- Propose a novel scheme to predict the impact of COVID-19 Pandemic
- Design a model based on Cloud Computing and Machine Learning for real-time prediction
- Show improved prediction accuracy compared to baseline method
- Highlight key future research directions and emerging trends
The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policy to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This has been deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.
Figure 1: Proposed Cloud based AI framework for COVID-19 related analytics
Figure 2: Global heat-map for total predicted cases for different countries as on May 4, 2020 (countries with insufficient data for prediction are shown in white)
This work utilizes the Next Generation Technologies such a Cloud Computing, Artificial Intelligence, Machine Learning to forecasting the Growth and Trend of Covid-19 Pandemic on various key dimensions.
Shreshth Tuli, Shikhar Tuli, Rakesh Tuli and Sukhpal Singh Gill, “Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing” Internet of Things: Engineering Cyber Physical Human Systems, Elsevier, Vol. 11, 2020. https://doi.org/10.1016/j.iot.2020.100222