2021-09-17 17:31:04

About me

  • Originally from Germany, moved to U.S. for graduate school - and never left 😄.
  • PhD in Physics from Georgia Tech, Postdoc in Computational Biology at Emory. Since 2009, Assistant/Associate/Full Professor in Epidemiology & Biostatistics, UGA.
  • Data analysis and modeling of infectious diseases on the population level and individual patient level.
  • A lot of influenza and norovirus, some other bugs. Recently also a lot of SARS-CoV-2.
  • More information:

Motivation for this talk

  • After 15+ years in academia, I want to branch out into some more applied work.
  • To get that process started, I want to do a short term (approx. 8 month) industry “internship”.
  • This presentation is part of my looking for something effort.
  • Also, academics just like to talk about their work 😁.

Presentation overview

  • A quick whirlwind of several projects that showcase different topics and approaches.
  • Some projects are basic research, some are more applied/operational.
  • I’ll describe each project very briefly. Happy to go deeper in the Q&A.
  • Slides here: https://www.andreashandel.com/presentations/

A COVID-19 Tracker


  • Early in the pandemic, I was part of a team of experts asked to forecast COVID.
  • I wanted quick access to data to make (intuitive) forecasts.
  • None of the COVID trackers at that time had what I wanted.
  • With a few team members, we built our own.


  • R/Shiny app that pulls data from different sources (JHU, NYT, Covidtracking, etc.)
  • Visualize data as time-trends for countries/states/counties.
  • Allow for adjustments, e.g. total cases versus per 100K, trend-lines, custom date ranges.

Results & More Information

Forecasting COVID in the US


  • We want to know how many cases/hospitalizations/deaths to expect.
  • A team of scientists coordinated with the CDC to set up a forecasting system: https://covid19forecasthub.org/
  • We/UGA are one of the participating teams (somewhat different model).


Dynamic, mechanistic (SIR type) model, implemented in R.


Model is calibrated by fitting to case and death data for each state, then used to forecast.

More Information

Community Outbreak Investigation of SARS-CoV-2 Transmission Among Bus Riders in Eastern China


  • Understanding when and how SARS-CoV-2 transmits is important.
  • Getting detailed data was and is difficult.
  • We collaborated with colleagues from China to analyze a well-described transmission scenario.


  • Data from a COVID-19 outbreak entailing 2 buses (bus #2 containing an infected person) going to a temple for an (outdoor) religious ceremony.
  • Basic statistical analysis to determine risk of infection.