Welcome to my personal web page.

I am a postdoctoral researcher working with Melanie Schienle at the Chair of Statistics and Econometrics at Karlsruhe Insitute for Technology and Tilmann Gneiting in the Computational Statistics Group at Heidelberg Institute of Theoretical Studies. I am moreover a fellow of the YIG Preparation Programme at KIT. You can also find me on Google Scholar and Twitter.

Research Interests

• Epidemic forecasting
• Forecast evaluation
• Count time series modelling

Short Bio

• since May 2020: Postdoctoral researcher, KIT Karlsruhe and HITS Heidelberg, Germany
• 2020: PhD Epidemiology and Biostatistics, University of Zurich, Switzerland. Thesis title: Statistical modelling and forecsting of infectious disease surveillance counts. Supervisor: Leonhard Held.
• 2016: MSc Statistics, LMU Munich, Germany
• 2013: BA Sociology, LMU Munich, Germany
• 2012: BSc Statistics, LMU Munich, Germany

Current Projects

My postdoctoral work is part of the Helmholtz Information & Data Science Pilot Project SIMCARD. Currently I spend most of my time on the following projects:

• German and Polish COVID19 Forecast Hub
• US COVID19 Forecast Hub, led by the Reich Lab at UMass Amherst

Papers and Preprints

• Bracher J, Wolffram D, Deuschel J, Görgen K, Ketterer J et al (2020). Short-term forecasting of COVID-19 in Germany and Poland during the second wave – a preregistered study. Preprint available here.
• Bracher J, Ray EL, Gneiting T and Reich NG (2020) Evaluating epidemic forecasts in an interval format. PLOS Computational Biology, accepted. Preprint available here.
• Ray EL, Wattanachit N, Niemi, J, Kanji AH, House, K, Cramer EY, Bracher J et al (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. Preprint available here.
• Bracher, J and Held, L (2020) A marginal moment matching approach for fitting endemic-epidemic models to underreported disease surveillance counts. Biometrics, in press. Preprint available here.
• Bracher J and Held L (2019). Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction. International Journal of Forecasting, in press. Preprint available here.
• Nightingale ES, Chapman LAC, Srikantiah S, Subramanian S, Purushothaman J, Bracher J, Cameron M, and Medley G (2020) A spatio-temporal approach to short-term forecasting of visceral leishmaniasis diagnoses in India. PLOS Neglected Tropical Diseases 14(7): e0008422. Preprint available here.
• Bracher J (2019). On the multibin logarithmic score used in the FluSight competitions. Proceedings of the National Academy of Sciences, 116(42), 20809-20810. Preprint of an extended version available here.
• Bracher J (2019) A new INARMA(1, 1) model with Poisson marginals. In: Steland, A., Rafajlowicz, E., Okhrin, O. (Eds.): Stochastic Models, Statistics and Their Applications, 323-333. Springer. Preprint available here.
• Bracher J (2019). Comment on “Under‐reported data analysis with INAR‐hidden Markov chains, Statistics in Medicine 38(5), 893-898. Preprint available here.
• Held L and Bracher J (2019). Invited discussion on Osthus et al, 2019, Bayesian Analysis 14(1), 296–300.
• Held L, Meyer S and Bracher J (2017). Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture, Statistics in Medicine 36(22), 3443-3460. Preprint available here.
• Keuschnigg M, Bader F and Bracher J (2016). Using crowdsourced online experiments to study context-dependency of behavior. Social Science Research 59, 68-82. Preprint available here.
• Fuertes E, Bracher J, Flexeder C, Markevych I, Klümper C, Hoffmann B, Krämer U, von Berg A, Bauer C-P, Koletzko S, Berdel D, Heinrich J, Schulz H (2016). Long-term air pollution exposure and lung function in 15 year-old adolescents living in an urban and rural area in Germany: The GINIplus and LISAplus cohorts, International Journal of Hygiene and Environmental Health 218(7), 656-665. Preprint available here

Recent talks

• Assembling, comparing and combining COVID-19 forecasts. M2C Seminar Series, Frankfurt Institute of Advanced Studies (FIAS), 28 Oct 2020.
• Evaluating probabilistic COVID19 forecasts under partial missingness: A pairwise comparison approach. MIDAS COVID-19 Modeling Collaboration Call, 27 Oct 2020. Slides available here.
• Assembling, comparing and combining COVID-19 forecasts. KI at KIT Online Lecture Series, Karlsruhe Institute of Technology, 28 Oct 2020.
• Assembling, comparing and combining COVID-19 forecasts. HITS Scientific Seminar, Heidelberg Institute for Theoretical Studies, 28 Sep 2020.