COVID-19 Outbreak Prediction with Machine Learning

Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson

Data were collected from https://www.worldometers.info/coronavirus/country for five countries, including Italy, Germany, Iran, USA, and China on total cases over 30 days. Figure 2 presents the total case number (cumulative statistic) for the considered countries. Currently, to contain the outbreak, the governments have implemented various measures to reduce transmission through inhibiting people's movements and social activities. Although for advancing the epidemiological models information on changes in social distancing is essential, for modeling with machine learning no assumption is required. As can be seen in Figure 2 , the growth rate in China is greater than that for Italy, Iran, Germany and the USA in the early weeks of the disease. The next step is to find the best model for the estimation of the time-series data. Logistic, Linear, Logarithmic, Quadratic, Cubic, Compound, Power and exponential equations ( Table 2 ) are employed to develop the desired model. A, B, C, µ, and L are parameters (constants) that characterize the above-mentioned functions. These constants need to be estimated to develop an accurate estimation model. One of the goals of this study was to model time-series data based on the logistic microbial growth model. For this purpose, the modified equation of logistic regression was used to estimate and predict the prevalence (i.e., I/Population at a given time point) of disease as a function of time. Estimation of the parameters was performed using evolutionary algorithms like GA, particle swarm optimizer, and the grey wolf optimizer. These algorithms are discussed in the following.