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Implications of a single case of paralysis for cryptic poliovirus circulation in Rockland County, New York

Requirements

The tidyverse suite including dplyr, tidyr and ggplot2. patchwork, lubridate, LaplacesDemon and ExtraDistr are also used.

General overview

All models fits and simulations are run using the simulation_cluster_twoimmune.R script. For the full suite of results, we run this script multiple times on the Harvard cluster (https://www.rc.fas.harvard.edu/) to generate many thousands of simulated trajectories. Cluster job submission is managed by cluster_submit.sh.

Briefly, the script runs the following actions:

  1. Read in a table of assumed model parameter priors from pars/priors.csv.
  2. For the high immunity and low immunity scenario analyses, run nsims simulations with a single call to random_simulation_twoimmune, each time drawing a new set of parameters from the prior (using the simulate_priors function).
  3. Extract the simulation results and filter down to only those draws which were consistent with the observed data.
  4. Using the successful draws, continue the simulation runs to generate complete trajectories. This part uses the final conditions from the first call to random_simulation_twoimmune, and calls restart_simulations_table_twoimmune to restart all the trajectories where random_simulation_twoimmune left off.
  5. Simulate runs from the same posterior distribution using population parameters for New York City. Again, this just uses random_simulation_twoimmune but with different input parameters.

The simulation can instead be run locally by changing the working directory on L6/7, and by setting the starting index i on L17 rather.

Detail of main simulation flow

The simulation is run with the following cascades:

  1. simulation_cluster_twoimmune.R is the top level master script. This calls...
  2. random_simulation_twoimmune, which manages the multiple prior draws and simulation runs and returns a large list of all the simulated trajectories and parameter values. This functions calls two other functions: a. simulate_priors, which simply returns a large table of parameter values with n draws from the priors. It takes many arguments, but these all just correspond to a parameter in the model as described in the methods. b. run_simulation_twoimmune, the main simulation code. This is called one time for each draw from the prior, and takes single value for each model parameter. It returns data frames for incidence and the final state of the simulation.
  3. restart_simulations_table_twoimmune takes in the final conditions (which are just a bunch of data frames) from random_simulation_twoimmune and restarts the each trajectory where they left off. In the restart, there is no need to stay consistent with any observed data, and the simulation is simply run for a set duration.

Top level simulation functions

Initial run

random_simulation_twoimmune manages the top level of the simulation.

KEY ARGUMENTS: It takes in all of the arguments corresponding to the prior distributions, the vector of observed paralysis incidence, the population size and simulation duration, and the number of simulations to be run. Each parameter with a prior must have its two parameters (mean/var or shape/scale or rate) specified. There are some optional flags to fix many of the model parameters rather than generating prior draws, but these can be ignored.

KEY OUTPUTS: a list with:

  1. simulation_results is a merged data frame giving the incidence trajectories for all of the simulated trajectories.
  2. final_conditions is a merged data frame with the final conditions (time step, population compartment sizes etc) of each simulation. These are used to restart the simulations later.
  3. par_table is a data frame with the prior draws.
  4. infectious_periods is a merged data frame giving the generation interval distribution of the fully susceptible group for each simulation
  5. infectious_ps_periods as in 4) but for the partially susceptible group
  6. incubation_periods merged data frame giving the incubation period distribution for each simulation
  7. tmax_vector vector, where each entry gives the number of time steps each simulation was run for
  8. n_paralysis data frame giving the total number of paralysis cases simulated before termination for each simulation
  9. data_are_consistent vector with an entry for each simulation. This gives the vector of flags showing if each trajectory remained consistent with the observed data or not.

Restart run

restart_simulations_table_twoimmune is very similar to random_simulation_twoimmune, but rather than generating new prior draws, it simply uses the model parameters and final conditions of a previous call to random_simulation_twoimmune and continues running the trajectories for tmax time steps, regardless of their consistency with any observed data.

KEY ARGUMENTS:

  1. use_sims is a vector of the simulation IDs for which we'd like to extend the trajectories. These should all have corresponding entries in pars and final_conditions.
  2. pars is a large data frame giving the model parameters used to generate the trajectories.
  3. final_conditions a large data frame with all of the final conditions of the previous simulation runs.
  4. t_starts is a vector with one entry for each simulation. This gives the time step at which the simulation should be restarted ie. the final time step from the previous call to random_simulation_twoimmune.
  5. vaccinate_proportion is an optional matrix with one row for each vaccination strategy to be trialled. By default, this is a matrix with a single row dictating no vaccination.
  6. tmax how long to run the extended simulations for in time steps.
  7. nruns optional, but allows each extended simulation to be run multiple times (ie. with different random course)
  8. P is the population size and should correspond to whatever was used in the initial call to random_simulation_twoimmune.

KEY OUTPUTS: one combined data farme giving the daily incidence of paralysis/infections for each immune class and Rt, with a simulation index.

Main body of simulation code

run_simulation_twoimmune is the main simulation function. It does not call any other user-defined functions.

KEY ARGUMENTS: It takes arguments for each of the key model parameters, including R0, the generation interval distribution parameters, the infection to paralysis fraction etc. A key argument is observed_data, which is simply a vector of consecutive days giving the number of observed paralysis cases. For a normal run, the continue_run and restart_simulation flags should be set to FALSE. These are accompanied by NULL for final_conditions, t_start and vaccinate_proportion. These are only used for special cases described later.

KEY OUTPUTS: a list with:

  1. inc_dat A data frame of infection incidence and paralysis incidence of each of the susceptible groups, as well as Rt.
  2. final_conditions A data frame giving the entire state of the simulation (population sizes in each immune state, incidence curves etc) at the end.
  3. t_end the final time point of the simulation
  4. n_paralysis the total number of paralysis cases simulated
  5. data_are_consistent flag showing whether the simulation ended entirely consistently with observed_data or not.

The main body of the simulation starts at L178. This is a while loop that steps the simulation forward in daily increments. The while loop ensures that the simulation terminates after tmax time steps. Otherwise, there are two conditions for the simulation to continue running:

  1. If continue_run==FALSE, the simulation starts a timer after the first case of paralysis is simulated. The simulation then continues for as many days as there are observations in observed_data, as long as the sequence of of observed data and the sequence of simulated paralysis cases continue to be consistent.
  2. If continue_run==TRUE, then the simulation does NOT need to be consistent with the observed data, and the simulation will simply run for tmax time steps.

New infections and cases of paralysis are handled from L196 to L285. These lines are simply draws from various distributions and then bookkeeping around population sizes.

The block at L294 acts as a sort of timer -- once the first case of paralysis has had onset, each day the simulated trajectory is checked for consistency with the observed data on L298. This checks that the vector of observed data and the incidence of paralysis to date are ENTIRELY consistent.

L307 onwards simply compiles the simulation state, incidence curves etc into data frames and values to be returned.