Volatile Multiple Smart Systems Network

Anne Håkansson


Cyber-physical systems, CPS, like self-driving vehicles, e.g., cars, drones, and water vessels, are common in society, today. AI is used to enable the CPS to autonomously carry out tasks in society. But fully autonomous CPS can currently not be accepted in heavy traffic and dynamic environments. In this talk Professor Anne Håkansson describes Volatile Multiple Smart Systems Network and how this kind of network can be applied to deploy fully autonomous CPS in society.  


Anne Håkansson is a professor in computer science with a focus on Artificial Intelligence, UiT The Arctic University of Norway. She is a Docent in computer science at KTH Royal Institute of Technology Sweden.   
Professor Håkansson has more than 25 years of experience in the AI field, giving her broad knowledge in several AI areas. Her research interests lie in decision-making and reasoning strategies and visualizing and explanation, in particular, negotiation between systems and agents. She has worked with KBS, meta-agents in MAS, and is currently working with CPS in the transportation sector, Industry 4.0, and Smart Cities and Societies with automated smart products and services.    
Anne Håkansson is the chair of the Computational Analytics and Intelligence (CAI) research lab at UiT, Tromsø which includes several different groups as UiT, Norway. She is also the chair of Senseable Stockholm Lab, KTH, Kista. She is the author and co-author of several books, proceedings, and international publications and serves in technical program committees of international top conferences IJCAI and AAAI as well as general track chair for KES international conferences. She is the editor-in-chief of NORA Machine Intelligence Journal, Norway. She is a frequently engaged speaker for Artificial Intelligence and popular science and has been an ambassador for Uppsala municipality, Sweden. 

Complex systems and Epidemic modeling

Jacques Demongeot


Epidemic modeling and more, pandemic modeling, involves a mathematical approach using complex systems architectures at two levels: i) for representing the contagion process between susceptible individuals and ii) for simulating the efficiency of mitigating and/or curative measures based on prevention, vaccination and therapy policies. For the first level, we will take as example the COVID-19 outbreak and for the second the obesity spread, both declared worldwide pandemic by the World Health Organization (WHO). 


1. J. Gaudart, J. Landier, L. Huiart, E. Legendre, L. Lehot, M.K. Bendiane, L. Chiche, A. Petitjean, E. Mosnier, F. Kirakoya-Samadoulougou, J. Demongeot, R. Piarroux & S. Rebaudet 
Factors associated with spatial heterogeneity of Covid-19 in France: a nationwide ecological study.  
The Lancet Public Health, 6, e222-e231 (2021). 

2. J. Demongeot, K. Oshinubi, M. Rachdi, H. Seligmann, F. Thuderoz & J. Waku 
Estimation of Daily Reproduction Rates in COVID-19 Outbreak. 
Computation, 9, 109 (2021).

3. K. Oshinubi, S. BuHamra, N. Alkandari, J. Waku, M. Rachdi & J. Demongeot 
Age Dependent Epidemic Modelling of COVID-19 Outbreak in Kuwait, France and Cameroon. 
Healthcare, 10, 482 (2022). 

4. Z. Xu, D. Yang, L. Wang & J. Demongeot 
Statistical Analysis Supports UTR (untranslated region) Deletion Theory in SARS-CoV-2. 
Virulence, 13, 1772-1789 (2022). 

5. J. Demongeot & C. Fougere 
mRNA vaccines – Facts and hypotheses on fragmentation and encapsulation. 
Vaccines, 11, 40 (2022). 

6. M. Jelassi, K. Oshinubi, M. Rachdi & J. Demongeot 
Epidemic Dynamics on Social Interaction Networks. 
AIMS Bioengineering, 9, 348-361 (2022). 

 7. J. Demongeot, Q. Griette, Y. Maday & P. Magal 
A Kermack-McKendrick model with age of infection starting from a single or multiple cohorts of infected patients. 
Proc. Royal Society A, 479, 2022.0381 (2023). 

8. B. Kammegne, K. Oshinubi, T. Babasola, O.J. Peter, O.B. Longe, R.B. Ogunrinde, E.O. Titiloye & J. Demongeot 
Mathematical modelling of spatial distribution of COVID-19 outbreak using diffusion equation. 
Pathogens, 12, 88 (2023). 

9. Z. Xu, D. Wei, Q. Zeng, H. Zhang, Y. Sun & J. Demongeot  
More or less deadly? A mathematical model that predicts 1 SARS-CoV-2 evolutionary direction. 
Computers in Biology & Medicine, 153, 106510 (2023). 

10. J. Demongeot & P. Magal 
Data Driven Modeling in Mathematical Biology.   
Frontiers in Applied Maths & Statistics, 9, 1129749 (2023). 

11. Z. Xu, D. Yang & J. Demongeot 
A Deterministic Agent-based Model with Antibody Dynamics Information in COVID-19 Epidemic Simulation. 
Frontiers in Public Health (submitted). 


Jacques Demongeot, MD and PhD in Mathematics, is Professor Emeritus at Université Grenoble Alpes (UGA), Honorary Member of the Institut Universitaire de France (IUF), Member of the International Academy of Medical and Biological Engineering (IAMBE) and of the I@nternational Organization called the Third Way of Evolution ( and foreign member of the Chilean Academy of Sciences. He belongs since 2016 to the AGEIS UGA laboratory, dedicated to gerontechnology and e-health.  In 1980, he founded the team Treatment of Information and Modeling in Biomedicine (TIMB) and in 1987 the CNRS laboratory (Techniques of Imaging, Modeling and Complexity (TIMC), inside the institute Informatics, Mathematics and Applications of Grenoble (IMAG), which he directed until 2011. In 1985, he founded at the Grenoble University Hospital the Medical Informatics Service, enlarged in 2003 to the Public Health Pole, which he directed until 2012. In 1992, he founded the Doctoral School of Engineering for Health, Cognition and Environment (EDISCE), which he directed until 2003. He was responsible from 1997 to 2000 of the Bureau Technologies for Health at the Ministry of Research, then from 2002 to 2007 of the CNRS Complex Systems Program. He has published more than 500 papers (, which are  focused on biomathematics, bioinformatics and medical engineering.