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摘要


Due to the outbreak of the COVID-19 epidemic in 2020, people are very worried about the situation of the epidemic. Being able to model the number of cases over time is crucial. This model is used to predict numbers of people who may get diseases in the future. We construct a model with some unknown parameters and decide those parameters by making the mean square error of the predicted value with actual data. The epidemic model is generalized SEIR model and it helps us to match with reality. The data we use are from database built by Hopkins University. The result shows that disease will finally disappear. We can see that the virus increase at a very fast speed at first but begin to decrease from the curve. It is because that more people to take actions for protecting themselves. This result shows the virus can be defeated if we put effort to stop spreading them.

參考文獻


Peng L., Yang W., Zhang D., Zhuge C., Hong L. Epidemic analysis ofCOVID-19 in China by dynamical modeling. MedRxiv Epidemiol. 2020 doi: 10.1101/2020.02.16.20023465. https://arxiv.org/abs/2002.06563
Cheynet E. Generalized SEIR Epidemic Model (Fitting and Computa- tion) ; website url: https://it.mathworks.com/matlabcentral/fileexchange/74545-generalized-seir-epidemic-model-fitting-and-computation
COVID-19 Data Repository by the Center for Systems Science and En- gineering (CSSE) at Johns Hopkins University. url:https://github.com/ CSSEGISandData/COVID-19
Dandekar R., Barbastathis G. Neural Network aided quarantine control model estimation of global COVID-19 spread. url:https://arxiv.org/abs/2004. 02752
Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) url:https://www.who.int/docs/default-source/coronaviruse/ who-china-joint-mission-on-covid-19-final-report

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