TY - JOUR
T1 - The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19
AU - Lee, Chaeyoung
AU - Li, Yibao
AU - Kim, Junseok
N1 - Funding Information:
The first author (C. Lee) was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2019R1A6A3A13094308 ). Y.B. Li was supported by the Fundamental Research Funds for the Central Universities (No. XTR042019005 ). The corresponding author (J.S. Kim) was supported by Korea University Future Research Grant. The authors are grateful to the editors and the reviewers for constructive and helpful comments on the revision of this article.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/10
Y1 - 2020/10
N2 - In this article, we propose the Susceptible-Unidentified infected-Confirmed (SUC) epidemic model for estimating the unidentified infected population for coronavirus disease 2019 (COVID-19) in China. The unidentified infected population means the infected but not identified people. They are not yet hospitalized and still can spread the disease to the susceptible. To estimate the unidentified infected population, we find the optimal model parameters which best fit the confirmed case data in the least-squares sense. Here, we use the time series data of the confirmed cases in China reported by World Health Organization. In addition, we perform the practical identifiability analysis of the proposed model using the Monte Carlo simulation. The proposed model is simple but potentially useful in estimating the unidentified infected population to monitor the effectiveness of interventions and to prepare the quantity of protective masks or COVID-19 diagnostic kit to supply, hospital beds, medical staffs, and so on. Therefore, to control the spread of the infectious disease, it is essential to estimate the number of the unidentified infected population. The proposed SUC model can be used as a basic building block mathematical equation for estimating unidentified infected population.
AB - In this article, we propose the Susceptible-Unidentified infected-Confirmed (SUC) epidemic model for estimating the unidentified infected population for coronavirus disease 2019 (COVID-19) in China. The unidentified infected population means the infected but not identified people. They are not yet hospitalized and still can spread the disease to the susceptible. To estimate the unidentified infected population, we find the optimal model parameters which best fit the confirmed case data in the least-squares sense. Here, we use the time series data of the confirmed cases in China reported by World Health Organization. In addition, we perform the practical identifiability analysis of the proposed model using the Monte Carlo simulation. The proposed model is simple but potentially useful in estimating the unidentified infected population to monitor the effectiveness of interventions and to prepare the quantity of protective masks or COVID-19 diagnostic kit to supply, hospital beds, medical staffs, and so on. Therefore, to control the spread of the infectious disease, it is essential to estimate the number of the unidentified infected population. The proposed SUC model can be used as a basic building block mathematical equation for estimating unidentified infected population.
KW - COVID-19
KW - Epidemic model
KW - Least-squares fitting
UR - http://www.scopus.com/inward/record.url?scp=85087653950&partnerID=8YFLogxK
U2 - 10.1016/j.chaos.2020.110090
DO - 10.1016/j.chaos.2020.110090
M3 - Article
AN - SCOPUS:85087653950
SN - 0960-0779
VL - 139
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
M1 - 110090
ER -
