outbreak-analysis

A modular Bayesian framework for real-time infectious disease surveillance. Provides tools for nowcasting, reproduction number estimation, delay estimation, and forecasting from data subject to reporting delays, right-truncation, missing data, and incomplete ascertainment

Course on nowcasting and forecasting of infectious disease dynamics

R package to calculate the final size of an SIR epidemic in populations with heterogeneity in social contacts and disease susceptibility

R package to estimate disease severity and under-reporting in real-time, accounting for reporting delays in epidemic time-series

R package for statistical and spatiotemporal modeling of epidemiological data for vector-borne diseases in Colombia

Understanding, evaluating, and improving forecasts of infectious disease burden

Python toolkit for epidemiological modelling and reporting. SIR/SEIR/SEIRD compartmental models, sensitivity analysis, interactive visualisations, and automated HTML/Markdown/JSON reports, built for low-resource health contexts.

Tutorials to learn reading, cleaning and validating case data, and converting line list data to incidence for visualizing epidemic curves.

[Not published - under active development] A Store of Outbreak Analytics Pipelines Provided as Rmarkdown Report Templates

Collection of reproducible how-to guides of Outbreak data analysis tasks using R packages.

Tutorials to learn real-time analysis that includes accessing epidemiological delays, estimating transmission metrics, forecasting, and severity from aggregated incidence data, superspreading from line list and contact data, and simulating transmission chains.

Tutorials to learn accessing and analyzing social contact matrices, scenario modelling to simulate disease spread and investigate interventions, and modelling disease burden.

This project analyzes a cholera outbreak using Python, integrating environmental, historical, and climatic variables to create a predictive model for early detection and response.

Stochastic Outbreak Learning and Variance-Aware Regression (SOLVAR) is an adaptive ensemble of ML and endemic-epidemic experts for probabilistic, MDRO-specific outbreak early warning, designed for infection control teams using hospital microbiology data.

Phylogenetic analysis of Ortohantavirus andes responsible for MV Hondius cruise outbreak in April-May 2026

A practice epidemiological analysis project in R (Posit Cloud), investigating the relationship between declining MMR (measles-mumps-rubella) vaccination coverage and the 2026 U.S. measles resurgence.

R analysis of the 2026 U.S. cyclosporiasis outbreak using CDC NNDSS data — seasonal baselines, state-level anomaly ranking, and documented data-cleaning gotchas.

Deidentified toolkit for within-outbreak measles genomics: sub-lineage transmission-structure analysis, case-based Rt (Cori et al. 2013 / EpiEstim), and BEAST2/PhyDyn tooling documenting the negative R0 result. Includes a synthetic demo dataset.