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Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out inChina, which can cause respiratory diseases and severe pneumonia. Mathematicaland empirical models relying on the epidemic situation scale for forecastingdisease outbreaks have received increasing attention. Given its successfulapplication in the evaluation of infectious diseases scale, we propose aSusceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effectiveprediction, prevention, and control of infectious diseases. Our model is amodified susceptible-infected-recovered (SIR) model that injects undiagnosedstate and offers pre-training effective reproduction number. Our SUIR model ismore precise than the traditional SIR model. Moreover, we combine domainknowledge of the epidemic to estimate effective reproduction number, whichaddresses the initial susceptible population of the infectious disease modelapproach to the ground truth. These findings have implications for theforecasting of epidemic trends in COVID-19 as these could help the growth ofestimating epidemic situation.

A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation