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EpiExposure package sticker

EpiExposure

EpiExposure is an R package for modeling and interpreting epidemiological exposure–lag–response relationships, simulating exposure profiles, and predicting plant disease outcomes from environmental drivers.

Core workflow

Model

Construct and fit DLNM-based models using multiple statistical frameworks and response distributions.

Interpret

Summarise exposure–lag relationships across lags, epidemiological periods, and complete exposure profiles.

Predict & Simulate

Evaluate observed or hypothetical exposure profiles, compare scenarios, and predict expected disease outcomes.

Why EpiExposure?

In plant disease epidemiology, environmental effects are often non-linear, cumulative, and time-dependent. Traditional approaches commonly summarize environmental conditions within predefined temporal windows, which may obscure delayed or continuously varying associations. EpiExposure provides a unified DLNM-based workflow for preserving exposure profiles, modeling lagged associations, and translating fitted models into epidemiologically interpretable summaries, simulations, and predictions.

What does it do?

EpiExposure enables you to:

  • Model non-linear exposure–lag–response relationships
  • Quantify lag- and period-specific environmental associations
  • Define biologically meaningful epidemiological periods
  • Summarise complete exposure profiles using the Exposure Cumulative Impact (ECI)
  • Decompose cumulative exposure impacts into exact lag-specific contributions
  • Simulate and compare hypothetical exposure–lag scenarios
  • Predict expected disease outcomes from complete exposure profiles
  • Compare and ensemble alternative DLNM specifications using cross-validation

Learning path

  1. Define exposure–lag structures with define_exposures().
  2. Build the DLNM design with build_design().
  3. Fit the epidemiological model with fit_epidlnm().
  4. Interpret exposure–lag relationships using summarise_effects().
  5. Explore hypothetical exposure profiles with simulate_exposures().
  6. Predict expected outcomes using predict_outcomes().
  7. Evaluate alternative model structures with find_bestfit() and ensemble_bestfit().

Installation

EpiExposure is currently available from GitHub. Install the package with:

install.packages("pak")
pak::pkg_install("tomazrg/EpiExposure")

Then load the package: