  SARS-COV-19 is the most prominent issue which many countries face today. The
frequent changes in infections, recovered and deaths represents the dynamic
nature of this pandemic. It is very crucial to predict the spreading rate of
this virus for accurate decision making against fighting with the situation of
getting infected through the virus, tracking and controlling the virus
transmission in the community. We develop a prediction model using statistical
time series models such as SARIMA and FBProphet to monitor the daily active,
recovered and death cases of COVID-19 accurately. Then with the help of various
details across each individual patient (like height, weight, gender etc.), we
designed a set of rules using Semantic Web Rule Language and some mathematical
models for dealing with COVID19 infected cases on an individual basis. After
combining all the models, a COVID-19 Ontology is developed and performs various
queries using SPARQL query on designed Ontology which accumulate the risk
factors, provide appropriate diagnosis, precautions and preventive suggestions
for COVID Patients. After comparing the performance of SARIMA and FBProphet, it
is observed that the SARIMA model performs better in forecasting of COVID
cases. On individual basis COVID case prediction, approx. 497 individual
samples have been tested and classified into five different levels of COVID
classes such as Having COVID, No COVID, High Risk COVID case, Medium to High
Risk case, and Control needed case.
