sisd_cummulative: Predict with or without adjusting the data

In RashiMohta/COVID-19-cases-prediction: Jump-drop adjusted prediction of cumulative infected cases using the modified SIS model

Predict with or without adjusting the data

Description

Prediction of Cumulative number of cases using data driven modified SIS model

Usage

sisd_cummulative(
  population = 18710922,
  gamma = 1/14,
  cur_date = "2020-5-29",
  start_date = "2020-3-13",
  last_n_day = 20,
  last_limit = 30,
  next_n_days = 20,
  data,
  adjusted = 0L,
  ub_for_adjustment = 5,
  bound_metric = "C3_1day",
  df_confirmed_values,
  mu
)

Arguments

population	number of people in the state
gamma	recovery rate
cur_date	current date for start of prediction phase
start_date	start date in the considered dataset
last_n_day	number of days in training phase
last_limit	maximum number of days in the validation period
next_n_days	number of days in the prediction phase
data	state wise daily cases adjusted data for the given state
adjusted	predictions can be made with and without adjustment in data
ub_for_adjustment	upper bound for the duration of a jump or drop
bound_metric	C1, C2, or C3 metric can be selected
df_confirmed_values	Dataframe of dates and observed number of daily confirmed cases
mu	mortality rate of the infection

Value

Returns graph showing the training phase and prediction of cummulative number of cases, data frame of cummulative cases in training and prediction phase, mean square error of validation and prediction period

Note

This function is called in the function "compare_results" It calls the function "plot_adjustment" for a visual depiction of the adjustments made by the given metric.

RashiMohta/COVID-19-cases-prediction documentation built on Oct. 26, 2024, 9:48 a.m.