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

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  • Lee, Chaeyoung
  • Li, Yibao
  • Kim, Junseok

Abstract

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.

Suggested Citation

  • Lee, Chaeyoung & Li, Yibao & Kim, Junseok, 2020. "The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304872
    DOI: 10.1016/j.chaos.2020.110090
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    References listed on IDEAS

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    1. Reis, Ruy Freitas & de Melo Quintela, Bárbara & de Oliveira Campos, Joventino & Gomes, Johnny Moreira & Rocha, Bernardo Martins & Lobosco, Marcelo & Weber dos Santos, Rodrigo, 2020. "Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Rihan, F.A. & Al-Mdallal, Q.M. & AlSakaji, H.J. & Hashish, A., 2019. "A fractional-order epidemic model with time-delay and nonlinear incidence rate," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 97-105.
    3. Rosa, Silvério & Torres, Delfim F.M., 2018. "Optimal control of a fractional order epidemic model with application to human respiratory syncytial virus infection," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 142-149.
    4. Alam, Muntasir & Tanaka, Masaki & Tanimoto, Jun, 2019. "A game theoretic approach to discuss the positive secondary effect of vaccination scheme in an infinite and well-mixed population," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 201-213.
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    Cited by:

    1. Chaeyoung Lee & Soobin Kwak & Junseok Kim, 2021. "Controlling COVID-19 Outbreaks with Financial Incentives," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
    2. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Hajri, Youssra & Allali, Amina & Amine, Saida, 2024. "A delayed deterministic and stochastic SIRICV model: Hopf bifurcation and stochastic analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 98-121.
    4. Lee, Chaeyoung & Kwak, Soobin & Kim, Sangkwon & Hwang, Youngjin & Choi, Yongho & Kim, Junseok, 2021. "Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).

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