version 1.0.0

DESCRIPTION

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+Package: REffectivePred
+Type: Package
+Title: Pandemic Prediction Model in an SIRS Framework
+Version: 1.0.0
+Authors@R: c(person(given = "Razvan", 
+                    family = "Romanescu", 
+                    role = c("aut", "cre"),
+                    email = "Razvan.Romanescu@umanitoba.ca"),
+             person(given = "Songdi", 
+                    family = "Hu", 
+                    role = "aut"), 
+             person(given = "Md Ashiqul", 
+                    family = "Haque", 
+                    role = "aut"),
+             person(given = "Olivier", 
+                    family = "Tremblay-Savard", 
+                    role = "aut"))
+Maintainer: Razvan Romanescu <Razvan.Romanescu@umanitoba.ca>
+Description: A suite of methods to fit and predict case count data using 
+    a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) 
+    model, based on an assumed specification of the effective reproduction 
+    number. The significance of this approach is that it relates epidemic 
+    progression to the average number of contacts of infected individuals, 
+    which decays as a function of the total susceptible fraction remaining 
+    in the population. The main functions are pred.curve(), which computes 
+    the epidemic curve for a set of parameters, and estimate.mle(), which 
+    finds the best fitting curve to observed data. The easiest way to pass 
+    arguments to the functions is via a config file, which contains input 
+    settings required for prediction, and the package offers two methods, 
+    navigate_to_config()  which points the user to the configuration file, 
+    and re_predict() for starting the fit-predict process.
+    Razvan G. Romanescu et al. (2023) <doi:10.1016/j.epidem.2023.100708>.
+Imports: yaml, config, zoo, grDevices, utils
+License: GPL (>= 2)
+Encoding: UTF-8
+RoxygenNote: 7.2.3
+Collate: 'prediction_model.v16.R' 'executions.v16.R'
+NeedsCompilation: no
+Packaged: 2024-01-31 18:14:33 UTC; gobs3
+Author: Razvan Romanescu [aut, cre],
+  Songdi Hu [aut],
+  Md Ashiqul Haque [aut],
+  Olivier Tremblay-Savard [aut]
+Repository: CRAN
+Date/Publication: 2024-02-02 18:30:02 UTC

MD5

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+5eeab009739343dbd99cb2d1e100af20 *DESCRIPTION
+0c79430589fc8375fa283c0b2a700212 *NAMESPACE
+e778b86e78e9398f7e47cb8162af93d8 *R/executions.v16.R
+2b4ed4b7ebb4197bc64f93029ce1cc9f *R/prediction_model.v16.R
+ace2b074ff852bd1b8d6aaf095fa1984 *inst/config.yml
+3280b6d9de50b546717c20bb93a3bd47 *inst/extdata/NY_OCT_4_2022.csv
+07601b5a14538a8542eabc6bcf863607 *man/c_helper.Rd
+2954881c32139660e1f2ba43651a1d00 *man/ci.curve.Rd
+710dd855d6bbb2ec96a06afe8fff9c5e *man/estimate.mle.Rd
+5ae616ca3469d67a1bc911563ee036a8 *man/find_ends.Rd
+8308d8a4941e57846739f16fce05642e *man/find_starts.Rd
+25527169ff8d23ea143dbd6ae7dc11da *man/load_config.Rd
+1d42025363b2d2fc72c8ccb86d9ddaed *man/log_lklh.Rd
+4277b6b452c4ee5e7bf151b118b4a78a *man/modif.helper.Rd
+b8d3f8315181bc4596ab6485637fd84b *man/navigate_to_config.Rd
+01b6259a012f0a4165489fb9e9a4680f *man/plot_outputs.Rd
+3477bcd035d8c1fc2ab8aa692e381812 *man/pred.curve.Rd
+07a4f0fb02de208a907cded1e3ad6e3d *man/ranges_to_waves.Rd
+02a4eb334b0d2fdca285d960a801fe34 *man/re_predict.Rd
+779993d869916312b0f76a7a2a197426 *man/rt_empirical.Rd
+7c4bc9bee71464512a36b71d1989bf4c *man/serial.helper.Rd
+c443136f1d32cfb1dae090f9585bb8f7 *man/waves_1d_list.Rd

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(c_helper)
+export(ci.curve)
+export(estimate.mle)
+export(find_ends)
+export(find_starts)
+export(load_config)
+export(log_lklh)
+export(navigate_to_config)
+export(plot_outputs)
+export(pred.curve)
+export(ranges_to_waves)
+export(re_predict)
+export(rt_empirical)
+export(waves_1d_list)
+import(config)
+import(yaml)
+importFrom(grDevices,colorRampPalette)
+importFrom(grDevices,dev.off)
+importFrom(graphics,abline)
+importFrom(graphics,axis)
+importFrom(graphics,lines)
+importFrom(graphics,points)
+importFrom(graphics,rect)
+importFrom(graphics,title)
+importFrom(stats,dlnorm)
+importFrom(stats,dnorm)
+importFrom(stats,optim)
+importFrom(utils,data)
+importFrom(utils,read.csv)
+importFrom(zoo,as.Date)

R/executions.v16.R

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+##################################### MAIN #####################################
+# includes underreporting estimation
+#' @import yaml
+#' @import config
+#' @importFrom zoo as.Date
+#' @importFrom grDevices dev.off
+#' @importFrom utils data read.csv
+#' @include prediction_model.v16.R
+NULL
+
+#' @title Navigate to the config file
+#' @description  Prints the path to the config file and opens the config file.
+#' @returns The path to the configuration file.
+#' @export
+navigate_to_config <- function() {
+  path_to_config <- system.file("config.yml", package = "REffectivePred")
+  system(path_to_config)
+  output <- path_to_config
+}
+
+#' @title Load configuration file
+#' @description Load the configuration file as an object in the global
+#'    environment. This can be passed to the main functions instead of the
+#'    individual arguments within, for user convenience.
+#' @returns A ’config’ object, which is a list that stores input parameters and
+#'    settings from config.yml. Variable names are imported exactly. See the
+#'    configuration file for details.
+#' @export
+load_config <- function(){
+  path_to_config <- system.file("config.yml", package = "REffectivePred")
+  cfg <- config::get(file = path_to_config)
+  output <- cfg
+}
+
+
+#' @title Demo of main functions
+#' @description Fits the model to an example case data and predicts the epidemic curves and plots the outputs.
+#' @details
+#'  Please modify the config file before invoking this method.
+#'  The config file contains all the settings and initial  parameter values
+#'  necessary for the algorithm to run. Path to the dataset (in csv format)
+#'  is also set in the config file. This file should be updated to the desired
+#'  specifications before running this demo. To open it, execute
+#'  REffectivePred::navigate_to_config().
+#' @usage NULL
+#' @param path_to_data Absolute path to the dataset in csv format.
+#' @returns No return value.
+#' @export
+re_predict <- function(path_to_data = NULL) {
+  path_to_config <- system.file("config.yml", package = "REffectivePred")
+  cfg <- config::get(file = path_to_config)
+
+  if (is.null(path_to_data)) {
+    path_to_data <- system.file("extdata/NY_OCT_4_2022.csv", package = "REffectivePred")
+  }
+
+  data <- read.csv(path_to_data)
+  cases <- diff(c(0, data$cases)) # Convert cumulative cases into daily cases
+  Time <- as.Date(data$date, tryFormats = c("%d-%m-%Y", "%d/%m/%Y"))
+  population <- cfg$population # Population size
+
+  lt <- length(cases) # Length of cases
+  window_size <- cfg$window.size # Window size for Instantaneous Rt
+  rho <- eval(parse(text = cfg$rho)) # Under-reporting fraction (ref Irons, Raftery, 2021)
+  serial_mean <- cfg$serial_mean
+  serial_var <- cfg$serial_var
+  adj.period <- cfg$adj.period # Assume that psi shifts linearly to the new value over this period, to account for delays in society adjusting, and reporting delays
+
+  fit.t.pred <- cfg$fit.t.pred #<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Time of prediction
+  not.predict <- cfg$not.predict
+  rt.func.num <- cfg$rt.func.num
+  num.iter <- cfg$num.iter
+  silence.errors <- cfg$silence.errors
+  predict.beyond <- cfg$predict.beyond
+
+  curve_params <- as.double(unlist(cfg$curve_params))
+
+  vt_params <- as.double(unlist(cfg$vt_params)) # The vt initial values, starting at wave 2
+
+  restriction_levels <- as.double(unlist(cfg$restriction_levels)) #   Psi
+
+  betas <- as.double(unlist(cfg$betas)) #   betas
+
+  ini_params <- c(curve_params, vt_params, restriction_levels, betas)
+
+  restrictions_params <- cfg$restrictions_params
+  restriction_st_params <- cfg$restriction_st_params
+  param_scale <- abs(ini_params) / 10
+  waves_list <- ranges_to_waves(cfg$waves_list)
+  params_limits <- cfg$params_limits
+  num_waves <- cfg$num_waves
+
+  waves <- waves_1d_list(num_waves, waves_list)
+
+  rho <- eval(parse(text = cfg$rho))
+  infections <- cases / rho # Estimated time series of total infections
+
+  # ==============================================================================
+
+    est <- estimate.mle(
+      ini_params = ini_params,
+      params_limits = params_limits,
+      restrictions = restrictions_params,
+      restriction.starts = restriction_st_params,
+      ranges = waves,
+      rt_func = rt.func.num,
+      silence.errors = silence.errors,
+      fit.t.pred = fit.t.pred,
+      param_scale = param_scale,
+      num.iter = num.iter,
+      cases = cases,
+      scenario = NULL,
+      H.E = NULL,
+      H.W = NULL,
+      adj.period = adj.period,
+      population = population,
+      rho = rho,
+      serial_mean = serial_mean,
+      serial_var = serial_var,
+      lt = lt,
+      window_size = window_size,
+      hessian = T
+    )
+
+    print(est)
+
+    a1 <- est$a1
+    a2 <- est$a2
+    a3 <- est$a3
+    a4 <- est$a4
+    nu <- est$nu
+    vt <- c(1, est$vt_params_est)
+    psi <- est$Psi
+    betas <- est$betas
+
+
+    # Result & Plot: Predictions are based on the function below
+
+    r1 <- pred.curve(
+      a1 = a1,
+      a2 = a2,
+      a3 = a3,
+      a4 = a4,
+      nu = nu,
+      Psi = psi,
+      betas = betas,
+      use.actual.not.predicted = not.predict,
+      restrictions = restrictions_params,
+      restriction.starts = restriction_st_params,
+      ranges = waves,
+      variant.transm = vt,
+      rt_func = rt.func.num,
+      fit.t.pred = fit.t.pred,
+      cases = cases,
+      scenario = NULL,
+      H.E = NULL,
+      H.W = NULL,
+      adj.period = adj.period,
+      population = population,
+      rho = rho,
+      serial_mean = serial_mean,
+      serial_var = serial_var,
+      lt = lt,
+      window_size = window_size
+    )
+
+    print(r1)
+    Rt.pr <- r1$`Predicted Rt` # Predicted Rt
+    yt.pr1 <- abs(r1$`Predicted Infections`)
+
+    # Rt
+    range.rt <- 1:length(lt)
+
+    ### Plot for each Rt (1:4)
+    range.points <- 1:(max(waves_list[[4]]) + 21) # Decide the range of points to plot
+
+    # Rt
+    plot_outputs(Time = Time,
+                 cases = cases,
+                 window_size = window_size,
+                 serial_mean = serial_mean,
+                 serial_var = serial_var,
+                 predict.beyond = predict.beyond,
+                 waves_list = waves_list,
+                 num_waves = num_waves,
+                 rt_func = rt.func.num,
+                 curve = r1,
+                 restrictions = restrictions_params,
+                 restriction.starts = restriction_st_params
+    )
+
+    # Confidence bounds
+    bounds <- ci.curve(fit = est,
+                       restrictions = restrictions_params,
+                       restriction.starts = restriction_st_params,
+                       ranges = waves,
+                       rt_func = rt.func.num,
+                       fit.t.pred = fit.t.pred,
+                       cases = cases,
+                       scenario = NULL,
+                       H.E = NULL,
+                       H.W = NULL,
+                       adj.period = adj.period,
+                       population = population,
+                       rho = rho,
+                       serial_mean = serial_mean,
+                       serial_var = serial_var,
+                       lt = lt,
+                       window_size = window_size)
+    print(bounds)
+}