README.md

In abh2k/sisd: Cumulative infected cases prediction

Installation

This SISD library can be intatalled using the following command: library(devtools) install_github("abh2k/sisd") This library can be imported using: library(SISD)

Authors

Abhinav Anand, Saurabh Kumar, and Palash Ghosh

Parameters

The function can be called as: sisd_cummulative(N, gamma, T_Current, T_Validation, T_Limit, T_Pred, Data, mu)

Parameters of the sisd_cummulative function:

• N : The total population number
• gamma : The recovery rate
• T_Current : Denotes the day when the training phase ends. After this day, the prediction phase starts.
• T_Validation : Length of Validation Period
• T_Limit : Denotes the upper limit of the number of additional days that can be added to the minimum training phase to optimally choose the training phase.
• T_Pred : Denotes the length of the prediction phase (in days).
• Data : The dataframe containing the observed data in the format specified below.
• mu (Optional) : If mu is given the the death rate is fixed. If this is None, then an optimal mu is choosen using the data-driven algorithm

Flow Chart of the above time periods:

Data format of Data parameter:

The data should be a dataframe with three columns -> Status, Date, Count.

• Status : Can contain three values -> Confirmed, Recovered, Deceased
• Date : The date in the DD-MonthName-YYYY format. For example: 14th March 2021 in this format will be 14-Mar-2021.
• Count : The number of the specified case.

Example:

WorkFlow:

The code will calculate the cumulative number of cases on the dates with the Data. The it will use the data-driven algorithm to estimate beta and mu, and will generate a dataframe with the predictions.

Example Run:

An example run of this library is available at sample_code.R file in this repository. The data we will use is available at State Wise Daily India Dataset.

abh2k/sisd documentation built on Dec. 18, 2021, 9:30 p.m.