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

One can calculate that standard epidemiological models can be effective and reliable only if (a) the social interactions are stationary through time (i.e., no changes in interventions or control measures), or (b) there exists a great deal of knowledge of class R with which to compute Eq. 3. Often to acquire information on class R, several novel models included data from social media or call data records (CDR), which showed promising results [18] [19] [20] [21] [22] [23] [24] [25] . However, observation of the behavior of COVID-19 in several countries demonstrates a high degree of uncertainty and complexity [26] . Thus, for epidemiological models to be able to deliver reliable results, they must be adapted to the local situation with an insight into susceptibility to infection [27] . This imposes a huge limit on the generalization ability and robustness of conventional models. Advancing accurate models with a great generalization ability to be scalable to model both the regional and global pandemic is, thus, essential [28] .