NEWS

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serofoi 1.0.2

New features

• Serological surveys simulation functionalities

  • Introduce enhanced data simulation functionalities to support a broader range of use cases for serosurvey analysis.
  • Add a dedicated vignette for data simulation to guide users on simulating data for serosurveys.

• Enables the implementation of a wide variety of new models

  • Add forward random walk age-varying models with uniform and normal priors.
  • Improves previous implementation of forward random walk time-varying models.
  • Enables prior distributions specifications by means of package specific functions like sf_normal() and sf_uniform().
  • Support for seroreversion rate estimation for all models with uniform and normal priors.

Breaking changes

• Replaced old modelling and visualization functions, making Bayesian model selection and specification more flexible.
• New models and functionalities include constant, time-varying, and age-varying models, as well as options for estimating seroreversion rates.
• Updated the R-hat convergence threshold for model convergence diagnostics to R-hat<1.01, following Vehtari et al. (2021).

Minor changes

• Remove the simdata_* datasets from the package and replaced them with code-based simulation in vignettes.
• Add missing examples to exported functions
• Add missing documentation entries

Internal changes

• Unit testing

  • Added unit tests for key package functionalities, including:
    • fit_seromodel() function (#213).
    • Visualization functionalities (#215).
    • build_stan_data and related functions (#232).
    • Validation functions (#235).
  • Test coverage increased to 100%

• Refactorization of simulation examples

  • Replaced static simulated datasets in tests and vignettes with dynamic examples using data simulation functions.

• Documentation improvements

  • Enhanced documentation for new functionalities and updated vignettes to reflect recent changes.

serofoi 0.1.0

New features

• Data simulation functions from time-varying or age-varying force of infection trends. The following is an example to simulate from a constant (in time) force of infection:

foi_sim_constant <- rep(0.02, 50)

serodata_constant <- generate_sim_data(  
  sim_data = data.frame(  
    age = seq(1, 50),  
    tsur = 2050),  
  foi = foi_sim_constant,  
  sample_size_by_age = 5  
)  

To generate grouped serosurveys the function group_sim_data can be used:

serodata_constant <- group_sim_data(serodata_constant, step = 5)

Breaking changes

• Simplifies fit_seromodel output

  • Before, the output of fit_seromodel was a list:

    seromodel_object <- list(
      fit = fit,
      seromodel_fit = seromodel_fit,
      serodata = serodata,
      serodata = serodata,
      stan_data = stan_data,
      ...
    )

• Now, the output is a stan_fit object as obtained from rstan::sampling. Because of this, some plotting functionalities now require serodata as an input.

• Initial prior distribution parameters foi_location and foi_scale can be specified explicitly in fit_seromodel:

seromodel <- fit_seromodel(
  serodata,
  foi_model = "tv_normal",
  foi_location = 0,
  foi_scale = 1
)

Depending on the selected model foi_model, the meaning of the parameters change. For the tv_normal_log model these parameters must be in logarithmic scale; the recommended usage is:

seromodel <- fit_seromodel(
  serodata,
  foi_model = "tv_normal_log",
  foi_location = -6,
  foi_scale = 4
)

• Chunks structure specification is now possible
  • Before, the models estimated one value of the force of infection per year in the time spanned by the serosurvey:

data(chagas2012)
serodata <- prepare_serodata(chagas2012)
seromodel <- fit_seromodel(serodata, foi_model = "tv_normal")

• Now, the amount of force of infection values estimated by the models depend on the specified chunk structure. This can either be specified by size:

seromodel <- fit_seromodel(serodata, foi_model = "tv_normal", chunk_size = 10)

or explicitly:

chunks <- rep(c(1, 2, 3, 4, 5), c(10, 10, 15, 15, max(serodata$age_mean_f)-50))
seromodel <- fit_seromodel(serodata, foi_model = "tv_normal", chunks = chunks)

• Deprecate run_seromodel. Initially this function was intended to be a handler for fit_seromodel for cases when the user may need to implement the same model to multiple independent serosurveys; now we plan to showcase examples of this using the current functionalities of the package (to be added in future versions to the vignettes).

Minor changes

• Refactorization of the visualization module

  1. plot_seroprev allows for data binning (age group manipulation) by means of parameters bin_data=TRUE and bin_step.
  2. Automatic selection of ymin and ymax aesthetics plotting functions (with the exception of plot_rhats).
  3. Correct input validation

• Remove duplicated data in veev2012 dataset

Internal changes

• Remove large files from git history (see #77).

• Added input validation for the following functions:

  • prepare_serodata
  • generate_sim_data
  • get_age_group
  • fit_seromodel
  • extract_seromodel_summary
  • plot_seroprev
  • plot_seroprev_fitted
  • plot_foi
  • plot_seromodel

• Unit testing:

  • Separate modelling tests by model
  • Use of dplyr::near to test models statistical validity
  • Add tests for data simulation functions

• Update package template in accordance to {packagetemplate}

serofoi 0.0.9

This release of serofoi, includes the following:

1. Implementation of package modules: Incorporates data preparation, modelling, and visualization modules, they enable efficient handling of data, perform statistical modelling, and generate visual representations of the results.
2. Documentation: It consists of vignettes, a website, and uses cases that provide detailed instructions on how to use the package effectively.
3. Implementation of 3 models for calculating the Force-of-Infection (FoI): The first model is the constant or endemic model, which assumes a stable FoI over time. The second and third models are time-varying, with the normal FoI model representing a slow change in FoI and the normal-log FoI model representing a fast epidemic change in FoI.
4. Definition of coverage test to assurance the quality of the package.

Overall, this release introduces essential package functionality, comprehensive documentation, various FoI models, and a coverage test, enabling users to analyse seroprevalence data and calculate the Force-of-infection.