Supplementary Data for Viral Host Identification Using Machine Learning and Viral Genome Sequences

The supplementary data for the MSc Thesis titled \"Viral Host Identification Using Machine Learning and Viral Genome Sequences.\" Virus host identification is important for emergency disease preparedness and effective response planning in both human and animal health. Host identification can be done using viral genome sequences and machine learning. This thesis seeks to address the topic of virus host identification using machine learning through identifying the current body of evidence and applying machine learning classifiers to the hemagglutinin sequence of H3 influenza. In chapter 2, a scoping review was performed. Data were charted for 53 publications that were identified to be within the scope. There was a wide variety in methodology used within these publications. In chapter 3, different classifiers were utilized to assign seven hosts to H3 influenza viral sequences. This was achieved with high accuracy. These applications show the potential of machine learning as a method for host identification and the different circumstances in which it could be applied within a virus management framework.