Open Access
Issue
Math. Model. Nat. Phenom.
Volume 19, 2024
Article Number 6
Number of page(s) 22
Section Population dynamics and epidemiology
DOI https://doi.org/10.1051/mmnp/2024005
Published online 11 April 2024
  1. R. Manjunath, S.L. Gaonkar, E.A. Musad Saleh and K. Husain, A comprehensive review on COVID-19 omicron (b.1.1.529) variant. Saudi J. Biol. Sci. 29 (2022) 103372. [Google Scholar]
  2. S. Arora, V. Grover, P. Saluja, Y.A. Algarni, S.A. Saquib, S.M. Asif, K. Batra, M.Y. Alshahrani, G. Das, R. Jain, et al., Literature review of omicron: a grim reality amidst COVID-19. Microorganisms 10 (2022) 451. [Google Scholar]
  3. Korea Disease Control and Prevention Agency, Public Health Weekly Report, PHWR 15 (2022). https://www.kdca.go.kr/board/board.es?mid=a20602010000&bid=0034&list_no=718944&act=view# (accessed February 25, 2023). [Google Scholar]
  4. J. Chen, A. Vullikanti, J. Santos, S. Venkatramanan, S. Hoops, H. Mortveit, B. Lewis, W. You, S. Eubank, M. Marathe, et al., Epidemiological and economic impact of covid-19 in the us. Sci. Rep. 11 (2021) 1–12. [Google Scholar]
  5. P. Yuan, E. Aruffo, Y. Tan, L. Yang, N.H Ogden, A. Fazil and H. Zhu, Projections of the transmission of the omicron variant for Toronto, Ontario, and Canada using surveillance data following recent changes in testing policies. Infect. Dis. Model. 7 (2022) 83–93. [Google Scholar]
  6. C.N. Ngonghala, H.B. Taboe, S. Safdar and A.B. Gumel, Unraveling the dynamics of the omicron and delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment. Appl. Math. Model. 114 (2023) 447–465. [Google Scholar]
  7. G. Vattiato, O. Maclaren, A. Lustig, R.N. Binny, S.C. Hendy and M.J. Plank, An assessment of the potential impact of the omicron variant of SARS-COV-2 in Aotearoa New Zealand. Infect. Dis. Model. 7 (2022) 94–105. [Google Scholar]
  8. J.-H. Oh, C. Apio and T.-S. Park, Mathematical modeling of the impact of omicron variant on the COVID-19 situation in South Korea. Genomics Inform. 20 (2022) 22–22. [Google Scholar]
  9. Y. Ko, J. Lee, Y. Kim, D. Kwon and E. Jung, COVID-19 vaccine priority strategy using a heterogenous transmission model based on maximum likelihood estimation in the Republic of Korea. Int. J. Environ. Res. Public Health 18 (2021) 6469. [CrossRef] [Google Scholar]
  10. L.L. Obsu and S.F. Balcha, Optimal control strategies for the transmission risk of COVID-19. J. Biol. Dyn. 14 (2020) 590–607. [Google Scholar]
  11. T.A. Perkins and G. España, Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions. Bull. Math. Biol. 82 (2020) 118. [Google Scholar]
  12. T. Li and Y. Guo, Optimal control and cost-effectiveness analysis of a new COVID-19 model for omicron strain. Physica A 606 (2022) 128134. [Google Scholar]
  13. W. Choi and E. Shim, Optimal strategies for social distancing and testing to control COVID-19. J. Theor. Biol. 512 (2021) 110568. [Google Scholar]
  14. C.J. Silva, C. Cruz, D.F.M. Torres, A.P. Munuzuri, A. Carballosa, I. Area, J.J. Nieto, R. Fonseca-Pinto, R. Passadouro, E.S. dos Santos, et al., Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal. Sci. Rep. 11 (2021) 3451. [Google Scholar]
  15. K. Zong and C. Luo, Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic. Risk Anal. 43 (2023) 62–77. [Google Scholar]
  16. D. Biswas and L. Alfandari, Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19. Eur. J. Oper. Res. 303 (2022) 1372–1391. [Google Scholar]
  17. C. Colas, B. Hejblum, S. Rouillon, R. Thiébaut, P.-Y. Oudeyer, C. Moulin-Frier and M. Prague, Epidemioptim: a toolbox for the optimization of control policies in epidemiological models. J. Artif. Intell. Res. 71 (2021) 479–519. [Google Scholar]
  18. V.M.P. Mendoza, R. Mendoza, J. Lee and E. Jung, Adjusting non-pharmaceutical interventions based on hospital bed capacity using a multi-operator differential evolution. AIMS Math. 7 (2022) 19922–19953. [Google Scholar]
  19. G. Bianchin, E. Dall’Anese, J.I. Poveda, D. Jacobson, E.J. Carlton and A.G. Buchwald, Novel use of online optimization in a mathematical model of covid-19 to guide the relaxation of pandemic mitigation measures. Sci. Rep. 12 (2022) 4731. [Google Scholar]
  20. P. Cumsille, O. Rojas-Díaz and C. Conca, A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile. Front. Public Health 11 (2023) 1111641. [Google Scholar]
  21. Coronavirus Disease 19 (COVID-19). https://ncov.kdca.go.kr/en/ (accessed December 11, 2023). [Google Scholar]
  22. K.M. Bubar, K. Reinholt, S.M. Kissler, M. Lipsitch, S. Cobey, Y.H. Grad and D.B. Larremore, Model-informed COVID-19 vaccine prioritization strategies by age and serostatus. Science 371 (2021) 916–921. [Google Scholar]
  23. Korea Disease Control and Prevention Agency, press release-regular briefing, January 13, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&act=view&list_no=718272 (accessed March 2, 2023). [Google Scholar]
  24. Ministry of the Interior and Safety, ktv news announcement: extension of prescription, May 17, 2022. https://www.mois.go.kr/video/bbs/type019/commonSelectBoardArticle.do?bbsId=BBSMSTR_000000000255&nttId=92097 (accessed March 2, 2023). [Google Scholar]
  25. O. Diekmann, J.A.P. Heesterbeek and M.G. Roberts, The construction of next-generation matrices for compartmental epidemic models. J. Roy. Soc. Interface 7 (2010) 873–885. [Google Scholar]
  26. Y. Alimohamadi, M. Taghdir and M. Sepandi, Estimate of the basic reproduction number for COVID-19: a systematic review and meta-analysis. J. Prev. Med. Public Health 53 (2020) 151. [Google Scholar]
  27. Korea Disease Control and Prevention Agency, Public Health Weekly Report, PHWR 14 (2021). https://www.kdca.go.kr/board/board.es?mid=a20602010000&bid=0034&list_no=712537&act=view#. (accessed February 25, 2023). [Google Scholar]
  28. Korea Disease Control and Prevention Agency, Press release-regular briefing, February 3, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718527&cg_code=&act=view&nPage=79 (accessed February 25, 2023). [Google Scholar]
  29. Korea Disease Control and Prevention Agency, Press release-regular briefing, September 23, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&act=view&list_no=720760 (accessed February 25, 2023). [Google Scholar]
  30. S. Lenhart and J.T. Workman, Optimal Control Applied to Biological Models. Chapman and Hall/CRC (2007). [Google Scholar]
  31. E.G. Nepomuceno, M.L.C. Peixoto, M.J. Lacerda, A.S.L.O. Campanharo, R.H.C. Takahashi and L.A. Aguirre, Application of optimal control of infectious diseases in a model-free scenario. SN Comput. Sci. 2 (2021) 405. [Google Scholar]
  32. H. Lee, C. Han, J. Jung and S. Lee, Analysis of superspreading potential from transmission clusters of COVID-19 in South Korea. Int. J. Environ. Res. Public Health 18 (2021) 12893. [Google Scholar]
  33. E. Shim, A. Tariq, W. Choi, Y. Lee and G. Chowell, Transmission potential and severity of COVID-19 in South Korea. Int. J. Infect. Dis. 93 (2020) 339–344. [Google Scholar]
  34. S. Jang, S.H. Han and J.-Y. Rhee, Cluster of coronavirus disease associated with fitness dance classes, South Korea. Emerg. Infect. Dis. 26 (2020) 1917. [Google Scholar]
  35. S. Flaxman, S. Mishra, A. Gandy, H.J.T. Unwin, T.A. Mellan, H. Coupland, C. Whittaker, H. Zhu, T. Berah, J.W. Eaton, et al., Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature 584 (2020) 257–261. [Google Scholar]
  36. A. Collin, B.P. Hejblum and C. Vignals, L. Lehot, R. Thiebaut, P. Moireau and M. Prague, Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions. Int. J. Biostat.. https://www.degruyter.com/document/doi/10.1515/ijb-2022-0087/html. [Google Scholar]
  37. M.R. Keogh-Brown, H.T. Jensen, W.J. Edmunds and R.D. Smith, The impact of COVID-19, associated behaviours and policies on the UK economy: a computable general equilibrium model. SSM-Popul. Health 12 (2020) 100651. [Google Scholar]
  38. Korea Disease Control and Prevention Agency, Press release-regular briefing, August 10, 2021. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=716456 (accessed: March 2, 2023). [Google Scholar]
  39. Korea Disease Control and Prevention Agency, Press release-regular briefing, January 10, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718251&cg_code=&act=view&nPage=92 (accessed: March 2, 2023). [Google Scholar]
  40. Korea Disease Control and Prevention Agency, Press release-regular briefing, january 24, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718415&cg_code=&act=view&nPage=88 (accessed: March 2, 2023). [Google Scholar]
  41. Korea Disease Control and Prevention Agency, Public Health Weekly Report, PHWR 14 (2021). https://www.kdca.go.kr/board/board.es?mid=a20602010000&bid=0034&list_no=716753&act=view (accessed February 25, 2023). [Google Scholar]
  42. Korea Disease Control and Prevention Agency, Press release-regular briefing, december 13, 2021. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=717925&cg_code=&act=view&nPage=100 (accessed March 2, 2023). [Google Scholar]
  43. Korea Disease Control and Prevention Agency, Press release-regular briefing, September 27, 2021. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=717073&cg_code=&act=view&nPage=124 (accessed March 2, 2023). [Google Scholar]
  44. Korea Disease Control and Prevention Agency, Press release-regular briefing, September 13, 2021. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=717235&cg_code=&act=view&nPage=120 (accessed March 2, 2023). [Google Scholar]
  45. Korea Disease Control and Prevention Agency, Press release-regular briefing, January 24, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718415&cg_code=&act=view&nPage=88 (accessed March 2, 2023). [Google Scholar]
  46. Y. Wang, R. Chen, F. Hu, Y. Lan, Z. Yang, C. Zhan, J. Shi, X. Deng, M. Jiang, S. Zhong, et al., Transmission, viral kinetics and clinical characteristics of the emergent SARS-COV-2 delta VOC in Guangzhou, China. EClinicalMedicine 40 (2021) 101129. [Google Scholar]
  47. Korea Disease Control and Prevention Agency, Public Health Weekly Report, PHWR 16 (2023). https://www.phwr.org/journal/view.html?pn=mostread&uid=75&vmd=Full (accessed November 18, 2023). [Google Scholar]
  48. B. Dhungel, M.S. Rahman, M.M. Rahman, A.K.C. Bhandari, P.M. Le, N.A. Biva and S. Gilmour, Reliability of early estimates of the basic reproduction number of COVID-19: a systematic review and meta-analysis. Int. J. Environ. Res. Public Health 19 (2022) 11613. [Google Scholar]
  49. World Health Organization, Transmission of SARS-COV-2: implications for infection prevention precautions: scientific brief, 09 July 2020. Technical report, World Health Organization, 2020. [Google Scholar]
  50. V. Thakur and R.K. Ratho, Omicron (b.1.1.529): a new SARS-COV-2 variant of concern mounting worldwide fear. J. Med. Virol. 94 (2022) 1821–1824. [Google Scholar]
  51. M.-R. Ki, Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCOV) disease in Korea. Epidemiol. Health 42 (2020) 7. [Google Scholar]
  52. C.-C. Lai, T.-P. Shih, W.-C. Ko, H.-J. Tang and P.-R. Hsueh, Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int. J. Antimicrob. Agents 55 (2020) 105924. [Google Scholar]
  53. M. Sepandi, Y. Alimohamadi and F. Esmaeilzadeh, Estimate of the basic reproduction number for delta variant of SARS- COV-2: a systematic review and meta-analysis. J. Biostat. Epidemiol. 8 (2022) 1–7. [Google Scholar]
  54. Y. Liu and J. Rocklöv, The effective reproductive number of the omicron variant of SARS-COV-2 is several times relative to delta. J. Travel Med. 29 (2022) taac037. [Google Scholar]
  55. COVID-19 response weekly news, Seoul Metropolitan Government, March 11, 2022. https://www.seoul.go.kr/seoulcom/fileDownload.do?fileName=corona/daily-news-review_220311_507.pdf (accessed April 2, 2023). [Google Scholar]
  56. J. Lopez Bernal, N. Andrews, C. Gower, E. Gallagher, R. Simmons, S. Thelwall, J. Stowe, E. Tessier, N. Groves, G. Dabrera, et al., Effectiveness of COVID-19 vaccines against the b.1.617.2 (delta) variant. N. Engl. J. Med. 385 (2021) 585–594. [Google Scholar]
  57. N. Andrews, J. Stowe, F. Kirsebom, S. Toffa, T. Rickeard, E. Gallagher, C. Gower, M. Kall, N. Groves, A.-M. O’Connell, et al., COVID-19 vaccine effectiveness against the omicron (b.1.1.529) variant. N. Engl. J. Med. 386 (2022) 1532–1546. [Google Scholar]
  58. H.F. Tseng, B.K. Ackerson, Y.L. Sy, L.S. Sy, C.A. Talarico, Y. Tian, K.J. Bruxvoort, J.E. Tubert, A. Florea, J.H. Ku, et al., Effectiveness of mrna-1273 against sars-cov-2 omicron and delta variants. Nat. Med. 28 (2022) 1063–1071. [Google Scholar]
  59. K.A. Twohig, T. Nyberg, A. Zaidi, S. Thelwall, M.A. Sinnathamby, S. Aliabadi, S.R. Seaman, R.J. Harris, R. Hope, J. Lopez-Bernal, et al., Hospital admission and emergency care attendance risk for SARS-COV-2 delta (b.1.617.2) compared with alpha (b.1.1.7) variants of concern: a cohort study. Lancet Infect. Dis. 22 (2022) 35–42. [Google Scholar]
  60. A.S. Lauring, M.W. Tenforde, J.D. Chappell, M. Gaglani, A.A. Ginde, T. McNeal, S. Ghamande, D.J. Douin, H.K. Talbot, J.D. Casey, et al., Clinical severity of, and effectiveness of MRNA vaccines against, COVID-19 from omicron, delta, and alpha SARS-COV-2 variants in the United States: prospective observational study. BMJ 376 (2022) e069761. [Google Scholar]
  61. Korea Disease Control and Prevention Agency, Press release-regular briefing, February 1, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718518&cg_code==&act=view&nPage=89# (accessed April 2, 2023). [Google Scholar]
  62. D.H. Shin, H.S. Oh, H. Jang, S. Lee, B.S. Choi and D. Kim, Analyses of confirmed covid-19 cases among korean military personnel after mass vaccination. J. Korean Med. Sci. 37 (2022). [Google Scholar]
  63. P.Gy. Choe, Y. Kim, E. Chang, C.K. Kang, N.J. Kim, N.-H. Cho, W. B. Park and M.-d. Oh, Kinetics of neutralizing antibody responses against SARS-COV-2 delta variant in patients infected at the beginning of the pandemic. J. Korean Med. Sci. 37 (2022). [Google Scholar]
  64. S. Yi, J.M. Kim, Y.J. Choe, S. Hong, S. Choi, S.B. Ahn, M. Kim and Y.-J. Park, SARS-COV-2 delta variant breakthrough infection and onward secondary transmission in household. J. Korean Med. Sci. 37 (2022). [Google Scholar]
  65. J. Um, Y.Y. Choi, G. Kim, M.-K. Kim, K.-S. Lee, H.K. Sung, B.C. Kim, Y.-k. Lee, H.-C. Jang, J.H. Bang, et al., Booster bnt162b2 COVID-19 vaccination increases neutralizing antibody titers against the SARS-COV-2 omicron variant in both young and elderly adults. J. Korean Med. Sci. 37 (2022). [Google Scholar]
  66. Korea Disease Control and Prevention Agency. https://ncv.kdca.go.kr/menu.es?mid=a12207000000 (accessed April 2, 2023). [Google Scholar]
  67. Korea Disease Control and Prevention Agency, Press release-regular briefing, July 15, 2022. https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=713974&cg_code=&act=view&nPage=147 (accessed April 2, 2023). [Google Scholar]
  68. R.S. Mahla, L.B. Dustin, et al., Lessons from a large-scale COVID-19 vaccine trial. J. Clin. Invest. 132 (2022). [Google Scholar]
  69. H. Chemaitelly, P. Tang, M.R. Hasan, S. AlMukdad, H.M. Yassine, F.M. Benslimane, H.A. Al Khatib, P. Coyle, H.H. Ayoub, Z. Al Kanaani, et al., Waning of bnt162b2 vaccine protection against SARS-COV-2 infection in Qatar. N. Engl. J. Med. 385 (2021) e83. [Google Scholar]
  70. L.S. Pontryagin, Mathematical Theory of Optimal Processes. CRC Press (1987). [Google Scholar]

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