Modelling smoking prevalence with supercomputers

Complex systems are an interdisciplinary subject of research and are used as the theoretical framework in a wide range of experimental fields such as economic and medical disciplines, as well as in mathematical and computer science studies.

A complex system is composed by a large number of elements that interact between each other.

The microscopic interaction among the elements gives rise to a global behavior of the system that can be very different, and at times even counterintuitive, with respect to the evolution rules of the single parts.

Leveraging on the complex systems approach, the ISI Foundation has developed a model that reproduces the adoption of and quit from the smoking habit in a population.

The model falls within the field of Global System Science (GSS) since it describes a large scale social system (in this case a whole country) to simulate its evolution in time.

Global Systems Science is a new area of research that is a generalization of Systems Science and that has an impact on several disciplines. It requires a realistic and accurate modelling of the systems under investigation and large computational capacity to simulate their evolution.

Examples of Global Systems Science are the study of climate changes, financial stability and the description of new technologies adoption.

The simulations performed at the ISI Foundation enable the study of the process of adopting and giving up the smoking habit in Great Britain and have been performed using a High Performance Computing (HPC) infrastructure. In particular, the HPC supercomputers called Hazelhen at the HLRS in Stuttgart (Germany) and the Eagle at the University of Poznan (Poland) have been used to run the simulations.

The system description relies on an agent-based-model (ABM), i.e., each person in the population is represented as a computer variable and the behavior of each single element / agent in the system is simulated accordingly to the proposed model.

With this type of modelling approach, it is possible to reproduce the macroscopic evolution of the system by simulating the microscopic evolution of every single element.

The ABM model is a modelling framework used in game theory, in computational sociology and in complex systems.

The model accounts for socio-economic indicators that measure the possibility to access tobacco products, thus simulating in a realistic way the behavior of the population of the country. In fact, the consumption of tobacco depends on several diverse factors such as social condition, age, economic factors etc. [ref. Psicol Soc (Bologna). Author manuscript; available in PMC 2014 Aprs 22. Italian.Published in final edited form as: Psicol Soc (Bologna). 2012; 2012(1): 7.30. Published online 2012. doi: 10.1482/36754 and others].

The developed ABM model and the behavior of the population reproduced by the simulations have been validated against data of smoking prevalence in Great Britain from 1976 to 2016 (data from the National Health Service and the Office for National Statistics), showing a very good agreement between the empirical data and the simulations’ results.

The validation procedure against the statistical data has been carried out performing hundreds of simulations, with thousands of variables, on the HPC infrastructure.

After the validation phase, the resulting model closely reproduces the historical series of data, thus providing a prediction of the evolution of the smoking habit in Great Britain for the next years. Moreover, the model is able to forecast the smoking prevalence in a country based on the expected effectiveness of different policies regulating the tobacco consumption put in place by the government. Specifically, the smoking prevalence in Great Britain will have a decreasing trend in the next years. This trend can be either speeded up or slowed down by government interventions on the tobacco price or accessibility.

Authors:
Enrico Ubaldi — ISI Foundation, Torino
Mario Scovazzi — CSP Innovazione nelle ICT, Torino

Computing Power for Global Systems | October 24-25, 2017

Following up on the ICSP, IMT School For Advanced Studies Lucca and the CoeGSS Consortium invite GSS community and HPC scientists to contribute to the ongoing discussion on computational modelling for addressing Global Challenges.
#CPGConference will be held on October 24-25, 2017 in Lucca, Italy

High Performance Computing meets Global Systems Science

After the International Conference on Synthetic Populations in Lucca, on these pages we’d like to open a confrontation on the societal impact of High Performance Computing and Global Systems Science.
In the beautiful location of IMT School, Carlo Jaeger, Potsdam University, and Bastian Koller, High Performance Computing Center Stuttgart (HLRS), offered us interesting ideas to think about.

We do not really understand the Global Systems. We have made advances in understanding complex systems at all scales, from molecules all the way up to galaxies. Global Systems have properties of themselves, they are very important for us to understand, it’s urgent and it is scientifically very interesting.

Carlo Jaeger told us, then added: Studying this kind of systems, that’s what we are engaged in.

CoeGSS is an opportunity to bring together people studying Global Systems with people working with extremely powerful computers

“High Performance Computing gives us the potential to go beyond the national limitations of experiments, tests and predictions, we can now simulate things which are too dangerous to do” added Bastian Koller.
If you use HPC, you can make simulations much wider and globally, so you can talk about countries, country borders, you can talk about the distribution of people, the different attitudes, the different behaviours.

The more parameters you can feed into the system the more calculations you can do and with HPC we have the potential to evolve this step by step, to bring in all these parameters and calculate it in a reasonable time frame