Aim: The present study aims at explaining the epidemic situation in India for COVID-19 and forecasting the expected rise in the positive cases in India.
Objective: This study will be useful for Government authorities and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. This research would also be useful in predicting outbreak numbers with greater precision for people involved in exploring this deadly disease.
Methods: We used the Support Vector Machine (SVM) to forecast and analyze the COVID-19 situations to predict future trends. On definite trail and model training, it was observed that the number of COVID cases will increase for the next four days.
Results: The SVM model predicted accurate results. The prediction accuracy seems to best fit and indicates the cases to rise in the next coming days. Confirmed cases and the SVM predictions are close to each other, thus proving the accuracy of the SVM predictions. It was inferred that the numbers of COVID-19 instances will rise if the same trend is followed.
Conclusion: The COVID-19 outbreak is exacerbated by secondary hospital transmission. Testing, particularly of those coming in with respiratory symptoms, is essential to isolate those in hospitals. A two-stage, pre-emptive testing is recommended in symptomatic older people immediately to reduce mortality. Immediate and on-going serological surveys are required to track the epidemic level. We are flying blind at the moment. The demand for the ventilators would be 1 million. The current supply in India is projected to range from 30 K to 50 K (the US has 160 K and is still running short). Health staff involved in treating COVID-19 patients also have to shield themselves using personal protection (i.e., masks and gowns) to save themselves from being infected. Thus, SVM model predictions will give a better insight into the growth of COVID-19 cases and, therefore, will allow the government of India to take adequate measures to restrain the issue at the earliest.