In wimmyteam/conisi: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE, 
  message = FALSE,  
  echo = FALSE
)

# Allow duplicate chunk labels
options(knitr.duplicate.label = "allow")

library(tidyverse)

# ===================
# Relative file paths
# ===================
#data_file <- system.file("extdata", "Example_Model_Output.RData", package = "conisi")

data_file <- "Example_Model_Output.RData"

# =============
# Load datasets
# =============
load(data_file)
df <- conisi::mutate_model_output(modelOutput,
                          pop = 168559618,
                          report_lag = 2,
                          start = lubridate::ymd("2020-03-12"))

Metadata

Dataset name: CONISI Model Output
Description of dataset: The dataset you are using will be specific to a region - either South Africa, Peru, Brazil, Chile, Zimbabwe, Limpopo Province, North West Province, Northern Cape Province, or Eastern Cape Province. Moreover, each value will be unique to the point in time for which the model was run. All datasets for each region and model run will have the same number and names of columns. This dataset currently contains r ncol(df) variables. Each row in the dataset represents a unique model run and time point in the model run. For example, this dataset of output contains r nrow(df) total rows of output, that belong to r round(nrow(df) / max(df$time)) model runs. Each of those model runs has a unique set of parameters that were used as input. Each model run has a row for r max(df$time) time steps.

Description of Variables

There are five different types of variables: identifiers, parameters, compartments, compartment combinations, and flows.

Identifiers

These variables in combination uniquely identify each row in the dataset.

experiment

This variable contains an integer that identifies the model run that the row belonged to.

time

This variable contains an integer that represents the number of days that have passed within the model world.

date

This is the conversion of the time variable into a date in the real world based upon when the local epidemic started and assumed reporting delays. The variable is formatted as YYYY-MM-DD. In this dataset the earliest date simulated is r format(min(df$date), "%A, %d %B %Y") and the most recent date availble is r format(max(df$date), "%A, %d %B %Y").

Parameters

The parameter variables were used as inputs into the model. They govern the rates at which people in the population move from one compartment (i.e. disease/health state) to another. Each model run (i.e. experiment) uses a unique set of parameters. The parameters are mostly the same across all time steps for that particular model run, but a few of the parameters are time-varying. Parameter variables are prefixed with "par_".

par_a_1d

This is the infectivity of diagnosed pre-symptomatic individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_1d"

par_a_1u

This is the infectivity of undiagnosed pre-symptomatic individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_1u"

par_a_2d

This is the infectivity of diagnosed asymptomatic individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_2d"

par_a_2u

This is the infectivity of undiagnosed asymptomatic individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_2u"

par_a_md

This is the infectivity of diagnosed mildly infected individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_md"

par_a_sd

This is the infectivity of diagnosed severely infected individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_sd"

par_a_su

This is the infectivity of undiagnosed severely infected individuals relative to undiagnosed mildly infected individuals.

var <- "par_a_su"

par_b_b

This is the effective contact rate, which encompasses all of the biological and behavioral considerations that influence contacts between individuals that lead to transmission.

var <- "par_b_b"

par_c_12u

This is the rate at which undiagnosed pre-symptomatic individuals become undiagnosed and asymptomatic, never to develop symptoms.

var <- "par_c_12u"

par_c_1mu

This is the rate at which undiagnosed pre-symptomatic individuals become undiagnosed with mild infection symptoms.

var <- "par_c_1mu"

par_c_1su

This is the rate at which undiagnosed pre-symptomatic individuals become undiagnosed with severe infection symptoms.

var <- "par_c_1su"

par_c_ceil

This is the maximum number of people who can be in critical care at a single time step.

var <- "par_c_ceil"

par_c_e1u

This is the rate at which exposed, but not infectious, individuals become undiagnosed pre-symptomatic infectious individuals.

var <- "par_c_e1u"

par_d_1

This is the rate at which pre-symptomatic individuals get diagnosed.

var <- "par_d_1"

par_d_2

This is the rate at which truly asymptomatic individuals get diagnosed.

var <- "par_d_2"

par_d_m

This is the rate at which mildly infected individuals get diagnosed.

var <- "par_d_m"

par_d_s

This is the rate at which severely infected individuals get diagnosed.

var <- "par_d_s"

par_ddf

This is the fraction of deaths that happened outside of the hospital where COVID-19 is identified as the cause of death.

var <- "par_ddf"

par_delta_c

This is the rate at which people in critical care die.

var <- "par_delta_c"

par_delta_h

This is the rate at which people in the hospital die before going to critical care.

var <- "par_delta_h"

par_delta_h_adjust

This is the factor by which you multiply the delta_h parameter if you want to decrease the rate at a specific point in time.

var <- "par_delta_h_adjust"

par_delta_su

This is the rate at which undiagnosed, severely infected people die.

var <- "par_delta_su"

par_eta_u

This is the rate at which undiagnosed severely infected people are hospitalised.

var <- "par_eta_u"

par_FOIadjust

This is the factor by which you multiply the force of infection if you want to decrease the rate at a specific point in time.

var <- "par_FOIadjust"

par_h_ceil

The maximum number of people who can be in the hospital at a given time.

var <- "par_h_ceil"

par_hdf

The fraction of all people who go to the hospital with COVID-19 that are actually diagnosed with COVID-19.

var <- "par_hdf"

par_r_2u

This is the rate at which undiagnosed truly asymptomatic infected individuals recover.

var <- "par_r_2u"

par_r_h

This is the rate at which hospitalised individuals recover, without going to critical care.

var <- "par_r_h"

par_r_mu

This is the rate at which undiagnosed mildly infected individuals recover.

var <- "par_r_mu"

par_r_p

This is the rate at which individuals in the post-critical care ward recover.

var <- "par_r_p"

par_rho

This is the rate that those in the critical care ward move to the post-critical care ward.

var <- "par_rho"

par_theta

This is the rate that those who are hospitalised move to the critical care ward.

var <- "par_theta"

par_r_2d

This is the rate that those who are diagnosed and asymptomatic recover.

var <- "par_r_2d"

par_r_md

This is the rate that those who are diagnosed and mildly infected recover.

var <- "par_r_md"

par_c_12d

This is the rate that those who are diagnosed and pre-symptomatic become a truly asymptomatic infected individual.

var <- "par_c_12d"

par_c_1md

This is the rate that those who are diagnosed and pre-symptomatic become a mildly infected individual.

var <- "par_c_1md"

par_c_1sd

This is the rate that those who are diagnosed and pre-symptomatic become a severely infected individual.

var <- "par_c_1sd"

par_eta_d

This is the rate that those who are diagnosed and severely infected become hosptialised.

var <- "par_eta_d"

par_delta_sd

This is the rate that those who are diagnosed and severely infected die without ever going to the hospital.

var <- "par_delta_sd"

par_d_e

This is the rate that undiagnosed exposed, but not infectious individuals become diagnosed.

var <- "par_d_e"

Compartments

These variables refer to the number of people who are in a specific disease state at a given point in time.

S

This is the total number of people who are susceptible to infection at a specific time step.

var <- "S"

E_d

This is the total number of people who are diagnosed with COVID-19, but not infectious yet at a specific time step.

var <- "E_d"

E_u

This is the total number of people who are undiagnosed and infected with COVID-19, but not infectious yet at a specific time step.

var <- "E_u"

I_1d

This is the total number of people who are diagnosed and pre-symptomatic at a specific time step.

var <- "I_1d"

I_1u

This is the total number of people who are undiagnosed and pre-symptomatic at a specific time step.

var <- "I_1u"

I_2d

This is the total number of people who are diagnosed and asymptomatic at a specific time step.

var <- "I_2d"

I_2u

This is the total number of people who are undiagnosed and asymptomatic at a specific time step.

var <- "I_2u"

I_md

This is the total number of people who are diagnosed and mildly infected at a specific time step.

var <- "I_md"

I_mu

This is the total number of people who are undiagnosed and mildly infected at a specific time step.

var <- "I_mu"

I_sd

var <- "I_sd"

This is the total number of people who are diagnosed and severely infected at a specific time step. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

I_su

This is the total number of people who are undiagnosed and severely infected at a specific time step.

var <- "I_su"

R_2d

This is the total number of diagnosed asymptomatic people who had recovered by a specific time step.

var <- "R_2d"

R_2u

This is the total number of undiagnosed asymptomatic people who had recovered by a specific time step.

var <- "R_2u"

R_md

This is the total number of diagnosed mildly infected people who had recovered by a specific time step.

var <- "R_md"

R_mu

This is the total number of undiagnosed mildly infected people who had recovered by a specific time step.

var <- "R_mu"

H

This is the total number of people who are hospitalised, but not in critical care, at a specific time step.

var <- "H"

R_h

This is the total number of hospitalised people who had recovered by a specific time step.

var <- "R_h"

C

var <- "C"

This is the total number of people in critical care at a specific time step. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

P

This is the total number of people who are post-critical care at a specific time step.

var <- "P"

R_c

This is the total number of people who were in critical care who had recovered by a specific time step.

var <- "R_c"

D_s

This is the total number of severely infected, but not hospitalised individuals that had died by a specific time step.

var <- "D_s"

D_h

This is the total number of hospitalised individuals that had died by a specific time step.

var <- "D_h"

D_c

This is the total number of individuals who were in critical care that had died by a specific time step.

var <- "D_c"

Flows

These are variables produced by the model that represent the cumulative number of people who were in a given disease state by a specific point in time.

ConfirmedCases

var <- "ConfirmedCases"

This is the cumulative number of cases that have been diagnosed by a specific time. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

ContribAll

This is the cumulative number of all people - diagnosed and undiagnosed - who have ever been infected by a specific time.

var <- "ContribAll"

ContribNonSympt

This is the cumulative cases caused by asymptomatic infections at a specific time.

var <- "ContribNonSympt"

eta_d_cumul_flow

This is the cumulative number of hospitalizations, among diagnosed cases, by a specific time.

var <- "eta_d_cumul_flow"

eta_u_cumul_flow

This is the cumulative number of hospitalizations, among undiagnosed infections, by a specific time.

var <- "eta_u_cumul_flow"

r_h_cumul_flow

This is the cumulative number of recoveries, among those in hospital (not critical care), by a specific time.

var <- "r_h_cumul_flow"

theta_cumul_flow

This is the cumulative of hospitalised cases that have gone to critical care, by a specific time.

var <- "theta_cumul_flow"

Asymp_diagnozed_cumul_flow

This is the cumulative number of diagnoses among asymptomatic cases, by a specific time.

var <- "Asymp_diagnozed_cumul_flow"

Symp_diagnozed_cumul_flow

This is the cumulative number of diagnoses among symptomatic cases, by a specific time.

var <- "Symp_diagnozed_cumul_flow"

Asymp_inf_cumul_flow

This is the cumulative number of pre-symptomatic infections that became asymptomatic by a specific time.

var <- "Asymp_inf_cumul_flow"

Symp_inf_cumul_flow

This is the cumulative number of pre-symptomatic infections that became symptomatic by a specific time.

var <- "Symp_inf_cumul_flow"

Combinations

These variables that were created from different combinations of the compartmental, flow, and parameter variables.

R0

This is the basic reproduction number. It is the average number of secondary infections produced by one case in a population where everyone is still susceptible.

var <- "R0"

Reff

This is the effective reproductive number. It is the average number of secondary infections produced by one case in a population where some people are susceptible and others are not susceptible because they have been vaccinated or infected.

var <- "Reff"

AllInfections

This is the total number of people who are exposed or infected, whether diagnosed or undiagnosed, at single point in time.

var <- "AllInfections"

ActiveInfections

var <- "ActiveInfections"

This is the total number of people at a single point in time who are infected, whether diagnosed or undiagnosed, who we assume are actively transmitting infection. It excludes those who are exposed, but not yet infectious, as well as those who are hospitalised or in critical care. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

SymptKnownAsymptInfections

This is the total number of people at a single point in time who symptomatic infections, whether diagnosed or undiagnosed, as well as diagnosed asymptomatic infections. It excludes those who are hospitalised or in critical care.

var <- "SymptKnownAsymptInfections"

SymptKnownInfections

var <- "SymptKnownInfections"

This is the total number of people at a single point in time who have a diagnosed symptomatic infection (mild or severe). It excludes those who are hospitalised or in critical care. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

SymptUnknownInfections

This is the total number of people at a single point in time who have a undiagnosed symptomatic infection (mild or severe). It excludes those who are hospitalised or in critical care.

var <- "SymptUnknownInfections"

AsymptKnownInfections

This is the total number of people at a single point in time who have a diagnosed asymptomatic infection. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

var <- "AsymptKnownInfections"

AsymptUnknownInfections

This is the total number of people at a single point in time who have an undiagnosed asymptomatic infection.

var <- "AsymptUnknownInfections"

SymptInfections

var <- "SymptInfections"

This is the total number of people at a single point in time who have a symptomatic infection (diagnosed and undiagnosed), but not hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

AsymptInfections

var <- "AsymptInfections"

This is the total number of people at a single point in time who have an asymptomatic infection (diagnosed and undiagnosed). In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

KnownInfections

var <- "KnownInfections"

This is the total number of people at a single point in time who have a diagnosed infection (not including hospitalised patients). In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

UnknownInfections

This is the total number of people at a single point in time who have an undiagnosed infection.

var <- "UnknownInfections"

SevereKnownMildInfections

This is the total number of people at a single point in time who have a severe infection (diagnosed and undiagnosed), or a diagnosed mild infection (not including hospitalised patients).

var <- "SevereKnownMildInfections"

SevereInfections

var <- "SevereInfections"

This is the total number of people at a single point in time who have a severe infection (diagnosed and undiagnosed), but not including hospitalised patients. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hospitalizations

var <- "Hospitalizations"

This is the total number of people at a single point in time who are hospitalised. It includes those in normal, critical care, and post-critical care units. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_I_sd

var <- "Hosp_I_sd"

This is the total number of people at a single point in time who have a severe diagnosed infection or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_SevereInfections

var <- "Hosp_SevereInfections"

This is the total number of people at a single point in time who have a severe infection (diagnosed or undiagnosed) or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_SevereKnownMildInfections

var <- "Hosp_SevereKnownMildInfections"

This is the total number of people at a single point in time who have a diagnosed mild infection, severe infection (diagnosed or undiagnosed) or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_SymptInfections

var <- "Hosp_SymptInfections"

This is the total number of people at a single point in time who have a symptomatic infection (diagnosed or undiagnosed) or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_SymptKnownAsymptInfections

var <- "Hosp_SymptKnownAsymptInfections"

This is the total number of people at a single point in time who have a diagnosed asymptomatic infection, symptomatic infection (diagnosed or undiagnosed), or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_ActiveInfections

var <- "Hosp_ActiveInfections"

This is the total number of people at a single point in time who have an active infection or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Hosp_SymptKnownInfections

var <- "Hosp_SymptKnownInfections"

This is the total number of people at a single point in time who a diagnosed symptomatic infection (mild or severe) or are hospitalised. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

hosp_nonicu

var <- "hosp_nonicu"

This is the total number of cases at a single point in time who are in any non-critical care ward. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

deaths_hosp

var <- "deaths_hosp"

This is the total number of deaths among people who were hospitalised in a normal ward or critical care unit. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

NotWorking

var <- "NotWorking"

This is the total number of people at a single point in time who have a diagnosed infection (pre-symptomatic, asymptomatic, mild or severe) or are hospitalised. Theoretically, these people would not be at work at this point in time. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

ReturnWork_cumul_flow

This is the total number of people who have recovered from an infection by a specific point in time.

var <- "ReturnWork_cumul_flow"

AllDeaths

var <- "AllDeaths"

This is the total number of people who have died by a specific point in time. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Prevalence

var <- "Prevalence"

This is the fraction of the population who has an active infection at a single point in time, not including hospitalised patients. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Exposure

var <- "Exposure"

This is the fraction of the population who has been exposed or infected by a specific point in time. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

FracSymptKnown

This is the fraction of active infections (not hospitalised) at a single point in time, which are symptomatic and diagnosed.

var <- "FracSymptKnown"

FracSymptUnknown

This is the fraction of active infections (not hospitalised) at a single point in time, which are symptomatic and undiagnosed.

var <- "FracSymptUnknown"

FracAsymptKnown

This is the fraction of active infections (not hospitalised) at a single point in time, which are asymptomatic and diagnosed.

var <- "FracAsymptKnown"

FracAsymptUnknown

This is the fraction of active infections (not hospitalised) at a single point in time, which are asymptomatic and undiagnosed.

var <- "FracAsymptUnknown"

FracHospSymptKnown

This is the fraction of hospitalised or symptomatic infections at a single point in time, which are diagnosed.

var <- "FracHospSymptKnown"

FracAsymptKnown2

This is the fraction of asymptomatic infections at a single point in time, which are diagnosed.

var <- "FracAsymptKnown2"

idf

This is the fraction of all infections at a single point in time, which are diagnosed.

var <- "idf"

ifr

This is the fraction of all people who have been infected, which have died by a point in time.

var <- "ifr"

cfr

This is the fraction of all diagnosed infections, which have died by a point in time.

var <- "cfr"

AllDailyInfections

This is the number of new infections that take place each day. It includes diagnosed, undiagnosed, asymptomatic, symptomatic.

var <- "AllDailyInfections"

NonSymptDailyInfections

This is the number of new infections caused by asymptomatic cases. It includes diagnosed and undiagnosed infections.

var <- "NonSymptDailyInfections"

RelContribNonSympt

This is the fraction of all new cases each day, which are caused by asymptomatic infections.

var <- "RelContribNonSympt"

NewCases

var <- "NewCases"

This is the number of new diagnosed infections each day. It includes asymptomatic and symptomatic infections. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

NewDeaths

var <- "NewDeaths"

This is the number of new deaths each day. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

eta_d_flow

var <- "eta_d_flow"

This is the number of new hospitalisations each day that occur from diagnosed cases. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

eta_u_flow

var <- "eta_u_flow"

This is the number of new hospitalisations each day that occur from undiagnosed infections. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

r_h_flow

var <- "r_h_flow"

This is the number of new recoveries each day that occur from hospitalised (not critical care) cases. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

delta_h_flow

var <- "delta_h_flow"

This is the number of new deaths each day that occur from hospitalised (not critical care) cases. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

theta_flow

var <- "theta_flow"

This is the number of new patients that enter critical care each day. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Symp_diagnozed_flow

var <- "Symp_diagnozed_flow"

This is the number of newly diagnosed symptomatic infections that occur each day. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

Asymp_diagnozed_flow

var <- "Asymp_diagnozed_flow"

This is the number of newly diagnosed asymptomatic infections that occur each day. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

ReturnWork_flow

var <- "ReturnWork_flow"

This is the number of employees (newly recovered cases) that return to work each day. In this dataset there are also r paste0(var, "_mean"), r paste0(var, "_min"), and r paste0(var, "_max") variables. These have the same definition, except they represent the mean, min and max values across all experiments at a given time point.

wimmyteam/conisi documentation built on Oct. 30, 2021, 4:11 p.m.