diff --git a/joss.05612/10.21105.joss.05612.crossref.xml b/joss.05612/10.21105.joss.05612.crossref.xml new file mode 100644 index 0000000000..aa9d61ccb4 --- /dev/null +++ b/joss.05612/10.21105.joss.05612.crossref.xml @@ -0,0 +1,230 @@ + + + + 20230926T103117-00ea19afc9996f096835500cbd3a6d23a1d37b3a + 20230926103117 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 09 + 2023 + + + 8 + + 89 + + + + Bernadette: Bayesian Inference and Model Selection for +Stochastic Epidemics in R + + + + Lampros + Bouranis + https://orcid.org/0000-0002-1291-2192 + + + + 09 + 26 + 2023 + + + 5612 + + + 10.21105/joss.05612 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.8376673 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5612 + + + + 10.21105/joss.05612 + https://joss.theoj.org/papers/10.21105/joss.05612 + + + https://joss.theoj.org/papers/10.21105/joss.05612.pdf + + + + + + Bayesian analysis of diffusion-driven +multi-type epidemic models with application to COVID-19 + Bouranis + 10.48550/arXiv.2211.15229 + 2022 + Bouranis, L., Demiris, N., +Kalogeropoulos, K., & Ntzoufras, I. (2022). Bayesian analysis of +diffusion-driven multi-type epidemic models with application to +COVID-19. arXiv. +https://doi.org/10.48550/arXiv.2211.15229 + + + Bernadette: Bayesian inference and model +selection for stochastic epidemics + Bouranis + 2023 + Bouranis, L. (2023). Bernadette: +Bayesian inference and model selection for stochastic epidemics. +https://CRAN.R-project.org/package=Bernadette + + + epidemia: Modeling of epidemics using +hierarchical Bayesian models + Scott + 2020 + Scott, J., Gandy, A., Mishra, S., +Unwin, J., Flaxman, S., & Bhatt, S. (2020). epidemia: Modeling of +epidemics using hierarchical Bayesian models. +https://imperialcollegelondon.github.io/epidemia/ + + + R: A language and environment for statistical +computing + R Core Team + 2023 + R Core Team. (2023). R: A language +and environment for statistical computing. R Foundation for Statistical +Computing. https://www.R-project.org/ + + + RStan: The R interface to +Stan + Stan Development Team + 2023 + Stan Development Team. (2023). RStan: +The R interface to Stan. https://mc-stan.org/ + + + Stan: A probabilistic programming +language + Carpenter + Journal of statistical +software + 1 + 76 + 10.18637/jss.v076.i01 + 2017 + Carpenter, B., Gelman, A., Hoffman, +M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, +P., & Riddell, A. (2017). Stan: A probabilistic programming +language. Journal of Statistical Software, 76(1), 1–32. +https://doi.org/10.18637/jss.v076.i01 + + + EpiEstim: Estimate time varying reproduction +numbers from epidemic curves + Cori + 2021 + Cori, A. (2021). EpiEstim: Estimate +time varying reproduction numbers from epidemic curves. +https://CRAN.R-project.org/package=EpiEstim + + + A new framework and software to estimate +time-varying reproduction numbers during epidemics + Cori + American Journal of +Epidemiology + 9 + 178 + 10.1093/aje/kwt133 + 2013 + Cori, A., Ferguson, N., Fraser, C., +& Cauchemez, S. (2013). A new framework and software to estimate +time-varying reproduction numbers during epidemics. American Journal of +Epidemiology, 178(9), 1505–1512. +https://doi.org/10.1093/aje/kwt133 + + + Practical considerations for measuring the +effective reproductive number, Rt + Gostic + PLoS Computational Biology + 12 + 16 + 10.1371/journal.pcbi.1008409 + 2020 + Gostic, K., McGough, L., Baskerville, +E., Abbott, S., Joshi, K., Tedijanto, C., Kahn, R., Niehus, R., Hay, J., +De Salazar, P., Hellewell, J., Meakin, S., Munday, J., Bosse, N., +Sherrat, K. e., Thompson, R., White, L., Huisman, J., Scire, J., … +Cobey, S. (2020). Practical considerations for measuring the effective +reproductive number, Rt. PLoS Computational Biology, 16(12), 1–21. +https://doi.org/10.1371/journal.pcbi.1008409 + + + Handbook of Markov chain Monte +Carlo + Brooks + 2011 + Brooks, S., Gelman, A., Jones, G., +& Meng, X. (2011). Handbook of Markov chain Monte Carlo. CRC +press. + + + loo: Efficient leave-one-out cross-validation +and WAIC for Bayesian models + Vehtari + 2023 + Vehtari, A., Gabry, J., Magnusson, +M., Yao, Y., Bürkner, P., Paananen, T., & Gelman, A. (2023). loo: +Efficient leave-one-out cross-validation and WAIC for Bayesian models. +https://mc-stan.org/loo/ + + + SARS-CoV-2 antibody prevalence in England +following the first peak of the pandemic + Ward + Nature Communications + 12 + 10.1038/s41467-021-21237-w + 2021 + Ward, H., Atchison, C., Whitaker, M., +Ainslie, K., Elliott, J., Okell, L., Redd, R., Ashby, D., Donnelly, C., +Barclay, W., Darzi, A., Cooke, G., Riley, S., & Elliott, P. (2021). +SARS-CoV-2 antibody prevalence in England following the first peak of +the pandemic. Nature Communications, 12, 905. +https://doi.org/10.1038/s41467-021-21237-w + + + + + + diff --git a/joss.05612/10.21105.joss.05612.jats b/joss.05612/10.21105.joss.05612.jats new file mode 100644 index 0000000000..7b9399aa0e --- /dev/null +++ b/joss.05612/10.21105.joss.05612.jats @@ -0,0 +1,518 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5612 +10.21105/joss.05612 + +Bernadette: Bayesian Inference and Model Selection for +Stochastic Epidemics in R + + + +https://orcid.org/0000-0002-1291-2192 + +Bouranis +Lampros + + + + + +Department of Statistics, Athens University of Economics +and Business, Athens, Greece + + + + +26 +9 +2023 + +8 +89 +5612 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Bayesian +Epidemics +R +Stan + + + + + + Summary +

The Coronavirus Disease 2019 (COVID-19) outbreak caused by + SARS-CoV-2 has led to developments in Bayesian infectious disease + modeling, allowing modelers to assess the impact of mitigation + strategies on transmission and to quantify the burden of the pandemic. + The Bernadette package + (Bouranis, + 2023) for the open-source R statistical software + (R Core + Team, 2023) implements a Bayesian evidence synthesis approach + to modeling the age-specific transmission dynamics of a disease based + on daily mortality counts. The functionality of + Bernadette can be used to reconstruct the + epidemic drivers from publicly available data, to estimate key + epidemiological quantities like the rate of disease transmission, the + latent counts of infections and the reproduction number for a given + population over time, and to perform model comparison using + information criteria. While Bernadette is + motivated by the analysis of healthcare surveillance data related to + COVID-19, it provides a template for implementation to a broader range + of infectious disease epidemics and outbreaks.

+ + + Statement of need +

The modeling of disease transmission dynamics is an active research + area; a list of R packages dedicated to the analysis of epidemics is + presented in the R Epidemics Consortium website + (https://www.repidemicsconsortium.org/). + Among these packages, EpiEstim + (Cori + et al., 2013; + Cori, + 2021) offers a data-driven Bayesian framework for the + reconstruction of the time-varying reproduction number of a disease + which represents the number of secondary infections generated by a + case infected at day t. + EpiEstim uses areal time-series in the form of + case counts for a given period and population. The incidence of + infection is assumed to follow a Poisson process with expectation + given by a renewal equation. The epidemia + package + (Scott + et al., 2020) links observed areal data to latent infections, + which are in turn modeled as a self-exciting process tempered by + time-varying reproduction numbers. It also supports Bayesian + multilevel models by pooling information across multiple + populations.

+

Bernadette complements these packages by + implementing the Bayesian hierarchical modeling approach described in + Bouranis et al. + (2022). It + links the observed age-stratified mortality counts for the population + of a given country over time to latent infections via an + over-dispersed count model. The change in infections over time is + governed by a deterministic multi-type compartmental model which is + driven by potentially non-scalar diffusion processes to adequately + capture the temporal evolution of the age-stratified transmission + rates. Bernadette relaxes the assumption of a + homogeneous population, incorporates age structure and accounts for + the presence of social structures via publicly available contact + matrices. This allows for further evidence synthesis utilizing + information from contact surveys and for sharing statistical strength + across age groups and time. Further, the Bayesian evidence synthesis + approach implemented in the Bernadette package + enables learning the age-stratified virus transmission rates from one + group to another, with a particular focus on the transmission rate + between and onto vulnerable groups which could support public health + authorities in devising interventions targeting these groups. The + effective reproduction number is estimated from the daily + age-stratified mortality counts, accounting for variations in + transmissibility that are not obvious from reported infection counts. + While estimation of the uncertainty over the effective reproduction + number is itself a challenging problem + (Gostic + et al., 2020), it is propagated naturally via Markov chain + Monte Carlo + (Brooks + et al., 2011).

+ + + Functionality +

Bernadette features eight main functions for + data processing and visualization, a main function for specifying and + fitting the Bayesian hierarchical model and five functions for + post-processing and visualization of posterior model estimates of + important epidemiological quantities. The GitHub page for this package + contains a detailed + README + file with a description of the modeling framework and the steps + involved in the workflow.

+ + Data processing and visualization + + +

age_distribution: Imports the age + distribution of a country for a given year, broken down by + 5-year age bands and gender, following the United Nations 2019 + Revision of World Population Prospects.

+ + +

aggregate_age_distribution: Aggregates + the age distribution according to user-defined age + groupings.

+ + +

contact_matrix: For a given country, + it imports a 16 by 16 contact matrix whose row + i of a column j corresponds to + the number of contacts made by an individual in group + i with an individual in group + j.

+ + +

aggregate_contact_matrix: Aggregates + the contact matrix according to user-defined age groupings.

+ + +

aggregate_ifr_react: Aggregates the + age-specific Infection Fatality Ratio (IFR) estimates reported + by the REACT-2 large-scale community study of SARS-CoV-2 + seroprevalence in England + (Ward + et al., 2021) according to age group mappings defined by + the user.

+ + +

itd_distribution: Parameterizes the + time distribution from an infection to an observation. + Bernadette focuses on observed mortality + counts, therefore we refer to this distribution as the + infection-to-death distribution.

+ + +

plot_age_distribution: visualizes a + given age distribution using a bar plot.

+ + +

plot_contact_matrix: visualizes a + given contact matrix using a heatmap.

+ + +

The age-specific time series of mortality counts and the + age-specific time series of cumulative reported infections complete + the list of data streams that are integrated together with expert + knowledge into a coherent modeling framework via a Bayesian evidence + synthesis approach for estimation of key epidemiological quantities. + Two example datasets (objects + age_specific_mortality_counts and + age_specific_cusum_infection_counts) are + provided with the Bernadette package to guide + the user regarding the format of their input.

+ + + Parameter estimation +

The main function for Bayesian parameter estimation, + stan_igbm, allows for specification of a + joint distribution for the outcomes (in this case, the age-specific + mortality counts) and the unknown quantities, which is expressed by + the likelihood for the outcomes conditional on the unknowns + multiplied by a marginal prior distribution for the unknowns. This + joint distribution is proportional to the posterior distribution of + the unknowns conditional on the observed data. Prior beliefs for the + unknown model parameters can be expressed by a selection of + appropriate distributions available to the end-user. + Bernadette uses the framework offered by the + probabilistic programming language Stan + (Carpenter + et al., 2017) to specify a model and draw from the posterior + distribution using MCMC. The user-specified model is internally + translated into data that are passed to a precompiled + Stan program and then it is fit using + sampling methods from rstan + (Stan + Development Team, 2023).

+ + + Post-processing +

The output of stan_igbm contains draws + from the posterior distribution, which can be post-processed and + visualized to extract insights about the mechanism of disease + transmission over a given period.

+ + +

plot_posterior_cm: Density plots of + the posterior distribution of the random contact matrix.

+ + +

posterior_infections: Summarizes the + posterior distribution of the infection counts over time + (age-specific and aggregated). It is accompanied by the plotting + function plot_posterior_infections.

+ + +

posterior_mortality: Summarizes the + posterior distribution of the mortality counts over time + (age-specific and aggregated). It is accompanied by the plotting + function plot_posterior_mortality.

+ + +

posterior_transmrate: Summarizes the + posterior distribution of the age-specific transmission rate. It + is accompanied by the plotting function + plot_posterior_transmrate.

+ + +

posterior_rt: Summarizes the posterior + distribution of the time-varying reproduction number. The + posterior trajectory is visualized with + plot_posterior_rt.

+ + +

The output of stan_igbm can additionally + be used to compute approximate leave-one-out cross-validation with + the loo R package + (Vehtari + et al., 2023). This enables estimation of information + criteria which are considered when comparing among a set of + alternative models for the same data.

+ + + + Licensing and Availability +

The Bernadette package is licensed under the + GNU General Public License (GPL v3.0). It is available on CRAN, and + can be installed using + install.packages("Bernadette"). All + code is open-source and hosted on GitHub, and bugs can be reported at + https://github.com/bernadette-eu/Bernadette/issues/.

+ + + Acknowledgements +

Lampros Bouranis’ research is supported by the European Union’s + Horizon 2020 research and innovation programme under the Marie + Sklodowska-Curie grant agreement No 101027218.

+ + + + + + + + BouranisL. + DemirisN. + KalogeropoulosK. + NtzoufrasI. + + Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19 + arXiv + 2022 + 10.48550/arXiv.2211.15229 + + + + + + BouranisLampros + + Bernadette: Bayesian inference and model selection for stochastic epidemics + 2023 + https://CRAN.R-project.org/package=Bernadette + + + + + + ScottJ. + GandyA. + MishraS. + UnwinJ. + FlaxmanS. + BhattS. + + epidemia: Modeling of epidemics using hierarchical Bayesian models + 2020 + https://imperialcollegelondon.github.io/epidemia/ + + + + + + R Core Team + + R: A language and environment for statistical computing + R Foundation for Statistical Computing + Vienna, Austria + 2023 + https://www.R-project.org/ + + + + + + Stan Development Team + + RStan: The R interface to Stan + 2023 + https://mc-stan.org/ + + + + + + CarpenterB. + GelmanA. + HoffmanM. + LeeD. + GoodrichB. + BetancourtM. + BrubakerM. + GuoJ. + LiP. + RiddellA. + + Stan: A probabilistic programming language + Journal of statistical software + 2017 + 76 + 1 + 10.18637/jss.v076.i01 + 1 + 32 + + + + + + CoriA. + + EpiEstim: Estimate time varying reproduction numbers from epidemic curves + 2021 + https://CRAN.R-project.org/package=EpiEstim + + + + + + CoriA. + FergusonN. + FraserC. + CauchemezS. + + A new framework and software to estimate time-varying reproduction numbers during epidemics + American Journal of Epidemiology + 2013 + 178 + 9 + 10.1093/aje/kwt133 + 1505 + 1512 + + + + + + GosticK. + McGoughL. + BaskervilleE. + AbbottS. + JoshiK. + TedijantoC. + KahnR. + NiehusR. + HayJ. + De SalazarP. + HellewellJ. + MeakinS. + MundayJ. + BosseN. + SherratK.e + ThompsonR. + WhiteL. + HuismanJ. + ScireJ. + BonhoefferS. + StadlerT. + WallingaJ. + FunkS. + LipsitchM. + CobeyS. + + Practical considerations for measuring the effective reproductive number, Rt + PLoS Computational Biology + 2020 + 16 + 12 + 10.1371/journal.pcbi.1008409 + 1 + 21 + + + + + + BrooksS. + GelmanA. + JonesG. + MengX. + + Handbook of Markov chain Monte Carlo + CRC press + 2011 + + + + + + VehtariA. + GabryJ. + MagnussonM. + YaoY. + BürknerP. + PaananenT. + GelmanA. + + loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models + 2023 + https://mc-stan.org/loo/ + + + + + + WardH. + AtchisonC. + WhitakerM. + AinslieK. + ElliottJ. + OkellL. + ReddR. + AshbyD. + DonnellyC. + BarclayW. + DarziA. + CookeG. + RileyS. + ElliottP. + + SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic + Nature Communications + 2021 + 12 + 10.1038/s41467-021-21237-w + 905 + + + + + +
diff --git a/joss.05612/10.21105.joss.05612.pdf b/joss.05612/10.21105.joss.05612.pdf new file mode 100644 index 0000000000..d8b6a187a4 Binary files /dev/null and b/joss.05612/10.21105.joss.05612.pdf differ