The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19

Chaeyoung Lee, Yibao Li, Junseok Kim

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Article number110090
JournalChaos, Solitons and Fractals
Publication statusPublished - 2020 Oct

Bibliographical note

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


  • COVID-19
  • Epidemic model
  • Least-squares fitting

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • General Mathematics
  • General Physics and Astronomy
  • Applied Mathematics


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