RashiMohta/COVID-19-cases-prediction: Jump-drop adjusted prediction of cumulative infected cases using the modified SIS model
Jump-drop adjusted prediction of cumulative infected cases (ex: COVID-19) using modified SIS model. SIS model assumes that reinfection is possible. We use the modified SIS model, proposed by Anand et al., 2021 (Epidemiologic Methods), a dynamic data-driven algorithm to estimate the model parameters based on an optimally chosen training phase to predict the number of cumulative jumps infected cases. Adjusts daily confirmed cases using C1, C2, C3 metrics (Fricker Jr et al., 2007; Statistics in Medicine) and predicts using original and adjusted data. It returns the prediction with a lesser mean square error value than the modified SIS model.
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Package details |
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| Author | Rashi Mohta, Prathapani Sravya, Dr. Palash Ghosh |
| Maintainer | Rashi Mohta <rashi@iitg.ac.in> <rashimohta15@gmail.com> |
| License | GPL-3 |
| Version | 0.1.0 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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