Acknowledgement
This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2021M3E5E3081425).
References
- Younes AB, Hasan Z. COVID-19: modeling, prediction, and control. Appl Sci 2020;10:3666. https://doi.org/10.3390/app10113666
- Coronavirus disease (COVID-19) pandemic. Geneva: World Health Organization, 2020. Accessed 2022 Jan 20. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
- Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 2020;584:262-267. https://doi.org/10.1038/s41586-020-2404-8
- Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, et al. Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand. London: Imperial College, 2020.
- Brzezinski A, Deiana G, Kecht V, Van Dijcke D. The COVID-19 Pandemic: Government vs. Community Action across the United States. INET Oxford Working Paper No. 2020-06. Oxford: Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, 2020.
- Graham BS. Rapid COVID-19 vaccine development. Science 2020;368:945-946. https://doi.org/10.1126/science.abb8923
- Wu SC. Progress and concept for COVID-19 vaccine development. Biotechnol J 2020;15:e2000147.
- Thanh Le T, Andreadakis Z, Kumar A, Gomez Roman R, Tollefsen S, Saville M, et al. The COVID-19 vaccine development landscape. Nat Rev Drug Discov 2020;19:305-306. https://doi.org/10.1038/d41573-020-00073-5
- Zuo M, Khosa SK, Ahmad Z, Almaspoor Z. Comparison of COVID-19 pandemic dynamics in Asian countries with statistical modeling. Comput Math Methods Med 2020;2020:4296806.
- Muse AH, Tolba AH, Fayad E, Abu Ali OA, Nagy M, Yusuf M. Modelling the COVID-19 mortality rate with a new versatile modification of the log-logistic distribution. Comput Intell Neurosci 2021;2021:8640794.
- de la Fuente-Mella H, Rubilar R, Chahuan-Jimenez K, Leiva V. Modeling COVID-19 cases statistically and evaluating their effect on the economy of countries. Mathematics 2021;9:1558. https://doi.org/10.3390/math9131558
- Liu X, Ahmad Z, Gemeay AM, Abdulrahman AT, Hafez EH, Khalil N. Modeling the survival times of the COVID-19 patients with a new statistical model: a case study from China. PLoS One 2021;16:e0254999. https://doi.org/10.1371/journal.pone.0254999
- Biggerstaff M, Cowling BJ, Cucunuba ZM, Dinh L, Ferguson NM, Gao H, et al. Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19. Emerg Infect Dis 2020;26:e1-e14.
- Childs ML, Kain MP, Harris MJ, Kirk D, Couper L, Nova N, et al. The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models. Proc Biol Sci 2021;288:20210811.
- Moreau VH. Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths. Braz J Microbiol 2020;51:1109-1115. https://doi.org/10.1007/s42770-020-00331-z
- Datta R, Trivedi PK, Kumawat A, Kumar R, Bhardwaj I, Kumari N, et al. Statistical modeling of COVID-19 pandemic stages worldwide. Preprints at https://doi.org/10.20944/preprints202005.0319.v1 (2020).
- Roy S, Bhunia GS, Shit PK. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model Earth Syst Environ 2021;7:1385-1391. https://doi.org/10.1007/s40808-020-00890-y
- Singh RK, Rani M, Bhagavathula AS, Sah R, Rodriguez-Morales AJ, Kalita H, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill 2020;6:e19115. https://doi.org/10.2196/19115
- Zeroual A, Harrou F, Dairi A, Sun Y. Deep learning methods for forecasting COVID-19 time-series data: a comparative study. Chaos Solitons Fractals 2020;140:110121. https://doi.org/10.1016/j.chaos.2020.110121
- Arora P, Kumar H, Panigrahi BK. Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos Solitons Fractals 2020;139:110017. https://doi.org/10.1016/j.chaos.2020.110017
- Kapoor A, Ben X, Liu L, Perozzi B, Barnes M, Blais M, et al. Examining COVID-19 forecasting using spatio-temporal graph neural networks. Preprint at https://arxiv.org/abs/2007.03113(2020).
- Fritz C, Dorigatti E, Rugamer D. Combining graph neural networks and spatio-temporal disease models to predict COVID-19 cases in Germany. Sci Rep 2022;12:3930. https://doi.org/10.1038/s41598-022-07757-5
- Rauf HT, Lali MI, Khan MA, Kadry S, Alolaiyan H, Razaq A, et al. Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. Pers Ubiquitous Comput 2021 Jan 10 [Epub]. https://doi.org/10.1007/s00779-020-01494-0.
- Nabi KN, Tahmid MT, Rafi A, Kader ME, Haider MA. Forecasting COVID-19 cases: a comparative analysis between recurrent and convolutional neural networks. Results Phys 2021;24:104137. https://doi.org/10.1016/j.rinp.2021.104137
- Chen TM, Rui J, Wang QP, Zhao ZY, Cui JA, Yin L. A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infect Dis Poverty 2020;9:24. https://doi.org/10.1186/s40249-020-00640-3
- Ndairou F, Area I, Nieto JJ, Torres DF. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846
- Car Z, Baressi Segota S, Andelic N, Lorencin I, Mrzljak V. Modeling the spread of COVID-19 infection using a multilayer perceptron. Comput Math Methods Med 2020;2020:5714714.
- Panovska-Griffiths J. Can mathematical modelling solve the current Covid-19 crisis? BMC Public Health 2020;20:551. https://doi.org/10.1186/s12889-020-08671-z
- Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond Ser A Contain Pap Math Phys Character 1927;115:700-721.
- Shankar S, Mohakuda SS, Kumar A, Nazneen PS, Yadav AK, Chatterjee K, et al. Systematic review of predictive mathematical models of COVID-19 epidemic. Med J Armed Forces India 2021;77(Suppl 2):S385-S392. https://doi.org/10.1016/j.mjafi.2021.05.005
- Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 2020;20:553-558. https://doi.org/10.1016/S1473-3099(20)30144-4
- Mohd MH, Sulayman F. Unravelling the myths of R 0 in controlling the dynamics of COVID-19 outbreak: a modelling perspective. Chaos Solitons Fractals 2020;138:109943. https://doi.org/10.1016/j.chaos.2020.109943
- Volpert V, Banerjee M, Petrovskii S. On a quarantine model of coronavirus infection and data analysis. Math Model Nat Phenom 2020;15:24. https://doi.org/10.1051/mmnp/2020006
- Kochanczyk M, Grabowski F, Lipniacki T. Dynamics of COVID-19 pandemic at constant and time-dependent contact rates. Math Model Nat Phenom 2020;15:28. https://doi.org/10.1051/mmnp/2020011
- Roda WC, Varughese MB, Han D, Li MY. Why is it difficult to accurately predict the COVID-19 epidemic? Infect Dis Model 2020;5:271-281.
- Tuite AR, Fisman DN, Greer AL. Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. CMAJ 2020;192:E497-E505. https://doi.org/10.1503/cmaj.200476
- Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health 2020;8:e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7
- Liu Y, Gayle AA, Wilder-Smith A, Rocklov J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med 2020;27:taaa021. https://doi.org/10.1093/jtm/taaa021
- Cakir Z, Savas HB. A mathematical modelling approach in the spread of the novel 2019 coronavirus SARS-CoV-2 (COVID-19) pandemic. Electron J Gen Med 2020;17:em205. https://doi.org/10.29333/ejgm/7861
- Bouchnita A, Jebrane A. A multi-scale model quantifies the impact of limited movement of the population and mandatory wearing of face masks in containing the COVID-19 epidemic in Morocco. Math Model Nat Phenom 2020;15:31. https://doi.org/10.1051/mmnp/2020016
- Sahin U, Sahin T. Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model. Chaos Solitons Fractals 2020;138:109948. https://doi.org/10.1016/j.chaos.2020.109948
- Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman JM, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Model 2020;5:256-263.
- Castro MC, Singer B. Prioritizing COVID-19 vaccination by age. Proc Natl Acad Sci U S A 2021;118:e2103700118. https://doi.org/10.1073/pnas.2103700118
- Ritchie H, Mathieu E, Rodes-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus (COVID-19) vaccinations. Oxford: University of Oxford, 2020. Accessed 2022 Jan 22. Available from: https://ourworldindata.org/covid-vaccinations.
- Vasireddy D, Vanaparthy R, Mohan G, Malayala SV, Atluri P. Review of COVID-19 variants and COVID-19 vaccine efficacy: what the clinician should know? J Clin Med Res 2021;13:317-325. https://doi.org/10.14740/jocmr4518
- Darby AC, Hiscox JA. Covid-19: variants and vaccination. BMJ 2021;372:n771. https://doi.org/10.1136/bmj.n771
- Jo H, Son H, Hwang HJ, Jung SY. Analysis of COVID-19 spread in South Korea using the SIR model with time-dependent parameters and deep learning. Preprint at https://doi.org/10.1101/ 2020.04.13.20063412 (2020).
- Kim YJ, Seo MH, Yeom HE. Estimating a breakpoint in the pattern of spread of COVID-19 in South Korea. Int J Infect Dis 2020;97:360-364. https://doi.org/10.1016/j.ijid.2020.06.055
- Nesteruk I. Estimations of the coronavirus epidemic dynamics in South Korea with the use of SIR model. Preprint at https://doi.org/10.13140/RG.2.2.15489.40807 (2020).
- Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057
- He J, Chen G, Jiang Y, Jin R, Shortridge A, Agusti S, et al. Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran. Sci Total Environ 2020;747:141447. https://doi.org/10.1016/j.scitotenv.2020.141447
- Reis RF, de Melo Quintela B, de Oliveira Campos J, Gomes JM, Rocha BM, Lobosco M, et al. Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil. Chaos SolitonsFractals 2020;136:109888. https://doi.org/10.1016/j.chaos.2020.109888
- Amiri Mehra AH, Shafieirad M, Abbasi Z, Zamani I. Parameter estimation and prediction of COVID-19 epidemic turning point and ending time of a case study on SIR/SQAIR epidemic models. Comput Math Methods Med 2020;2020:1465923.
- Ko Y, Lee J, Seo Y, Jung E. Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study. Epidemiol Health 2021;43:e2021059. https://doi.org/10.4178/epih.e2021059
- Domingo E, Holland JJ. RNA virus mutations and fitness for survival. Annu Rev Microbiol 1997;51:151-178. https://doi.org/10.1146/annurev.micro.51.1.151
- Zhou W, Wang W. Fast-spreading SARS-CoV-2 variants: challenges to and new design strategies of COVID-19 vaccines. Signal Transduct Target Ther 2021;6:226. https://doi.org/10.1038/s41392-021-00644-x
- Ko Y, Lee J, Kim Y, Kwon D, Jung E. 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 2021;18:6469. https://doi.org/10.3390/ijerph18126469
- Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057
- Kim S, Seo YB, Jung E. Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea. Epidemiol Health 2020;42:e2020026.
- Brauer F. Compartmental models in epidemiology. In: Mathematical Epidemiology (Brauer F, van den Driessche P, Wu J, eds.). Berlin: Springer Berlin Heidelberg, 2008. pp. 19-79.
- JM24. fitVirusCV19v3 (COVID-19 SIR Model). Natick: MathWorks, 2022. Accessed 2022 Jan 23. Available from: https://www.mathworks.com/matlabcentral/fileexchange/74676-fitviruscv19v3-covid-19-sir-model.
- Hale T, Petherick A, Phillips T, Webster S. Variation in Government Responses to COVID-19. Blavatnik School of Government Working Paper 31. Oxford: Blavatnik School of Government, 2020.
- Blavatnik School of Government, University of Oxford. COVID-19 government response tracker. Oxford: Blavatnik School of Government, 2020. Accessed 2022 Jan 22. Available from: https://www.bsg.ox.ac.uk/research/research-projects/ coronavirus-government-response-tracker.
- Korean situation report of COVID-19. Cheonju: Korea Disease Control and Prevention Agency, 2020. Accessed 2022 Jan 4. Available from: http://ncov.mohw.go.kr/tcmBoardView.do?brdId=&brdGubun=&dataGubun=&ncvContSeq=352903&contSeq=352903.
- Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, et al. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill 2020;25:2000257.
- Ki M; Task Force for 2019-nCoV. Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea. Epidemiol Health 2020;42:e2020007. https://doi.org/10.4178/epih.e2020007
- Lee YH, Hong CM, Kim DH, Lee TH, Lee J. Clinical course of asymptomatic and mildly symptomatic patients with coronavirus disease admitted to community treatment centers, South Korea. Emerg Infect Dis 2020;26:2346-2352. https://doi.org/10.3201/eid2610.201620
- Ritchie H, Mathieu E, Rodes-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus pandemic (COVID-19). Oxford: University of Oxford, 2020. Accessed 2022 Jan 4. Available from: https://ourworldindata.org/coronavirus.
- Quick review on COVID-19 vaccine issues (ver. 2.0). Seoul: National Evidence-based healthcare Collaborating Agency, 2021. Available from: https://www.neca.re.kr/lay1/bbs/S1T11C174/ F/58/view.do?article_seq=8634.
- Mathieu E, Ritchie H, Ortiz-Ospina E, Roser M, Hasell J, Appel C, et al. A global database of COVID-19 vaccinations. Nat Hum Behav 2021;5:947-953. https://doi.org/10.1038/s41562-021-01122-8
- Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020;20:533-534. https://doi.org/10.1016/S1473-3099(20)30120-1
- Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID's innovative contribution to global health. Glob Chall 2017;1: 33-46. https://doi.org/10.1002/gch2.1018
- Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data: from vision to reality. Euro Surveill 2017;22:30494.
- Living with COVID-19: what to expect. Seoul: Korean Herald, 2021. Accessed 2022 Feb 11. Available from: http://www.koreaherald.com/view.php?ud=20211102000490.