AbcSmc

Sequential Monte Carlo Approximate Bayesian Computation with Partial Least Squares

AbcSmc is a parameter estimation library implemented in C++ that has been developed to enable fitting complex stochastic models to disparate types of empirical data. We use partial least squares to address problems arising from parameters and/or empirical metrics that co-vary or are unidentifiable (parameters) or uninformative (metrics). Because of the long running times, often requiring many processor-core years of computation, AbcSmc is particularly well-suited to being used in high performance (e.g. cluster or supercomputer) environments. AbcSmc includes a convenient means of distributing and gathering work in HPC environments: the program pulls jobs from and writes output to a standardized SQL database, and implements a dynamic load balancing scheme to compensate for variable simulation run times and hardware failures. AbcSmc uses SQLite for the database, for portability of data.

Quick Start

Install the GNU Scientific Library (https://www.gnu.org/software/gsl/) if you don't already have it. For example:

sudo apt install libgsl-dev                   # on Ubuntu

Then, from a projects root folder:

git clone git@github.com:tjhladish/AbcSmc.git # get the repository
cd AbcSmc                                     # enter the resulting directory 
git submodule update --init --recursive       # pull down the associated submodules
cmake -S . -B build                           # setup build folder
cd build                                      # enter build folder
make abc                                      # make the library
cd ../examples                                # enter the example directory
make run_sql; make run_sql; make run_sql      # 3 passes with the example dice game simulator

Peer-reviewed publications that have used AbcSmc:

Hladish, T.J., C.A.B. Pearson, D.P. Rojas, K.B. Toh, P. Manrique-Saide, G.M. Vazquez-Prokopec, M.E. Halloran, I.M. Longini (2020) Designing effective control of dengue with combined interventions. Proc Natl Acad Sci U S A. 2020 Jan 23. pii: 201903496. doi:10.1073/pnas.1903496117

Hladish, T.J., C.A.B. Pearson, D.P. Rojas, H. Gómez-Dantés, M.E. Halloran, G.M. Vazquez-Prokopec, I.M. Longini (2018) Forecasting the effectiveness of indoor residual spraying for reducing dengue burden. PLoS Negl Trop Dis 12(6): e0006570. doi:10.1371/journal.pntd.0006570

Flasche, S., M. Jit, I. Rodríguez-Barraquer, L. Coudeville, M. Recker, K. Koelle, G. Milne, T.J. Hladish, A. Perkins, D.A.T. Cummings, I. Dorigatti, D.J. Laydon, G. España, J. Kelso, I. Longini, J. Lourenco, C.A.B. Pearson, R.C. Reiner, L. Mier-y-Terán-Romero, K. Vannice, N. Ferguson (2016) The long term safety, public health impact, and cost effectiveness of routine vaccination with a recombinant, live-attenuated dengue vaccine (Dengvaxia): a model comparison study. PLoS Medicine 13(11): e1002181. doi:10.1371/journal.pmed.1002181

Hladish, T.J., C.A.B. Pearson, D.L. Chao, D.P. Rojas, G.L. Recchia, H. Gómez-Dantés, M.E. Halloran, J.R.C. Pulliam, I.M. Longini (2016) Projected impact of dengue vaccination in Yucatán, Mexico. PLoS Negl Trop Dis 10(5): e0004661. doi:10.1371/journal.pntd.0004661

License

AbcSmc is licensed under GPLv3 (or later, at your discretion); see license here.