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. Interactive prediction graphs can be seen at: https://collaboration.coraltele.com/covid/.
To install and run the dynamic real-time prediction webapp on your server run the following commands:
$ git clone https://github.com/shreshthtuli/covid-19-prediction.git
$ mv covid-19-prediction covid
$ chmod +x run.sh
$ ./run.sh
To access your server go to $HOSTNAME/covid/ from your browser. The webapp is hosted on https://collaboration.coraltele.com/covid2/ where graphs get updated daily based on new data.
We use the Our World in Data dataset for predicting number of new cases and deaths in various countries.
Shreshth Tuli ([email protected])
If you use our static model, please cite:
@article{tuli2020predicting,
title = "Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing",
journal = "Internet of Things",
pages = "100--222",
year = "2020",
issn = "2542-6605",
doi = "https://doi.org/10.1016/j.iot.2020.100222",
url = "http://www.sciencedirect.com/science/article/pii/S254266052030055X",
author = "Shreshth Tuli and Shikhar Tuli and Rakesh Tuli and Sukhpal Singh Gill",
}
If you use our dynamic model, please cite:
@article{tuli2020modelling,
title={Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types},
author={Tuli, Shreshth and Tuli, Shikhar and Verma, Ruchi and Tuli, Rakesh},
journal={medRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory Press}
}
- 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, ISSN: 2542-6605, Elsevier Press, Amsterdam, The Netherlands, May 2020. (Open access link)
- Shreshth Tuli, Shikhar Tuli, Ruchi Verma and Rakesh Tuli, Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types. medRxiv, June 2020. (Open access link)