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, epidemiologica[...]
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.
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 simple pipeline for infectious disease forecasts
Validates input data, optionally applies nowcasting to adjust for reporting delays, and generates forecasts.
A denoising diffusion probabilistic models for infectious disease forecasting.
A real-time nosocomial outbreak analytics platform.
Email me at cgeisma1@jhu.edu