Machine Learning Model to Detect Emergency in the Global Pandemic




Raju, Rini

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It is crucial to use advanced machine learning models to improve disaster and emergency response in critical events around the world. In this paper, we introduce a new model, which can highlight the essential help that people need in times of emergency. Based on the user comments, we choose the emergency response that can use the optimal resources to address the maximum needs. The new features in the model help to analyze each person's response from political, social, and health perspectives. This approach helps to recognize different types of users to improve emergency response in the time of the global pandemic. Also, collecting pandemic data from different online resources, makes this research more powerful in feature extraction to improve the model accuracy based on emergency data. This model can help health applications to improve disaster response time and services.



COVID-19, Machine learning