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Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics
Ist Teil von
Chaos, solitons and fractals, 2020-10, Vol.139, p.110058-110058, Article 110058
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2020
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
•A hybrid prediction model for COVID-19 based on Logistic and Prophet is proposed.•The epidemic trend of COVID-19 in global, Brazil, Russia, India, Peru and Indonesia are predicted by our proposed model.•Three significant points are summarized from our modeling results.•The number of accumulated infections in global by late October is estimated to be 14.12 million.
COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it’s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively.