Prediction of the COVID-19 epidemic trends based on SEIR and AI models
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Abstract
In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.
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SciScore for 10.1101/2020.04.21.20074138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has some limitations. Firstly, we built the model according to the conventional infection model, without considering the parameter fluctuation caused by the possible super disseminator and virus variation in the SEIR model. Secondly, we use multiple data including epidemic data, urban data, migration data to predict the …
SciScore for 10.1101/2020.04.21.20074138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has some limitations. Firstly, we built the model according to the conventional infection model, without considering the parameter fluctuation caused by the possible super disseminator and virus variation in the SEIR model. Secondly, we use multiple data including epidemic data, urban data, migration data to predict the epidemic trends of COVID-19 in other parts of China, without considering the potential impact of other factors on COVID-19 in the DNN and RNN model. Thirdly, in our models, our original parameters are based on previous studies and experience from SARS control. Besides, the data we using is based on the data before March 3, 2020. With the progression of COVID-19, the model parameters will change greatly because of more and more data.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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