Prediction: Prediciton

In YuanchenZhu2020/DemoPreTurningPointsCOVID19: Demo of Tracking and Forecasting Milepost Moments of COVID-19

Description

Integrate functions above, and handle a special situation (removedrate > 1).

Usage

prediction(RawData, M, Beginning_Time)

Arguments

RawData	dataframe with data and four ordered variables, i.e. the day t, the cumulative confirmed cases up to the given day t, the daily confirmed cases at day t, the daily recovered ones at day t, the daily deaths at day t.
M	the selection of time window.
Beginning_Time	the selection of beginning time, which must be in the formate "%y-%m-%d" as a character.

Value

List contains important info. in the calculation:

• Beginning_Time: Hyper Param. the selection of beginning time, which must be in the formate "%y-%m-%d" as a character.

• M: Hyper Param. the selection of time window.

• indicators: the iconic indicators generated from real data.

• velocity: A list contains 4 elements:

  • infection_rate_velocity: the velocity of K.

  • removed_rate_velocity: the velocity of I.

  • corr.infection: vvector of corrected M_K and T_K.

  • corr.removed: vector of corrected M_I and T_I.

• pre_indicators: dataframe with 4 future indicators:

  • date: the exact day in the formate "%y-%m-%d" as a character

  • infection_rate.pre: prediction result of infection rate

  • removed_rate.pre: prediction result of removed_rate

  • active_cases.pre: prediction result of active_cases

  • confirmed.pre: prediction result of confirmed cases

• mileposts: dataframe contains 4 milepost T.1, T.2, Z.1, Z.2, which must be in the formate "%y-%m-%d" as a character.

Examples

Beginning_Time = "2020-01-29"
M <- 5
result <- prediction(COVID19_CN, M, Beginning_Time)

YuanchenZhu2020/DemoPreTurningPointsCOVID19 documentation built on Aug. 17, 2020, 12:24 a.m.