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

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

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.