leapfrog

Leapfrog is a multistate population projection model for demographic and HIV epidemic estimation.

The name leapfrog is in honor of Professor Basia Zaba.

Note: the CCMPP model and implementation of Bayesian Population Reconstruction in TMB previously here have moved to a separate repository: https://www.github.com/mrc-ide/ccmpp.tmb

Simulation model

The simulation model is implemented in a header-only C++ library located in inst/include/leapfrog-raw.h. This location allows the C++ code to be imported in other R packages via specifying LinkingTo: leapfrog in the DESCRIPTION file.

The simulation model is callable in R via a wrapper function leafrogR() created with Rcpp.

Installation

Install the development version from GitHub via devtools:

# install.packages("devtools")
devtools::install_github("mrc-ide/leapfrog")

Example

The file pjnz/bwa2021_v6.13.pjnz contains an example Spectrum file constructed from default country data for Botswana in Spectrum v2.13 (December 2021).

Prepare model inputs.

library(leapfrog)

pjnz <- system.file("pjnz/bwa2021_v6.13.pjnz", package = "leapfrog")

demp <- prepare_leapfrog_demp(pjnz)
hivp <- prepare_leapfrog_projp(pjnz)

Simulate ‘full’ age group (single-year age) and ‘coarse’ age group (collapsed age groups) models.

lsimF <- leapfrogR(demp, hivp, hiv_strat = "full")
lsimC <- leapfrogR(demp, hivp, hiv_strat = "coarse")

Compare the HIV prevalence age 15-49 years and AIDS deaths 50+ years. Deaths 50+ years are to show some noticeable divergence between the "full" and "coarse" age group simulations.

prevF <- colSums(lsimF$hivpop1[16:50,,],,2) / colSums(lsimF$totpop1[16:50,,],,2)
prevC <- colSums(lsimC$hivpop1[16:50,,],,2) / colSums(lsimC$totpop1[16:50,,],,2)

deathsF <- colSums(lsimF$hivdeaths[51:81,,],,2)
deathsC <- colSums(lsimC$hivdeaths[51:81,,],,2)

plot(1970:2030, prevF, type = "l", main = "Prevalence 15-49")
lines(1970:2030, prevC, col = 2)

plot(1970:2030, deathsF, type = "l", main = "AIDS Deaths 50+ years")
lines(1970:2030, deathsC, col = 2)

Benchmarking

Note: The function devtools::load_all() invokes pkgbuild::compile_dll(), which executes with default arguemnt debug = TRUE. This compiles the package with -O0 compiler optimisation flags. For benchmarking, make sure to install the package or compile the DLL with pkgbuild::compile_dll(debug = FALSE).

library(bench)
library(eppasm)

fp <- eppasm::prepare_directincid(pjnz)

bench::mark(leapfrogR(demp, hivp, hiv_strat = "full"),
            leapfrogR(demp, hivp, hiv_strat = "coarse"),
            eppasm::simmod(fp),
            check = FALSE)
#> # A tibble: 3 × 6
#>   expression                                       min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                                  <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 leapfrogR(demp, hivp, hiv_strat = "full")     4.34ms   5.01ms      194.    4.33MB     45.2
#> 2 leapfrogR(demp, hivp, hiv_strat = "coarse")  761.4µs 967.22µs      971. 1006.65KB     34.8
#> 3 eppasm::simmod(fp)                            1.01ms   1.29ms      741.    1.43MB     32.8

Code design

Simulation model

The simulation model is implemented as templated C++ code in inst/include/leapfrog-raw.h. This is the simulation model may be developed as a standalone C++ library that can be called by other software without requiring R-specific code features. The code uses header-only open source libraries to maximize portability.

R functions

The file src/leapfrogR.cpp contains R wrapper functions for the model simulation via Rcpp and RcppEigen.

Development notes

Simulation model

• The model was implemented using Eigen::Tensor containers. These were preferred for several reasons:

  • Benchmarking found they were slighlty more efficient than boost::multi_array.
  • Column-major indexing in the same order as R
  • Other statistical packages (e.g. TMB, Stan) rely heavily on Eigen so using Eigen containers slims the dependencies.