UK Epidemiological Data Management

Contains utilities and functions for the cleaning, processing and management of patient level public health data for surveillance and analysis held by the UK Health Security Agency, UKHSA.

Combined Visualisation of Phylogenetic and Epidemiological Data

A collection of utilities and 'ggplot2' extensions to assist with visualisations in genomic epidemiology. This includes the 'phylepic' chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included 'ggplot2' extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) .

Basic Sensitivity Analysis of Epidemiological Results

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

Digital Epidemiological Analysis and Visualization Tools

Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) and Wickham et al. (2019, ISBN:9781492052040>.

Machine Learning and Mapping for Spatial Epidemiology

Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' and . It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.

Principal Component Pursuit for Environmental Epidemiology

Implementation of the pattern recognition technique Principal Component Pursuit tailored to environmental health data, as described in Gibson et al (2022) .

Report Templates and Helper Functions for Applied Epidemiology

A meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including 'epitabulate' (for epidemiological tables), 'epidict' (for data dictionaries), 'epikit' (for epidemiological utilities), and 'apyramid' (for age-sex pyramids). Simply load 'sitrep' to access all functions from the ecosystem.

Companion to R for Plant Disease Epidemiology Book

Datasets and utility functions to support the book "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease, assessing spatial patterns, and modeling plant disease epidemics based on weather predictors. These tools are intended for teaching and research in plant disease epidemiology. Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) .

Bayesian Parameter Estimation and Forecasting for Epidemiological Models

Methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in and .

Curated Datasets and Tools for Epidemiological Data Analysis

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible.