Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in China, which can cause respiratory diseases and severe pneumonia. Epidemic models relying on the incidence rate of new cases for forecasting epidemic outbreaks have received increasing attention. However, many prior works in this area mostly focus on the application of the traditional SIR model and disregard the transmission characteristics of 2019-nCoV, exceptionally the infectious of undiagnosed cases. Here , we propose a SUIR model based on the classical SIR model object to supervise the effective prediction, prevention, and control of infectious diseases. SUIR model adds a unique U (Undiagnosed) state of the epidemic and divides the population into four states: S (Susceptible), U (Undiagnosed), I (Infectious and Uninfectious), and R (Recovered). This approach enables us to predict the incidence of 2019-nCoV effectively and the clear advantage of the model accuracy more reliable than the traditional SIR model .
Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in China, which can cause respiratory diseases and severe pneumonia. Mathematical and empirical models relying on the epidemic situation scale for forecasting disease outbreaks have received increasing attention. Given its successful application in the evaluation of infectious diseases scale , we propose a Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effective prediction, prevention, and control of infectious diseases. Our model is a modified susceptible-infected-recovered (SIR) model that injects undiagnosed state and offers pre-training effective reproduction number. Our SUIR model is more precise than the traditional SIR model. Moreover, we combine domain knowledge of the epidemic to estimate effective reproduction number, which addresses the initial susceptible population of the infectious disease model approach to the ground truth. These findings have implications for the forecasting of epidemic trends in COVID-19 as these could help the growth of estimating epidemic situation .
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Epidemic
Mathematical and empirical
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incidence rate of new cases for forecasting epidemic
epidemic situation scale for forecasting disease
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However, many prior works in this area mostly focus on the application of the traditional SIR model and disregard the transmission characteristics of 2019-nCoV, exceptionally the infectious of undiagnosed cases. Here
Given its successful application in the evaluation of infectious diseases scale
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SUIR model based on the classical SIR model object to supervise
Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer
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SUIR model adds a unique U (Undiagnosed) state
Our model is a modified susceptible-infected-recovered (SIR) model that injects undiagnosed state and offers pre-training effective reproduction number. Our SUIR model is more precise than the traditional SIR model. Moreover, we combine domain knowledge
R
and divides the population into four states: S (Susceptible), U (Undiagnosed), I (Infectious and Uninfectious), and R (Recovered). This approach enables us to predict the incidence of 2019-nCoV effectively and the clear advantage of
to estimate effective reproduction number, which addresses the initial susceptible population of the infectious disease model approach to
R
model accuracy more reliable than the traditional SIR model
ground truth. These findings have implications for the forecasting of epidemic trends in COVID-19 as these could help the growth of estimating epidemic situation