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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

Define exposure–lag structures with define_exposures().
Build the DLNM design with build_design().
Fit the epidemiological model with fit_epidlnm().
Interpret exposure–lag relationships using summarise_effects().
Explore hypothetical exposure profiles with simulate_exposures().
Predict expected outcomes using predict_outcomes().
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:

library(EpiExposure)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.