SARS-CoV-2), URL https://www.gov.pl/web/koronawirus/wykaz-zarazen-koronawirusem-sars-cov-2 [Accessed 31 July 2021]

Mitze, T., Kosfeld, R., Rode, J. and Wälde, K., (2020). Face masks considerably reduce COVID-19 cases in Germany. Proceedings of the National Academy of Sciences, 117(51), pp. 32293–32301, https://doi.org/10.1073/pnas.2015954117

Palomino, J. C., Rodríguez, J. G., Sebastian, R., (2020). Wage inequality and poverty effects of lockdown and social distancing in Europe. European Economic Review, 129, pp. 103564, doi: https://doi.org/10.1016/j.euroecorev.2020.103564.

Piovani, D., Christodoulou, M. N., Hadjidemetriou, A., Pantavou, K., Zaza, P., Bagos, P. G., Bonovas, S., and Nikolopoulos, G. K., (2021). Effect of early application of social distancing interventions on COVID-19 mortality over the first pandemic wave: An analysis of longitudinal data from 37 countries. Journal of Infection, 82(1), pp. 133–142, https://doi.org/10.1016/j.jinf.2020.11.033

Ritchie, H., Ortiz-Ospina, E., Beltekian, D., Mathieu, E., Hasell, J., Macdonald, B., Giattino, C., Appel, C., Rodés-Guirao, L., and Roser, M., (2021). Coronavirus pandemic (COVID-19). Our World in Data, URL https://ourworldindata.org/coronavirus [Accessed 31 July 2021]

Ruan, L., Wen, M., Zeng, Q., Chen, C., Huang, S., Yang, S., Yang, J., Wang, J., Hu, Y., Ding, S., Zhang, Y., Zhang, H., Feng, Y., Jin, K., and Zhuge, Q., (2020). New Measures for the Coronavirus Disease 2019 Response: A Lesson From the Wenzhou Experience. Clinical Infectious Diseases, 71(15), pp. 866–869, https://doi.org/10.1093/cid/ciaa386

Siedner, M. J., Harling, G., Reynolds, Z., Gilbert, R. F., Haneuse, S., Venkataramani, A. S., and Tsai, A. C., (2020). Social distancing to slow the US COVID-19 epidemic: Longitudinal pretest–posttest comparison group study. PLoS Medicine, 17(8 August), pp. 1–12, https://doi.org/10.1371/JOURNAL.PMED.1003244

Tian, T., Tan, J., Luo, W., Jiang, Y., Chen, M., Yang, S., Wen, C., Pan, W., and Wang, X., (2021). The Effects of Stringent and Mild Interventions for Coronavirus Pandemic. Journal of the American Statistical Association, 116(534), pp. 481–491, https://doi.org/10.1080/01621459.2021.1897015

Tian, T., Luo, W., Tan, J., Jiang, Y., Chen, M., Pan, W., Yang, S., Zhao, J., Wang, X., and Zhang, H., (2021). The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method. Statistics and Its Interface, 14(1), pp. 3–12, https://doi.org/10.4310/20-SII634

Wu, S., Yao, M., Deng, C., Marsiglia, F. F., and Duan, W., (2021). Social Isolation and Anxiety Disorder During the COVID-19 Pandemic and Lockdown in China. Journal of Affective Disorders, 294, pp. 10–16, https://doi.org/https://doi.org/10.1016/j.jad.2021.06.067

Zhang, R., Li, Y., Zhang, A. L., Wang, Y., and Molina, M. J., (2020). Identifying airborne transmission as the dominant route for the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(26), 14857 LP–14863, https://doi.org/10.1073/pnas.2009637117\

Zhang, H., Li, P., Zhang, Z., Li, W., Chen, J., Song, X., Shibasaki, R., and Yan, J. (2022). Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis. Cities, 120, 103502, https://doi.org/10.1016/j.cities.2021.103502

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