pred.curve: Epidemic Curve Model

Description

Computes the epidemic curve and associated quantities for a given parameter set.

Usage

pred.curve(
  a1 = 0,
  a2 = 0,
  a3 = 0,
  a4 = 0,
  nu = 0,
  variant.transm = NULL,
  Psi = NULL,
  betas = NULL,
  cases = NULL,
  cfg = NULL,
  use.actual.not.predicted = FALSE,
  restrictions = NULL,
  restriction.starts = NULL,
  ranges = NULL,
  rt_func = 1,
  fit.t.pred = NULL,
  predict.beyond = 0,
  scenario = NULL,
  H.E = NULL,
  H.W = NULL,
  adj.period = NULL,
  population = NULL,
  rho = NULL,
  serial_mean = NULL,
  serial_var = NULL,
  lt = NULL,
  window_size = NULL,
  verbose = FALSE
)

Value

Returns list:

Predicted Infections - Vector of estimated infections, computed as predicted cases divided by rho.
Predicted Cases - Vector of predicted cases.
Predicted $R_t$ - Vector of predicted susceptible fractions
Predicted $R_t$ - Vector of (model) predicted $R_t$.
Predicted Lambda t - Vector of predicted Lambda_t, which is the numerator used in computing the empirical $R_t$.
Psi.vec - Vector of psi values, which pastes together parameters psi over the period they apply, or 1 when there are no restrictions.
Contact rate params - Vector of the curve parameters (a1, a2, a3, a4).

Arguments

a1, a2, a3, a4: Parameters of the contact rate curve specified by rt_func.
nu: Loss of immunity rate beyond the first wave.
variant.transm: Vector of transmissibility of variants in each wave, as relative multiplication factors compared to transmissibility in wave 1. Should always be 1 for the first wave.
Psi: Vector of restriction parameters for severity levels 1 - 4.
betas: Vector containing (beta0,beta.R,beta.E,beta.W), when restrictions = NULL. Not currently implemented.
cases: vector of case counts.
cfg: The object that contains all variables from the configuration file. a1, a2, a3, a4, nu, variant.transm, Psi, betas, and cases are also required for the method to execute. All other parameters will not be used if cfg is passed to the method.
use.actual.not.predicted: Logical; if FALSE (default), the susceptible fraction is updated using predicted cases. Otherwise updated using actual cases.
restrictions: A numeric integer vector giving the severity of restrictions. Zero means no restriction, and higher numbers means greater severity/disruption. The ordered unique values should be consecutive integers starting from zero. Each number (other than 0) adds a new parameter to the fit.
restriction.starts: A vector of same length as restrictions, of times when restrictions came into effect. Note: the first index time should be 1.
ranges: A vector of time ranges for the different waves. The wave ranges should be contiguous, with at least one unit of time between consecutive waves.
rt_func: The parametric form of function c(). Options are listed under function c_helper.
fit.t.pred: Time from which prediction is done. If use.actual.not.predicted is TRUE, values of $S_t$ before this time will be computed using actual counts.
predict.beyond: Number of days to predict beyond the end of cases. See Details for usage notes.
scenario: A character string describing options to deal with restrictions. Currently unsupported.
H.E: Mobility metrics for category Retail & Entertainment. Currently unsupported.
H.W: Mobility metrics for category Workplaces. Currently unsupported.
adj.period: Adjustment period following a change in severity level. Restriction level (psi) is linearly interpolated from the old to the new value over this period.
population: Total population size.
rho: A vector of under-reporting rates of the same length as cases. If a scalar is supplied, the vector will be constant with this value.
serial_mean: Mean of the serial interval on the log scale.
serial_var: Variance of the serial interval on the log scale.
lt: The length of cases.
window_size: The maximum value for the serial interval.
verbose: Logical. If TRUE, provides additional details while running the function.

Details

At each time step, $R_{t+1}$ is computed using the contact rate function $c(S_t)$ implemented via c_helper. Then the number of cases is computed using formula: $$y_{t+1}=R_{t+1} \sum_{s=1}^M w_s y_{t+1-s}$$ Finally, the fraction $S_{t+1}$ is updated. This creates a curve over the entire range of ranges. See Romanescu R, Hu S, Nanton D, Torabi M, Tremblay-Savard O, Haque MA. The effective reproductive number: modeling and prediction with application to the multi-wave Covid-19 pandemic. Epidemics. 2023 Jul 20:100708 for more details.

For predicting an ongoing wave beyond the end of cases, the end of ranges (or waves_list, if using cfg) should be specified to match the predict.beyond argument. As well, argument use.actual.not.predicted should be set to FALSE when predicting beyond the end of cases.

Examples

Run this code

library(REffectivePred)
## Read in the data
path_to_data <- system.file("extdata/NY_OCT_4_2022.csv", package = "REffectivePred")
data <- read.csv(path_to_data)
head(data)
cases <- diff(c(0, data$cases)) # Convert cumulative cases into daily cases
lt <- length(cases)             # Length of cases
Time <- as.Date(data$date, tryFormats = c("%d-%m-%Y", "%d/%m/%Y"))

navigate_to_config() # Open the config file, make any necessary changes here.
path_to_config <- system.file("config.yml", package = "REffectivePred")  # Read config file
cfg <- load_config()    # Build the cfg object

# Example 1. Using fits from Romanescu et al. (2023)

r1 <- pred.curve(
a1 = 0.58,
a2 = 1.12,
nu = 0.56,
variant.transm = c(1,1.22,0.36,0.56),
Psi = c(0.58,0.52,0.49),
cases = cases,
cfg = cfg
)

plot(cases, xlab="Day", ylab="Predicted cases")
lines(r1$'Predicted Cases', col='red')

# Example 2. Best fit curve
est <- estimate.mle(
    cases = cases,
    cfg = cfg
    )
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

r1 <- pred.curve(
a1 = a1,
a2 = a2,
a3 = a3,
a4 = a4,
nu = nu,
variant.transm = vt,
Psi = psi,
betas = betas,
cases = cases,
cfg = cfg
)
plot(r1$'Predicted Infections', xlab="Day", ylab="Predicted infections")

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