Continuous Epidemiological Week Indexing for Time-Series Analysis

Provides a simple algorithm to generate a continuous epidemiological week index from date variables in a dataframe. Weeks are computed as sequential 7-day intervals starting from the earliest observed date. They do not reset at calendar year boundaries and are not ISO 8601 nor MMWR calendar weeks. The approach is intended for epidemiological modeling and time-series analysis where temporal continuity is required. The generated weeks are sequential and do not reset at calendar year boundaries.

Data Visualisation Functions for Epidemiological Data Science Products

Tools for making epidemiological reporting easier with consistent static and dynamic charts and maps. Builds on 'ggplot2' for static visualizations as described in Wickham (2016) and 'plotly' for interactive visualizations as described in Sievert (2020) .

Access Infectious and Epidemiological Data via 'disease.sh API'

Provides functions to access real-time infectious disease data from the 'disease.sh API', including COVID-19 global, US states, continent, and country statistics, vaccination coverage, influenza-like illness data from the Centers for Disease Control and Prevention (CDC), and more. Also includes curated datasets on a variety of infectious diseases such as influenza, measles, dengue, Ebola, tuberculosis, meningitis, AIDS, and others. The package supports epidemiological research and data analysis by combining API access with high-quality historical and survey datasets on infectious diseases. For more details on the 'disease.sh API', see < https://disease.sh/>.

Literature Matrix Synthesis Tools for Epidemiology and Health Science Research

An easy-to-use workflow that provides tools to create, update and fill literature matrices commonly used in research, specifically epidemiology and health sciences research. The project is born out of need as an easy–to–use tool for my research methods classes.

Dynamical Systems Approach to Infectious Disease Epidemiology (Ecology/Evolution)

Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.

Estimating Plant Pathogen Epidemiology Parameters from Laboratory Assays

Provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), . These models were originally implemented in the 'EpiPv' 'GitHub' package.

Flexible Extended State-Space Epidemiological Models with Modern Inference

An extended epidemiological modelling framework that goes beyond the classical SIR (Susceptible-Infectious-Recovered) model. Supports SEIR (Susceptible-Exposed-Infectious-Recovered), SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased), and age-stratified compartmental models with flexible intervention functions (spline-based, Gaussian process, or user-defined). Inference is available via maximum likelihood or sequential Monte Carlo (SMC, also known as particle filtering) with no external binary dependencies. Includes a dependency-free real-time effective reproduction number (Rt) estimator, spatial multi-patch models with gravity-model mobility, ensemble forecasting via Bayesian model averaging (BMA), and proper scoring rules including CRPS (Continuous Ranked Probability Score), coverage, and MAE (Mean Absolute Error) for forecast evaluation. Methods follow Anderson and May (1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon (2001) , Cori et al. (2013) , and Gneiting and Raftery (2007) .

Code Sharing at the Department of Epidemiology Research at Statens Serum Institut

This is a collection of assorted functions and examples collected from various projects. Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using 'grf'.

Tables of Descriptive Statistics in HTML

Create HTML tables of descriptive statistics, as one would expect to see as the first table (i.e. "Table 1") in a medical/epidemiological journal article.

Process Based Epidemiological Model for Cercospora Leaf Spot of Sugar Beet

Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.