findBestRe: Find Best Fit for Re

In lemdt/CovidShinyModel:

Description

This takes in historical hospitalization data (day.vec and num_actual.vec). Given a fixed set of parameters, it finds the Re that gives the least square error with the data.

Usage

findBestRe(model, N, start.exp, num.days, day.vec, num_actual.vec, params)

Arguments

model	String, model to use (either M0, M1, M2)
N	Numeric. Number of people in the area.
start.exp	Numeric. Starting number of exposures.
num.days	Numeric. Number of days to simulate.
day.vec	Vector of numerics.
num_actual.vec	Vector of numerics.
params	List of paramters for SEIR simulation.

Details

The main inputs are day.vec and num_actual vec. Day.vec consists of indices of the historical days relative to the 'set date.' For example, if the date is set as April 1, 2020, and the user provides data from March 28, 29, and 31, then the day.vec = c(-4, -3, -1)

Num_actual.vec is a vector of the values of historical hospitalizations from those dates.

The function returns a list with 'best.re' (The Re with the best fit) and 'best.vals' (a vector of numerics with projected values of hospitalizations on the historical dates for which data was provided).

Value

List with best Re and the projected number of hospitalizations on the historical dates for which data was provided.

lemdt/CovidShinyModel documentation built on May 10, 2020, 1:54 p.m.