Machine learning in handling disease outbreaks: a comprehensive review

The changes in the global environment have made impact on evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with technological advances. This work aims identify and analyze previous studies machine learning applications handling disease outbreaks. Bibliometric analysis was conducted 3,447 scientific articles selected from Scopus database. Further, latent dirichlet (LDA) method applied topic hotspots attempting deepen analysis. LDA results identified twelve that can classified into three themes: COVID-19 disease, miscellaneous public opinion outbreaks for discussion. study reveals structure this domain is dominated by research caused pathogens some genetic factors. A huge amount multimodal medical data used prediction, forecasting, classification, screening purposes resolve many problems including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, pathogenesis. Public opinions toward also an interesting addition perceptions response protocol policies.