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Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea
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Soyoung Kim, Yu Bin Seo, Eunok Jung
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Epidemiol Health. 2020;42:e2020026. Published online April 13, 2020
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DOI: https://doi.org/10.4178/epih.e2020026
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Abstract
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Abstract
OBJECTIVES Since the report of the first confirmed case in Daegu on February 18, 2020, local transmission of coronavirus disease 2019 (COVID-19) in Korea has continued. In this study, we aimed to identify the pattern of local transmission of COVID-19 using mathematical modeling and predict the epidemic size and the timing of the end of the spread.
METHODS We modeled the COVID-19 outbreak in Korea by applying a mathematical model of transmission that factors in behavioral changes. We used the Korea Centers for Disease Control and Prevention data of daily confirmed cases in the country to estimate the nationwide and Daegu/Gyeongbuk area-specific transmission rates as well as behavioral change parameters using a least-squares method.
RESULTS The number of transmissions per infected patient was estimated to be about 10 times higher in the Daegu/Gyeongbuk area than the average of nationwide. Using these estimated parameters, our models predicts that about 13,800 cases will occur nationwide and 11,400 cases in the Daegu/Gyeongbuk area until mid-June.
CONCLUSIONS We mathematically demonstrate that the relatively high per-capita rate of transmission and the low rate of changes in behavior have caused a large-scale transmission of COVID-19 in the Daegu/Gyeongbuk area in Korea. Since the outbreak is expected to continue until May, non-pharmaceutical interventions that can be sustained over the long term are required.
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Summary
Korean summary
본 논문은 행동변화를 고려한 수학적 모델을 이용하여 코로나바이러스병-19의 유행 양상을 분석하고 총 환자수와 유행기간을 예측하고자 한다. 질병관리본부 확진자 데이터를 이용하여 전국과 대구·경북 지역의 감염전파율과 행동변화율을 추정하였다. 3월 10일까지의 데이터를 기준으로 전국적으로 6월 중순까지 약 13,000명, 대구·경북지역의 경우 5월 말까지 약 11,000명의 환자가 발생할 것으로 예측된다.
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Citations
Citations to this article as recorded by
- A framework for incorporating behavioural change into individual‐level spatial epidemic models
Madeline A. Ward, Rob Deardon, Lorna E. Deeth Canadian Journal of Statistics.2024;[Epub] CrossRef - Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data
Kyulhee Han, Bogyeom Lee, Doeun Lee, Gyujin Heo, Jooha Oh, Seoyoung Lee, Catherine Apio, Taesung Park Scientific Reports.2024;[Epub] CrossRef - Mathematical modeling and control of Covid‐19
Atena Ghasemabadi Mathematical Methods in the Applied Sciences.2024; 47(12): 10478. CrossRef - Assessment of age-dependent effects during the transmission of Omicron and the outcomes of booster campaign vaccination strategies
Yang Deng, Daihai He, Yi Zhao Applied Mathematical Modelling.2024; 133: 148. CrossRef - Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee Yonsei Medical Journal.2023; 64(1): 1. CrossRef - Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa F1000Research.2023; 12: 445. CrossRef - Factors Influencing Infection Anxiety in Korean Male Firefighters Due to COVID-19 Infection Status
Seung-Woo Han, Hyun-Ok Jung Healthcare.2023; 11(11): 1623. CrossRef - Neural network method and multiscale modeling of the COVID-19 epidemic in Korea
Ziqian Li, Jiwei Jia, Guidong Liao, Young Ju Lee, Siyu Liu The European Physical Journal Plus.2023;[Epub] CrossRef - Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa F1000Research.2023; 12: 445. CrossRef - Studying the Effect of Particulate Matter as SARS-CoV-2 Transmitters
Abdulrahim R. Hakami, Gasim Dobie Journal of Public Health Research.2022; 11(1): jphr.2021.2521. CrossRef - Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective
Manimuthu Arunmozhi, Jinil Persis, V. Raja Sreedharan, Ayon Chakraborty, Tarik Zouadi, Hanane Khamlichi International Journal of Quality & Reliability Management.2022; 39(9): 2184. CrossRef - Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic
Ming Guan Journal of Epidemiology and Global Health.2022; 12(1): 40. CrossRef - Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics
Ali AlArjani, Md Taufiq Nasseef, Sanaa M. Kamal, B. V. Subba Rao, Mufti Mahmud, Md Sharif Uddin Arabian Journal for Science and Engineering.2022; 47(8): 10163. CrossRef - Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges
Jinxing Guan, Yang Zhao, Yongyue Wei, Sipeng Shen, Dongfang You, Ruyang Zhang, Theis Lange, Feng Chen Medical Review.2022; 2(1): 89. CrossRef - Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra ISA Transactions.2022; 124: 57. CrossRef - Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
Hadeel AlQadi, Majid Bani-Yaghoub, Yury E. Khudyakov PLOS ONE.2022; 17(4): e0265815. CrossRef - A Mathematical Model of Transmission Dynamics of SARS-CoV-2 (COVID-19) with an Underlying Condition of Diabetes
Samuel Okyere, Joseph Ackora-Prah, Shih Pin Chen International Journal of Mathematics and Mathematical Sciences.2022; 2022: 1. CrossRef - Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore
Murali Krishna Gurram, Min Xian Wang, Yi-Chen Wang, Junxiong Pang Scientific Reports.2022;[Epub] CrossRef - Compartmental structures used in modeling COVID-19: a scoping review
Lingcai Kong, Mengwei Duan, Jin Shi, Jie Hong, Zhaorui Chang, Zhijie Zhang Infectious Diseases of Poverty.2022;[Epub] CrossRef - Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study
Youngsuk Ko, Victoria May Mendoza, Renier Mendoza, Yubin Seo, Jacob Lee, Jonggul Lee, Donghyok Kwon, Eunok Jung Journal of Korean Medical Science.2022;[Epub] CrossRef - Investigating Online Learning Process in Business School: Case Study from Business School in Jakarta, Indonesia
Sekar W. Prasetyaningtyas, Agustian B. Prasetya International Journal of Information and Education Technology.2022; 12(9): 964. CrossRef - Evolution and consequences of individual responses during the COVID-19 outbreak
Wasim Abbas, Masud M. A., Anna Park, Sajida Parveen, Sangil Kim, Siew Ann Cheong PLOS ONE.2022; 17(9): e0273964. CrossRef - Managing bed capacity and timing of interventions: a COVID-19 model considering behavior and underreporting
Victoria May P. Mendoza, Renier Mendoza, Youngsuk Ko, Jongmin Lee, Eunok Jung AIMS Mathematics.2022; 8(1): 2201. CrossRef - Model-informed COVID-19 exit strategy with projections of SARS-CoV-2 infections generated by variants in the Republic of Korea
Sung-mok Jung, Kyungmin Huh, Munkhzul Radnaabaatar, Jaehun Jung BMC Public Health.2022;[Epub] CrossRef - Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism
Oualid Limam, Mohamed Limam Biomechanics and Modeling in Mechanobiology.2021; 20(1): 379. CrossRef - Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra Journal of Control, Automation and Electrical Systems.2021; 32(2): 326. CrossRef - Non-pharmaceutical interventions during the COVID-19 pandemic: A review
Nicola Perra Physics Reports.2021; 913: 1. CrossRef - Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra IEEE Journal of Biomedical and Health Informatics.2021; 25(3): 615. CrossRef - Graphs in the COVID-19 news: a mathematics audit of newspapers in Korea
Oh Nam Kwon, Chaereen Han, Changsuk Lee, Kyungwon Lee, Kyeongjun Kim, Gyeongha Jo, Gangwon Yoon Educational Studies in Mathematics.2021; 108(1-2): 183. CrossRef - Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model
Gregory L. Watson, Di Xiong, Lu Zhang, Joseph A. Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W. Rimoin, Marc A. Suchard, Christina M. Ramirez, Virginia E. Pitzer PLOS Computational Biology.2021; 17(3): e1008837. CrossRef - Using the Weibull distribution to model COVID-19 epidemic data
Vitor Hugo Moreau Model Assisted Statistics and Applications.2021; 16(1): 5. CrossRef - Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown
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Thomas Usherwood, Zachary LaJoie, Vikas Srivastava Scientific Reports.2021;[Epub] CrossRef - Behavioral Risk Modeling for Pandemics: Overcoming Challenges and Advancing the Science
Ellen P. Carlin, Koya C. Allen, Jeffrey J. Morgan, Jean-Paul Chretien, Suzan Murray, Deborah Winslow, Dawn Zimmerman Health Security.2021; 19(4): 447. CrossRef - Structure of epidemic models: toward further applications in economics
Toshikazu Kuniya The Japanese Economic Review.2021; 72(4): 581. CrossRef - How Important Is Behavioral Change during the Early Stages of the COVID-19 Pandemic? A Mathematical Modeling Study
Jongmin Lee, Seok-Min Lee, Eunok Jung International Journal of Environmental Research and Public Health.2021; 18(18): 9855. CrossRef - Factors shaping the COVID-19 epidemic curve: a multi-country analysis
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Theyazn H. H. Aldhyani, Hasan Alkahtani Life.2021; 11(11): 1118. CrossRef - Health and pathology: a brief history of the biopolitics of US mathematics education
Ryan Ziols, Kathryn L. Kirchgasler Educational Studies in Mathematics.2021; 108(1-2): 123. CrossRef - Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model
Chaeyoung Lee, Soobin Kwak, Sangkwon Kim, Youngjin Hwang, Yongho Choi, Junseok Kim Chaos, Solitons & Fractals.2021; 153: 111556. CrossRef - Risk Perceptions, Knowledge and Behaviors of General and High-Risk Adult Populations Towards COVID-19: A Systematic Scoping Review
Nathalie Clavel, Janine Badr, Lara Gautier, Mélanie Lavoie-Tremblay, Jesseca Paquette Public Health Reviews.2021;[Epub] CrossRef - Modeling COVID-19 Transmission Dynamics With Self-Learning Population Behavioral Change
Tsz-Lik Chan, Hsiang-Yu Yuan, Wing-Cheong Lo Frontiers in Public Health.2021;[Epub] CrossRef - The first year of covid-19 in croatia - a mathematical model
Tibor Rodiger, Edgar Glavaš, Ivan Kovač Croatian Regional Development Journal.2021; 2(2): 32. CrossRef - Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
Janik Schüttler, Reinhard Schlickeiser, Frank Schlickeiser, Martin Kröger Physics.2020; 2(2): 197. CrossRef - Preliminary Clinical and Epidemiological Analysis of the First 1,000 Pediatric COVID-19 Cases in Moscow Region
Elena R. Meskina Journal of microbiology, epidemiology and immunobiology.2020; 97(3): 202. CrossRef - Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths
Vitor Hugo Moreau Brazilian Journal of Microbiology.2020; 51(3): 1109. CrossRef - Mathematical modeling for infectious viral disease: The COVID‐19 perspective
Hafeez Aderinsayo Adekola, Ibrahim Ayoade Adekunle, Haneefat Olabimpe Egberongbe, Sefiu Adekunle Onitilo, Idris Nasir Abdullahi Journal of Public Affairs.2020;[Epub] CrossRef - Estimating the Transmission Risk of COVID-19 in Nigeria: A Mathematical Modelling Approach
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Eunsun Jeong, Munire Hagose, Hyungul Jung, Moran Ki, Antoine Flahault International Journal of Environmental Research and Public Health.2020; 17(24): 9571. CrossRef
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Ebola virus disease outbreak in Korea: use of a mathematical model and stochastic simulation to estimate risk
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Youngsuk Ko, Seok-Min Lee, Soyoung Kim, Moran Ki, Eunok Jung
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Epidemiol Health. 2019;41:e2019048. Published online November 24, 2019
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DOI: https://doi.org/10.4178/epih.e2019048
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13,041
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Abstract
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Abstract
OBJECTIVES According to the World Health Organization, there have been frequent reports of Ebola virus disease (EVD) since the 2014 EVD pandemic in West Africa. We aim to estimate the outbreak scale when an EVD infected person arrives in Korea.
METHODS Western Africa EVD epidemic mathematical model SEIJR or SEIJQR was modified to create a Korean EVD outbreak model. The expected number of EVD patients and outbreak duration were calculated by stochastic simulation under the scenarios of Best case, Diagnosis delay, and Case missing.
RESULTS The 2,000 trials of stochastic simulation for each scenario demonstrated the following results: The possible median number of patients is 2 and the estimated maximum number is 11 when the government intervention is proceeded immediately right after the first EVD case is confirmed. With a 6-day delay in diagnosis of the first case, the median number of patients becomes 7, and the maximum, 20. If the first case is missed and the government intervention is not activated until 2 cases of secondary infection occur, the median number of patients is estimated at 15, and the maximum, at 35.
CONCLUSIONS Timely and rigorous diagnosis is important to reduce the spreading scale of infection when a new communicable disease is inflowed into Korea. Moreover, it is imperative to strengthen the local surveillance system and diagnostic protocols to avoid missing cases of secondary infection.
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Summary
Korean summary
본 연구는 수학적 모델과 확률 시뮬레이션 기법을 이용하여 국내에 유입되지 않았던 에볼라바이러스병(EVD)의 확산 위험도를 정량적으로 예측하는 첫 번째 연구이다. 또한 이 연구를 통해 에볼라바이러스병 환자의 유입 시 발생 가능한 진단 지연 혹은 유입 미인지 상황을 가정하여 발생할 수 있는 2차 감염자 수 및 감염 종식까지의 기간을 계산했고 에볼라바이러스 유입 대비 실시간모니터링의 중요성과 확산 시 상황에 따른 최대 일일 환자수를 합리적으로 제시할 수 있다.
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Citations
Citations to this article as recorded by
- Estimating the Transmission Risk of COVID-19 in Nigeria: A Mathematical Modelling Approach
Irany FA, Akwafuo SE, Abah T, Mikler AR Journal of Health Care and Research.2020; 1(3): 135. CrossRef
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