I am a computational epidemiologist at Johns Hopkins University working under the CDC Center for Forecasting Analytics’ Insight Net. I work primarily on respiratory infectious diseases with interest in real-time forecasting using deep learning models and Bayesian inference of transmission trees leveraging genetic, epidemiological and contact data.
A statistical framework to compare sets of transmission trees
Methods for comparing collections of graphs to test whether they originate from the same or different generative processes.
Infer group-level assortativity from transmission trees
A novel framework for estimating group transmission assortativity, quantifying how transmission patterns vary across different population groups.
Helper functions for the outbreaker2 R package
Tools to analyse and visualise Bayesian inference of transmission chains with outbreaker2.
Tools to time pipe operations in R
Measure elapsed time in R pipelines. Works seamlessly with native R pipe (|>) and tidyverse workflows.
A complete pipeline for infectious disease forecasts
incast is an R package for infectious disease nowcasting and
forecasting developed through Insight Net, a CDC Center for Forecasting
and Outbreak Analytics initiative.
Hospital census forecasts from hubverse admission forecasts
censcast convolves hubverse-format admission quantile forecasts with a length-of-stay (LOS) distribution to produce hubverse-format census quantile forecasts.
A denoising diffusion probabilistic models for infectious disease forecasting.
Email me at [email protected]