Analysis and Prediction of Outbreaks Using ML Models

Abstract

ML algorithm used dataset time series coronavirus dataset to predict the risk of Covid-19. The forecast of Covid-19 is positive, as there are fears all over the world that the epidemic will impact a lot of people around the world. In this analysis, ML algorithms were used to predict the effects of coronavirus outbreaks in Ethiopia. Anticipation of an outbreak in Ethiopia may help policy makers and governments to take inclusive and necessary action. In this research, SVM and PR models were used to forecast the spread of pandemic trends in Ethiopia. In this research, SVM and PR models were used to forecast the spread of pandemic trends in Ethiopia. Typical datasets contain a time series of actual data and make predictions for the next 30 days using SVM and PR. Shape the investigation outcome to check that SVM performs better in current confirmed cases, recovered cases and reported dataset deaths. According to the forecast model, the confirmed case and death rate will rise over the next 30 days. The PR model shows weak results in all three situations. Because of the complexity of the data set. We conclude that ML prediction depends on current data, which can allow us to know the next state. It was used to take remedial action for the body concerned.