DOI QR코드

DOI QR Code

환자 상태 정보를 활용한 메르스 치사율 추정법

Estimation of the case fatality ratio of MERS epidemics using information on patients' severity condition

  • 투고 : 2016.02.23
  • 심사 : 2016.03.31
  • 발행 : 2016.05.31

초록

한국에서 새로운 유형의 메르스 코로나 바이러스 감염에 의한 중동 호흡기 증후군 환자가 처음으로 발생한 이후 급속하게 번져, 사회적으로 큰 문제가 되었다. 최근 중동의 아라비아 반도를 중심으로 처음으로 감염 환자가 발생한 이 질병은 사우디 아라비아에서의 치사율이 30~40%에 이르는 것으로 알려져 있다. 그러나 전염 과정 초기에 한국질병관리본부에서 발표하는 치사율은 10% 초반으로 기존에 알려져 있는 치사율에 비해 현저히 낮은 수준을 보였다. 이는 전염 진행 과정에서 사망 또는 퇴원하지 않고 입원 중인 메르스 확진 환자의 수를 고려하지 않은 확진자 중 사망자의 비율을 사용하는 단순추정법에 기인한 것이었다. 치사율은 그 값에 따라서 전염병의 대처 정책에 큰 영향을 미치는 값이므로 전염 과정의 초기부터 안정적으로 치사율을 추정하는 것이 중요하다. 따라서 본 연구에서는 기존의 추정치에 비해 감염의 초기 단계에서부터 안정적으로 치사율을 추정하는 방법을 제시한다. 제시된 추정치는 메르스로 인한 사망자 수 이외에 입원 환자의 상태의 정보를 활용하였다. 새로운 추정치의 성능을 보기 위하여 한국에서 발생한 감염 이후 2015년 8월 10일까지 186명의 감염자 자료를 사용하여 치사율을 추정하고 기존의 여러 가지 치사율 추정치와 비교하였다. 제시한 추정치는 감염의 초기 단계에서부터 다른 추정치에 비해 안정적인 모습을 보였다.

The first patient of Middle East respiratory syndrome caused by a novel coronavirus infection in Korea was confirmed on May 20, 2015. After that, MERS spread over the country. In recent years, patients of MERS have been found around the Arabian Peninsula and the case fatality ratio of MERS in those area was been reported to range from 30 to 40%. In this paper, we estimate the case fatality ratio of MERS of Korea using data of 186 infections until December 1, 2015. In this study we propose a novel estimator of the case fatality ratio using information of the patients severity condition as well as records on the days of confirmation and death or recovery of the patient. By using publicly available data of the Department of Health and Human Services Centers for Disease Control, we evaluate a performance of the estimator and demonstrate a stability of the estimator from the early stage of the epidemic.

키워드

참고문헌

  1. Atkins, K. E., Wenzel, N. S., Ndeffo-Mbah, M., Altice, F. L., Townsend, J. P. and Galvani, A. P. (2014). Under-reporting and case fatality estimates for emerging epidemics. BMJ, 350, h1115.
  2. Balcan, D., Hu, H., Goncalves, B., Bajardi, P., Poletto, C., Ramasco, J. J., Paolotti, D., Perra, N., Tizzoni, M., Broeck, W. V. d., Colizza, V. and Vespignani, A. (2009). Seasonal transmission potential and activity peaks of the new influenza A(H1N1): A Monte Carlo likelihood analysis based on human mobility. BMC Medicine, 7, 1-12. https://doi.org/10.1186/1741-7015-7-1
  3. Chowell, G., Blumberga, S., Simonsena, L., Millera, M. A. and Viboud, C. (2014). Synthesizing data and models for the spread of MERS-CoV, 2013: Key role of index cases and hospital transmission. Epidemics, 9, 40-51. https://doi.org/10.1016/j.epidem.2014.09.011
  4. Cowling, B. J., Park, M., Fang, V. J., Wu, P., Leung, G. M. and Wu, J. T. (2015). Preliminary epidemiological assessment of MERS-CoV outbreak in South Korea, May to June 2015. Euro Surveill, 20, pii=21163.
  5. Donnelly, C. A., Ghani, A. C., Leung, G. M., Hedley, A. J., Fraser, C., Riley, S., Abu-Raddad, L. J., Ho, L. M., Thach, T. Q., Chau, P., Chan, K. P., Lam, T. H., Tse, L. Y., Tsang, T., Liu, S. H., Kong, J. H. B., Lau, E. M. C., Ferguson, N. M. and Anderson, R. M. (2003). Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet, 361, 1761-1766. https://doi.org/10.1016/S0140-6736(03)13410-1
  6. Echevarria-Zuno, S., Mejia-Arangure, J. M., Mar-Obeso, A. J., Grajales-Muniz, C., Robles-Perez, E., Gonzalez-Leon, M., Ortega-Alvarez, M. C., Gonzalez-Bonilla, C., Rascon-Pacheco, R. A. and Borja-Aburto, V. H. (2009). Infection and death from influenza A H1N1 virus in Mexico: A retrospective analysis. Lancet, 374, 2072-2079. https://doi.org/10.1016/S0140-6736(09)61638-X
  7. Gardner, L. M., Rey, D., Heywood, A. E., Toms, R., Wood, J., Waller, S. T. and MacIntyre, C. R. (2014). A scenario-based evaluation of the Middle East Respiratory Syndrome Coronavirus and the Hajj. Risk Analysis, 34, 1391-1400. https://doi.org/10.1111/risa.12253
  8. Garske, T., Legrand, J., Donnelly, C. A., Ward, H., Cauchemez, S., Fraser, C., Ferguson, N. M. and Ghani, A. C. (2009). Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ, 339, b2840. https://doi.org/10.1136/bmj.b2840
  9. Ghani, A. C., Donnelly, C. A., Cox, D. R., Griffin, J. T., Fraser, C., Lam, T. H., Ho, L. M., Chan, W. S., Anderson, R. M., Hedley, A. J. and Leung, G. M. (2005). Methods for estimating the case fatality ratio for a novel, emerging infectious disease. American Journal of Epidemiology, 162, 479-486. https://doi.org/10.1093/aje/kwi230
  10. Hwang, S., Sohn, S. and Oh, C. (2015). Maximum likelihood estimation for a mixture distribution. Journal of the Korean Data & Information Science Society, 26, 313-322. https://doi.org/10.7465/jkdi.2015.26.2.313
  11. Jewell, N. P., Lei, X., Ghani, A. C., Donnelly, C. A., Leung, G. M., Ho, L. M. and Cowling, Benjamin J. and Hedley, A. J. (2007). Non-parametric estimation of the case fatality ratio with competing risks data:An application to Severe Acute Respiratory Syndrome (SARS). Statistics in medicine, 26, 1982-1998. https://doi.org/10.1002/sim.2691
  12. Khandaker, G., Dierig, A., Rashid, H., King, C., Heron, L. and Booy, R. (2011). Systematic review of clinical and epidemiological features of the pandemic influenza A (H1N1) 2009. Influenza and Other Respiratory Viruses, 5, 148-156. https://doi.org/10.1111/j.1750-2659.2011.00199.x
  13. Kim, K. M., Ki, M., Cho, S.-i., Sung, M., Hong, J. K., Cheong, H. K., Kim, J. H., Lee, S. E., Lee, C., Lee, K. J., Park, Y. S., Kim, S. W. and Choi, B. Y. (2015). Epidemiologic features of the first MERS outbreak in Korea: Focus on Pyeongtaek St. Mary's Hospital. Epidemiology and Health, 37, 1-10.
  14. Korea infection control headquater (2015). MERS portal, http://www.mers.go.kr, last checked: 11.14.2015.
  15. Ma, J. and Driessche, P. V. D. (2008). Case Fatality Proportion. Bulletin of Mathematical Biology, 70, 118-133. https://doi.org/10.1007/s11538-007-9243-8
  16. Merler, S., Ajelli, M., Pugliese, A. and Ferguson, N. M. (2011). Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: Implications for real-time modelling. PLoS Computational Biology, 7, e1002205. https://doi.org/10.1371/journal.pcbi.1002205
  17. Mishra, A. C., Chadha, M. S., Choudhary, M. L. and Potdar, V. A. (2010). Pandemic influenza (H1N1) 2009 is associated with severe disease in India. PLoS One, 5, e10540. https://doi.org/10.1371/journal.pone.0010540
  18. Mizumoto, K., Saitoh, M., Chowell, G., Miyamatsu, Y. and Nishiura, H. (2015). Estimating the risk of Middle East respiratory syndrome (MERS) death during the course of the outbreak in the Republic of Korea, 2015. International Journal of Infectious Diseases, 39, 7-9. https://doi.org/10.1016/j.ijid.2015.08.005
  19. Nguyen-Van-Tam, J. S., Openshaw, P. J. M., Hashim, A., Gadd, E. M., Lim, W. S., Semple, M. G., Read, R. C., Taylor, B. L., Brett, S. J., McMenamin, J., Enstone, J. E., Armstrong, C., Nicholson, K. G. and on behalf of the Influenza Clinical Information Network (FLU-CIN) (2010). Risk factors for hospitalisation and poor outcome with pandemic A/H1N1 influenza: United Kingdom first wave (May-September 2009). Thorax, 65, 645-651. https://doi.org/10.1136/thx.2010.135210
  20. Nishiura, H. (2010). Case fatality ratio of pandemic influenza. Lancet infection, 10, 443-444. https://doi.org/10.1016/S1473-3099(10)70120-1
  21. Oh, C. (2014). Estimation in the mixture of shifted Poisson distributions. Journal of the Korean Data & Information Science Society, 25, 255-261. https://doi.org/10.7465/jkdi.2014.25.1.255
  22. Poletto, C., Pelat, C., Levy-Bruhl, D., Yazdanpanah, Y., Boelle, P. Y. and Colizza, V. (2014). Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic in the Middle East and risk of international spread using a novel maximum likelihood analysis approach. Euro Surveill, 19, 20824. https://doi.org/10.2807/1560-7917.ES2014.19.23.20824
  23. Presanis, A. M., Angelis, D. D., The New York City Swine Flu Investigation Team, Hagy, A., Reed, C., Riley, S., Cooper, B. S., Finelli, L., Biedrzycki, P. and Lipsitch, M. (2009). The severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysis. PLoS Medicine, 6, e1000207. https://doi.org/10.1371/journal.pmed.1000207
  24. Yang, Y., Sugimoto, J. D., Halloran, M. E., Basta, N. E., Chao, D. L., Matrajt, L., Potter, G., Kenah, E. and Jr, I. M. L. (2009). The transmissibility and control of pandemic influenza a (H1N1) virus. Science, 326, 729-733. https://doi.org/10.1126/science.1177373
  25. Yu, H., Cowling, B., Feng, L., Lau, E. H. Y., Liao, Q., Tsang, T. K., Peng, Z., Wu, P., Liu, F., Fang, V. J., Zhang, H., Li, M., Zeng, L., Xu, Z., Li, Z., Luo, H., Li, Q., Feng, Z., Cao, B., Yang, W., Wu, J. T., Wang, Y. and Leung, G. M. (2013). Human infection with avian influenza A H7N9 virus: An assessment of clinical severity. Lancet, 382, 138-145. https://doi.org/10.1016/S0140-6736(13)61207-6

피인용 문헌

  1. Comparison study of SARIMA and ARGO models for in influenza epidemics prediction vol.27, pp.4, 2016, https://doi.org/10.7465/jkdi.2016.27.4.1075
  2. 베이지안 음이항 분기과정을 이용한 한국 메르스 발생 연구 vol.28, pp.1, 2016, https://doi.org/10.7465/jkdi.2017.28.1.153
  3. Comparison of Severe Disease Incidence among Eligible Insureds to Expand Coverage for Substandard Risks vol.43, pp.4, 2018, https://doi.org/10.21032/jhis.2018.43.4.318