update description and namespace

man/progress.seiqhrf.icm.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sirPlus-functions.R
+\name{progress.seiqhrf.icm}
+\alias{progress.seiqhrf.icm}
+\title{Progress icm}
+\usage{
+progress.seiqhrf.icm(dat, at)
+}
+\arguments{
+\item{dat}{Object containing all data}
+
+\item{at}{?}
+}
+\value{
+progress
+}
+\description{
+Function to get progress of icms
+}

man/saveout.seiqhrf.icm.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sirPlus-functions.R
+\name{saveout.seiqhrf.icm}
+\alias{saveout.seiqhrf.icm}
+\title{Save icm}
+\usage{
+saveout.seiqhrf.icm(dat, s, out = NULL)
+}
+\arguments{
+\item{dat}{Object containing all data}
+
+\item{out}{?}
+
+\item{at}{?}
+}
+\value{
+out to save
+}
+\description{
+Function to save icm
+}

man/set.control.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi-functions.R
+\name{set.control}
+\alias{set.control}
+\title{Set control params}
+\usage{
+set.control(
+  type = "SEIQHRF",
+  nsteps = 366,
+  nsims = 8,
+  ncores = 4,
+  prog.rand = FALSE,
+  rec.rand = FALSE,
+  fat.rand = TRUE,
+  quar.rand = FALSE,
+  hosp.rand = FALSE,
+  disch.rand = TRUE
+)
+}
+\arguments{
+\item{type}{Type of model: SI, SIR, SIS, SEIR, SEIQHR and 
+SEIQHRF available, but only SEIQHRF is likely to work in the 
+current version of the code.}
+
+\item{s.num}{Initial number of *S compartment individuals in 
+the simulated population. An overall population of 10,000 is a good 
+compromise. A set of models will still take several minutes or more
+to run, in parallel.}
+
+\item{e.num}{Initial number of E compartment individuals in 
+the simulated population.}
+
+\item{i.num}{Initial number of I compartment individuals in 
+the simulated population.}
+
+\item{q.num}{Initial number of Q compartment individuals in 
+the simulated population.}
+
+\item{h.num}{Initial number of H compartment individuals in
+the simulated population.}
+
+\item{r.num}{Initial number of R compartment individuals in 
+the simulated population.}
+
+\item{f.num}{Initial number of F compartment individuals in 
+the simulated population.}
+}
+\value{
+control.icm object
+}
+\description{
+Sets the controls for stochastic individual contact models simulated with 
+icm.
+}

man/set.init.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi-functions.R
+\name{set.init}
+\alias{set.init}
+\title{Set init params}
+\usage{
+set.init(
+  s.num = 9997,
+  e.num = 0,
+  i.num = 3,
+  q.num = 0,
+  h.num = 0,
+  r.num = 0,
+  f.num = 0
+)
+}
+\arguments{
+\item{s.num}{Initial number of *S compartment individuals in 
+the simulated population. An overall population of 10,000 is a good 
+compromise. A set of models will still take several minutes or more
+to run, in parallel.}
+
+\item{e.num}{Initial number of E compartment individuals in 
+the simulated population.}
+
+\item{i.num}{Initial number of I compartment individuals in 
+the simulated population.}
+
+\item{q.num}{Initial number of Q compartment individuals in 
+the simulated population.}
+
+\item{h.num}{Initial number of H compartment individuals in
+the simulated population.}
+
+\item{r.num}{Initial number of R compartment individuals in 
+the simulated population.}
+
+\item{f.num}{Initial number of F compartment individuals in 
+the simulated population.}
+}
+\value{
+init.icm object
+}
+\description{
+Sets the initial conditions for stochastic individual contact models 
+simulated with icm.
+}

man/set.param.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi-functions.R
+\name{set.param}
+\alias{set.param}
+\title{Set epidemic params}
+\usage{
+set.param(
+  inf.prob.e = 0.02,
+  act.rate.e = 10,
+  inf.prob.i = 0.05,
+  act.rate.i = 10,
+  inf.prob.q = 0.02,
+  act.rate.q = 2.5,
+  quar.rate = 1/30,
+  hosp.rate = 1/100,
+  disch.rate = 1/15,
+  prog.rate = 1/10,
+  prog.dist.scale = 5,
+  prog.dist.shape = 1.5,
+  rec.rate = 1/20,
+  rec.dist.scale = 35,
+  rec.dist.shape = 1.5,
+  fat.rate.base = 1/50,
+  hosp.cap = 40,
+  fat.rate.overcap = 1/25,
+  fat.tcoeff = 0.5,
+  vital = TRUE,
+  a.rate = (10.5/365)/1000,
+  a.prop.e = 0.01,
+  a.prop.i = 0.001,
+  a.prop.q = 0.01,
+  ds.rate = (7/365)/1000,
+  de.rate = (7/365)/1000,
+  di.rate = (7/365)/1000,
+  dq.rate = (7/365)/1000,
+  dh.rate = (20/365)/1000,
+  dr.rate = (7/365)/1000,
+  out = "mean"
+)
+}
+\arguments{
+\item{inf.prob.e}{Probability of passing on infection at each 
+exposure event for interactions between infectious people in the E 
+compartment and susceptibles in S. Note the default is lower than for 
+inf.prob.i reflecting the reduced infectivity of infected but 
+asymptomatic people (the E compartment). Otherwise as for inf.exp.i.}
+
+\item{act.rate.e}{The number of exposure events (acts) between 
+infectious individuals in the E compartment and susceptible individuals 
+in the S compartment, per day. Otherwise as for act.rate.i.}
+
+\item{inf.prob.i}{Probability of passing on infection at each 
+exposure event for interactions between infectious people in the I
+compartment and susceptibles in S. Reducing inf.prob.i is equivalent to 
+increasing hygiene measures, such as not putting hands in eyes, nose or 
+moth, use of hand sanitisers, wearing masks by the infected, and so on.}
+
+\item{act.rate.i}{The number of exposure events (acts) between 
+infectious individuals in the I compartment and susceptible individuals 
+in the S compartment, per day. It's stochastic, so the rate is an
+average, some individuals may have more or less. Note that not every 
+exposure event results in infection - that is governed by the inf.prob.i
+parameters (see below). Reducing act.rate.i is equivalent to increasing 
+social distancing by people in the I compartment.}
+
+\item{inf.prob.q}{Probability of passing on infection at each 
+exposure event for interactions between infectious people in the Q 
+compartment and susceptibles in S. Note the default is lower than for 
+inf.prob.i reflecting the greater care that self-isolated individuals 
+will, on average, take regarding hygiene measures, such as wearing masks,
+to limit spread to others. Otherwise as for inf.exp.i.}
+
+\item{act.rate.q}{The number of exposure events (acts) between 
+infectious individuals in the Q compartment (isolated, self or otherwise)
+and susceptible individuals in the S compartment, per day. Note the much
+lower rate than for the I and E compartments, reflecting the much 
+greater degree of social isolation for someone in (self-)isolation. The
+exposure event rate is not zero for this group, just much less. 
+Otherwise as for act.rate.i.}
+
+\item{quar.rate}{Rate per day at which symptomatic (or tested 
+positive), infected I compartment people enter self-isolation (Q 
+compartment). Asymptomatic E compartment people can't enter 
+self-isolation because they don't yet know they are infected. Default is
+a low rate reflecting low community awareness or compliance with 
+self-isolation requirements or practices, but this can be tweaked when
+exploring scenarios.}
+
+\item{hosp.rate}{Rate per day at which symptomatic (or tested 
+positive), infected I compartment people or self-isolated Q compartment
+people enter the state of requiring hospital care -- that is, become 
+serious cases. A default rate of 1% per day with an average illness 
+duration of about 10 days means a bit less than 10% of cases will 
+require hospitalisation, which seems about right (but can be tweaked, 
+of course).}
+
+\item{disch.rate}{Rate per day at which people needing 
+hospitalisation recover.}
+
+\item{prog.rate}{Rate per day at which people who are infected 
+but asymptomatic (E compartment) progress to becoming symptomatic (or 
+test-positive), the I compartment. See prog.rand above for more details.}
+
+\item{prog.dist.scale}{Scale parameter for Weibull distribution 
+for progression, see prog.rand for details.}
+
+\item{prog.dist.shape}{Shape parameter for Weibull distribution 
+for progression, see prog.rand for details. Read up on the Weibull 
+distribution before changing the default.}
+
+\item{rec.rate}{Rate per day at which people who are infected and
+symptomatic (I compartment) recover, thus entering the R compartment. 
+See rec.rand above for more details.}
+
+\item{rec.dist.scale}{Scale parameter for Weibull distribution for
+recovery, see rec.rand for details.}
+
+\item{rec.dist.shape}{Shape parameter for Weibull distribution for
+recovery, see rec.rand for details. Read up on the Weibull distribution
+before changing the default.}
+
+\item{fat.rate.base}{Baseline mortality rate per day for people 
+needing hospitalisation (deaths due to the virus). See fat.rand for more
+details.}
+
+\item{hosp.cap}{Number of available hospital beds for the modelled
+population. See fat.rand for more details.}
+
+\item{fat.rate.overcap}{Mortality rate per day for people needing
+hospitalisation but who can't get into hospital due to the hospitals 
+being full (see hosp.cap and fat.rand). The default rate is twice that 
+for those who do get into hospital.}
+
+\item{fat.tcoeff}{Time co-efficient for increasing mortality rate 
+as time in the H compartment increases for each individual in it. See 
+fat.rand for details.}
+
+\item{vital}{Enables demographics, that is, arrivals and 
+departures, to and from the simulated population.}
+
+\item{a.rate}{Background demographic arrival rate. Currently all 
+arrivals go into the S compartment, the default is approximately the 
+daily birth rate for Australia. Will be extended to cover immigration in
+future versions.}
+
+\item{ds.rate}{Background demographic departure (death not due to
+virus) rates. Defaults based on Australian crude death rates. Can be 
+used to model emigration as well as deaths.}
+
+\item{de.rate}{Background demographic departure (death not due to 
+virus) rates. Defaults based on Australian crude death rates. Can be used
+to model emigration as well as deaths.}
+
+\item{dq.rate}{Background demographic departure (death not due to 
+virus) rates. Defaults based on Australian crude death rates. Can be used
+to model emigration as well as deaths.}
+
+\item{dh.rate}{Background demographic departure (death not due to 
+virus) rates. Defaults based on Australian crude death rates. Can be used
+to model emigration as well as deaths.}
+
+\item{dr.rate}{Background demographic departure (death not due to 
+virus) rates. Defaults based on Australian crude death rates. Can be used 
+to model emigration as well as deaths.}
+
+\item{out}{Summary function for the simulation runs. median is 
+also available, or percentiles, see the EpiModel documentation.}
+}
+\value{
+param.icm object
+}
+\description{
+Sets the epidemic parameters for stochastic individual contact models 
+simulated with icm.
+}

man/sirPlus.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sirPlus-package.R
+\docType{package}
+\name{sirPlus}
+\alias{sirPlus}
+\title{sirPlus}
+\description{
+\pkg{sirPlus} is a package for modeling COVID-19 spread, focusing on rates
+of hospitalization, which should be useful for hospitals.
+}

vignettes/epimodels-SEIQHRF.Rmd

+---
+title: "sirPLUS models"
+date: "Last updated: 23 March 2020"
+output:
+    BiocStyle::html_document:
+        toc: true
+        toc_float: true
+vignette: >
+  %\VignetteIndexEntry{sirPLUS models}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+## Backgrouund on the SEIQHRF (SIR + extra compartments) model
+
+Based on [Tim Churches"'" blog post](https://timchurches.github.io/blog/posts/2020-03-18-modelling-the-effects-of-public-health-interventions-on-covid-19-transmission-part-2/). 
+
+![COVID-19 transition diagram]('figure/TChurches-transition-diagram.png')
+*Note the lower case letters between compartment nodes represent model input 
+parameters defined in the blog post*
+
+
+## Sett up environment and pull parameters
+
+```{r setup environment, include=FALSE}
+library(tidyverse)
+library(dplyr)
+library(magrittr)
+library(lubridate)
+library(stringr)
+library(tibble)
+library(broom)
+library(ggplot2)
+#remotes::install_github("rstudio/gt")
+library(gt)
+library(knitr)
+library(devtools)
+library(DiagrammeR)
+library(parallel)
+library(foreach)
+library(tictoc)
+suppressMessages(library(EpiModel))
+library(incidence)
+.libPaths('/mnt/mcfiles/rlyu/Software/R/3.6/Rlib')
+#devtools::install_github("reconhub/earlyR")
+library(earlyR)
+
+tic("Time to complete")
+```
+
+```{r data from Tim Churches}
+source_files <- c("_icm.mod.init.seiqhrf.R", "_icm.mod.status.seiqhrf.R", 
+    "_icm.mod.vital.seiqhrf.R", "_icm.control.seiqhrf.R", "_icm.utils.seiqhrf.R", 
+    "_icm.saveout.seiqhrf.R", "_icm.icm.seiqhrf.R")
+
+src_path <- paste0("./_posts/2020-03-18-modelling-the-effects-of-public-health-", 
+    "interventions-on-covid-19-transmission-part-2/")
+
+gist_url <- "https://gist.github.com/timchurches/92073d0ea75cfbd387f91f7c6e624bd7"
+
+local_source <- FALSE
+
+for (source_file in source_files) {
+    if (local_source) {
+        source(paste(src_path, source_file, sep = ""))
+    } else {
+        Sys.sleep(1)
+        source_gist(gist_url, filename = source_file)
+    }
+}
+```
+
+## Define Functions
+
+```{r simulate baselines}  
+# function to set-up and run the baseline simulations
+devtools::load_all(".")
+control <- set.control()
+param <- set.param()
+init <- set.init(s.num = 10000, e.num = 150, q.num = 10, h.num = 0)
+init
+
+sim <- icm.seiqhrf(param, init, control)
+sim_df <- as.data.frame(sim, out=out)
+simulate <- list(sim=sim, df=sim_df)
+
+```
+
+
+## Generate and inspect baseline simulations
+started at 3:12 pm
+```{r baseline sims}
+baseline_sim <- simulate(ncores = 4)
+times <- get_times(baseline_sim)
+
+times %>% filter(duration <= 30) %>% ggplot(aes(x = duration)) + 
+    geom_bar() + facet_grid(period_type ~ ., scales = "free_y") + 
+    labs(title = "Duration frequency distributions", subtitle = "Baseline simulation")
+```
+
+
+
+```{r viz prevalence}
+baseline_plot_df <- baseline_sim$df %>% # use only the prevalence columns
+select(time, s.num, e.num, i.num, q.num, h.num, r.num, f.num) %>% 
+    # examine only the first 100 days since it is all over by
+# then using the default parameters
+filter(time <= 100) %>% pivot_longer(-c(time), names_to = "compartment", 
+    values_to = "count")
+
+# define a standard set of colours to represent compartments
+compcols <- c(s.num = "yellow", e.num = "orange", i.num = "red", 
+    q.num = "cyan", h.num = "magenta", r.num = "lightgreen", 
+    f.num = "black")
+complabels <- c(s.num = "Susceptible", e.num = "Infected/asymptomatic", 
+    i.num = "Infected/infectious", q.num = "Self-isolated", h.num = "Requires hospitalisation", 
+    r.num = "Recovered", f.num = "Case fatality")
+
+baseline_plot_df %>% ggplot(aes(x = time, y = count, colour = compartment)) + 
+    geom_line(size = 2, alpha = 0.7) + scale_colour_manual(values = compcols, 
+    labels = complabels) + theme_dark() + labs(title = "Baseline simulation", 
+    x = "Days since beginning of epidemic", y = "Prevalence (persons)")
+```
+## Experiment 1
+
+