The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19
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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.
Palabras clave
Epidemic model; Least-squares fitting; COVID-19Link para o recurso
https://doi.org/10.1016/j.chaos.2020.110090Collections
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