MIDAS Webinar: Assembly of the first multi-scale ensemble COVID-19 model

May 28, 2021


May 28, 2021
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The MIDAS Webinar Series features research by MIDAS members, and is open to the public. 

Date/time: Friday May 28, 12:00 – 1:00pm, EDT

Overall Topic: Assembly of the first multi-scale ensemble COVID-19 model


  • Filippo Castiglione, Research Director, National Research Council of Italy
  • Lucas Böttcher, Assistant Professor, Frankfurt School of Finance & Management
  • Jacob Barhak, Sole Proprietor


Talk 1. Filippo Castiglione – 15 minutes
Topic: Deriving  age-dependent mortality probability using stochastic agent-based immune simulation
We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We set parameters to reproduce known inter-patient variability and general epidemiological statistics. We reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Finally, we infer the age-dependent mortality probability function from the in silico cohort data.  

Filippo Castiglione Bio:
Filippo Castiglione is currently working as a research director at the National Research Council of Italy and is an adjunct professor at RomaTre University. He graduated in Computer Science at the University of Milan (Italy) and got a Ph.D. in Scientific Computing from the University of Cologne (Germany). He was a visiting scholar at IBM T.J. Watson Research Center, at the Department of Molecular Biology of Princeton University, at the Harvard Medical School, and Institute for Medical Bio-Mathematics in Tel Aviv (Israel). He has received EU research funds in several ICT for Health projects. His interests range from the study of complex systems in general to the modelling of biological systems with a particular interest in the immune system and related pathologies. He is also interested in Machine Learning and Artificial Intelligence methodologies applied to Medicine and Biology.

Talk 2: Lucas Böttcher – 15 minutes

Topic: Using excess deaths and testing statistics to determine COVID-19 mortalities
Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We also formulate an infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures.

Lucas Böttcher Bio:  
Lucas Böttcher is an Assistant Professor for Computational Social Science at the Frankfurt School of Finance & Management. Lucas received his PhD in applied mathematics and computational physics from ETH Zürich in 2018. Prior to his appointment at Frankfurt School, he worked as a Lecturer at the Institute for Theoretical Physics and as a postdoctoral researcher in the Macroeconomics, Innovation, and Policy group at ETH Zürich. In 2020, he joined the Departments of Computational Medicine and Mathematics at UCLA as a research scientist and fellow of the Swiss National Fund.    

Talk 3: Jacob Barhak – 20 minutes:

Topic: The Reference Model for COVID-19 – The First Multi-Scale Ensemble Disease Model
The Reference Model for disease progression was initially a diabetes model using the approach of assembling models and validating them against different populations from clinical trials. The model performed simulation at the individual level while modeling entire populations using the MIcro-Simulation Tool (MIST) that employed High Performance Computing (HPC). The Reference Model was transformed to model COVID-19 with the start of the epidemic. The model is now composed of multiple models that represent different phenomena such as models for: infectiousness, transmission, human response, and mortality. Some of those models were calculated using at different scales including cell scale, organ scale, individual scale, and population scale. The Reference Model has therefore now reached the achievement of being the first known multi-scale ensemble model for COVID-19. This project is ongoing and glimpses to future developments will be provided.

Interactive Presentation link:

Jacob Barhak Bio:
Jacob Barhak is a Sole Proprietor. He is a Computational Disease Modeler focusing on machine comprehension of clinical data. The Reference Model for disease progression is his invention that is now protected by 2 US patents. His other efforts include standardizing clinical data through and development of the Micro Simulation Tool (MIST). Dr. Barhak has a diverse international background in engineering and computing science. He is active within the python community and runs the Evening of Python Coding meetup. For additional information please visit



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