JDR Vol.17 No.1 pp. 57-60
doi: 10.20965/jdr.2022.p0057


Huge Excess Mortality Due to the Delta Strain of COVID-19 in Japan in August 2021

Junko Kurita*, Tamie Sugawara**,†, and Yasushi Ohkusa**

*Department of Nursing, Tokiwa University
1-430-1 Miwa, Mito, Ibaraki 310-8585, Japan

**Infectious Disease Surveillance Center, National Institute of Infectious Diseases (NIID), Tokyo, Japan

Corresponding author

October 27, 2021
December 8, 2021
January 30, 2022
excess mortality, delta strain, COVID-19, all cause death, stochastic frontier estimation

Background: No remarkable excess mortality attributable to COVID-19 has been observed in Japan until the delta strain of COVID-19 emerged. Object: We sought to quantify high pathogenicity of the delta strain using the National Institute of Infectious Diseases (NIID) model. Method: We applied the NIID model to deaths of all causes from 1987 up through August 2021 for the whole of Japan. Results: Results in Japan show 4105 excess mortality in August 2021 in Japan. It was estimated as 3.8% of the baseline. Discussion and Conclusion: We found substantial excess mortality since the outbreak of COVID-19 had emerged in August 2021, in Japan. It might be due to spread of delta strain at that time.

Cite this article as:
Junko Kurita, Tamie Sugawara, and Yasushi Ohkusa, “Huge Excess Mortality Due to the Delta Strain of COVID-19 in Japan in August 2021,” J. Disaster Res., Vol.17, No.1, pp. 57-60, 2022.
Data files:
  1. [1] J. Kurita, T. Sugawara, and Y. Ohkusa, “Excess Mortality Probably Attributable to COVID-19 in Tokyo, Japan During August and October 2020,” J. Disaster Res., Vol.16, No.5, pp. 890-894, doi: 10.20965/jdr.2021.p0890, 2021.
  2. [2] Tokyo Metropolitan Government, “Data of COVID-19 monitoring meeting in Tokyo metropolitan,” 2021, (in Japanese) [accessed October 21, 2021]
  3. [3] B. Li, A. Deng, K. Li et al., “Viral infection and transmission in a large well-traced outbreak caused by the SARS-CoV-2 Delta variant,” medRxiv preprint, 2021.07.07.21260122, doi: 10.1101/2021.07.07.21260122, 2021.
  4. [4] H. C. Lin, H. F. Chiu, S. C. Ho, and C. Y. Yang, “Association of influenza vaccination and reduced risk of stroke hospitalization among the elderly: a population-based case-control study,” Int. J. Environ Res. Public Health, Vol.11, No.4, pp. 3639-3649, 2014.
  5. [5] Z. Asghar, C. Coupland, and N. Siriwardena, “Influenza vaccination and risk of stroke: Self-controlled case-series study,” Vaccine, Vol.33, No.41, pp. 5458-5463, 2015.
  6. [6] E. M. Riedmann, “Influenza vaccination reduces risk of heart attack and stroke,” Hum Vaccin Immunother, Vol.9, No.12, p. 2500, 2013.
  7. [7] C. S. Kwok, S. Aslam, E. Kontopantelis, P. K. Myint, M. J. Zaman, I. Buchan, Y. K. Loke, and M. A. Mamasf, “Influenza, influenza-like symptoms and their association with cardiovascular risks: a systematic review and meta-analysis of observational studies,” Int. J. Clin Pract, Vol.69, No.9, pp. 928-937, 2015.
  8. [8] S. Muhammad, E. Haasbach, M. Kotchourko, A. Strigli, A. Krenz, D. A. Ridder, A. B. Vogel, H. H. Marti, Y. Al-Abed, O. Planz, and M. Schwaninger, “Influenza virus infection aggravates stroke outcome,” Stroke, Vol.42, No.3, pp. 783-791, 2011.
  9. [9] F. Assad, W. C. Cockburn, and T. K. Sundaresan, “Use of excess mortality from respiratory diseases in the study of influenza,” Bull WHO, Vol.49, No.3, pp. 219-233, 1973.
  10. [10] US Center for Disease Control and Prevention, “Excess Deaths Associated with COVID-19,” [accessed July 15, 2020]
  11. [11] T. Sugawara and Y. Ohkusa, “Comparison of Models for Excess Mortality of Influenza Applied to Japan,” J. of Biosciences and Medicines, Vol.7, No.6, pp. 13-23, doi: 10.4236/jbm.2019.76002, 2019.
  12. [12] Ministry of Health, Labour and Welfare, “Preliminary statistics on demographics,” (in Japanese) [accessed July 26, 2020]
  13. [13] D. Aiger, K. Lovell, and P. Schmitidt, “Formulation and estimation of stochastic frontier production function models,” J. of Econometrics, Vol.6, No.1, pp. 21-37, 1997.
  14. [14] J. Jondrow, K. Lovell, I. S. Materov, and P. Schmidt, “On the estimation of technical inefficiency in the stochastic frontier production function model,” J. of Econometrics, Vol.19, No.2-3, pp. 233-239, 1982.
  15. [15] T. Li and R. Rosenman, “Cost inefficiency in Washington Hospitals: A stochastic frontier approach using panel data,” Health Care Management Science, Vol.4, pp. 73-81, 2001.
  16. [16] J. P. Newhouse, “Frontier Estimation: How useful a tool for health economics?,” J. of Health Economics, Vol.13, No.3, pp. 317-322, 1994.
  17. [17] H. S. Brown III, “Managed care and technical efficiency,” Health Economics, Vol.12, No.2, pp. 149-158, 2003.
  18. [18] R. Jacobs, “Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis,” Health Care Management Science, Vol.4, pp. 103-115, 2001.
  19. [19] M. D. Rosko, “Cost efficiency of US hospitals: A stochastic frontier approach,” Health Economics, Voi.10, No.6, pp. 539-551, 2001.
  20. [20] “Excess and Exiguous Deaths Dashboard in Japan,” [accessed July 7, 2021]
  21. [21] T. Kawashima, S. Nomura, Y. Tanoue et al., “Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January–May 2020,” Emerg Infect Dis, Vol.27, No.3, pp. 789-795, doi: 10.3201/eid2703.203925, 2021.
  22. [22] Center of Disease Control and Prevention, “Excess Deaths Associated with COVID-19,” [accessed June 23, 2021]
  23. [23] EUROMOMO, [accessed June 23, 2021]

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Last updated on May. 20, 2022