Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics

Abstract

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.

Keywords: 00-01; 99-00; COVID-19; Coronavirus; Epidemic; FbProphet; Forecasting; Logistic; Modeling.

Fig. 1

All infected countries with COVID-19 pandemic by June 16, 2020. The warmer the color is, the more infections are in the country.

Fig. 2

The world’s top 10 countries with the number of confirmed COVID-19 cases.

Fig. 3

The Logistic growth modeling of COVID-19 and SARS 2003 in China.

Fig. 4

Framework of forecasting the trend of COVID-19 with Logistic model and Prophet. The key point with fastest growth rate t_fast is fitted from Logistic model based on epidemical data of COVID-19. By estimating the time $t_{\max }$ with maximum cases, the cap value of epidemic size Q_top is calculated by using Logistic modeling method, then the cap value is used to the Prephet model, and model the full epidemic trend of COVID-19.

Fig. 5

Number of accumulated confirmed cases (purple), recovered (green), death (black), and active confirmed cases (blue) by the hybrid Logistic and Prophet model for Global (a), Brazil (b), Russia (c), India (d), Peru (e), and Indonesia (f). Actual data of accumulated confirmed infections were fitted onto the curve (red circles). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

The predicting results of Prophet model for the total confirmed case number in Global (a), Brazil (b), Russia (c), India (d), Peru (e), and Indonesia (f). The line with a 95% confidence interval is confirmed cases and the true reported values are marked as circles. The horizontal axis represents the date, and the vertical axis represents the cumulative number of infections.

Fig. 7

Number of Daily confirmed cases in Global (a), Brazil (b), Russia (c), India (d), Peru (e), and Indonesia (f) by June 16, 2020.