Study and Identification of Various Factors for Outbreak Classification Towards Sustainable Healthcare Sector

Recently the world has witnessed a global outbreak, where the spread of Coronavirus was declared a pandemic situation due to its global spread. In past also, epidemic and the pandemic situation has been observed due to SARS, H1 N1 influenza, etc. During a global health emergency i.e., during the pandemic situation, there is always a need for a huge number of medical facilities, etc. throughout the world. The shortage of these facilities can cause a loss of a plethora of lives. The spread of any virus or the phase of conversion of an epidemic or endemic situation into a pandemic situation can be tracked by measuring or tracking important parameters, and a timely classification and prediction of a pandemic situation can help the world in every sense. In this paper, an outbreak classification framework is presented to predict the pandemic situation from an endemic situation. To achieve this goal, a rigorous study has been done on various existing resources such as WHO, CDC, etc. to identify important factors for modeling the proposed system. Various factors such as Mode of transmission, Infectiousness, Contagiousness of disease, Incubation Period, Contagiousness of the Asymptomatic Carriers, Severity, Population Density, and Herd Immunity are identified. Furthermore, to test the efficacy of the identified factors, SVM and Random Forest-based classification models are developed for outbreak classification. The identified features are able to generate accuracy of 84.09 and 90.90% which shows their applicability for predicting outbreaks.