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

The outbreaks of a wide range of infectious diseases have been modeled using Eq. 4. However, for the COVID-19 outbreak prediction, due to the strict measures enforced by authorities, the susceptibility to infection has been manipulated dramatically. For example, in China, Italy, France, Hungary and Spain the SIR model cannot present promising results, as individuals committed voluntarily to quarantine and limited their social interaction. However, for countries where containment measures were delayed (e.g., United States) the model has shown relative accuracy [12] . Figure. 1 shows the inaccuracy of conventional models applied to the outbreak in Italy by comparing the actual number of confirmed infections and epidemiological model predictions 1 . The SEIR models through considering the significant incubation period during which individuals have been infected showed progress in improving the model accuracy for Varicella and Zika outbreak [13, 14] . SEIR models assume that the incubation period is a random variable and similarly to the SIR model, there would be a disease-free-equilibrium [15, 16] . It is worth mentioning that SEIR model will not work well where the parameters are non-stationary through time [17] . A key cause of non-stationarity is where the social mixing (which determines the contact network) changes through time. Social mixing determines the reproductive number 0 which is the number of susceptible individuals that an infected person will infect. Where 0 is less than 1 the epidemic will die out. Where it is greater than 1 it will spread. 0 for COVID-19 prior to lockdown was estimated as a massive 4 presenting a pandemic. It is expected that lockdown measures should bring 0 down to less than 1. the KEY reason why SEIR models are difficult to fit for COVID-19 is non-stationarity of mixing, caused by nudging (step-by-step) intervention measures.