New advances in science and medicine help us gain ground against certain infectious diseases, yet new infections continue to emerge that spread rapidly into the population and frequently reach pandemic proportions causing significant human and economic costs. Computational epidemiology, as an interdisciplinary field integrating complex systems with statistical physics approaches, computational sciences, mathematical epidemiology, Information Communication Technologies (ICT) and Geographic Information Systems, can help confronting this reality by offering new tools as important as medical, clinical, genetic or molecular diagnosis tools – namely, computational models. Massive datasets describing human activities are becoming available, thanks to pervasive new technologies leaving behind digital traces of individual behaviors. Increasingly powerful CPU capabilities allow us to store and rationalize these data, and solve sophisticate intensive algorithms to describe complex spreading processes. The product of the ICT and “Big Data” revolution has seen in this field the development of realistic computational models for the simulation of infectious disease spread, providing a synthetic framework where to conduct experiments not feasible in the real world. With less than 10 years since the first publications, models have offered an additional insight in response planning. The progress has been dramatic. As a by-product, however, such progress has also created an increased demand for quantitative, realistic, detailed and reliable data-driven computational models for the simulation of epidemic spread to guide decision-making processes. Used for the first time during an influenza pandemic event in the 2009 H1N1 case, models have indeed also uncovered their current limits. While intrinsically multi-scale and unfolding at several different spatial and temporal levels – from human-to-human transmission, to population level, space and mobility, up to the environment – infectious diseases transmission has been modeled so far by targeting specific geotemporal scales, typically treating each of them separately. Our ability to comprehensively understand the propagation and react to it is critically challenged; social and behavioral factors describing human behaviors, as well as how communities are structured and how they react to the environmental, technological, political and cultural aspects, are all layers that intrinsically interact with the biological layer of pathogen transmission, and, most importantly, with the intervention strategies put in place to control and mitigate the epidemic. Can we harmonize the multiple scales, interlinked one to each other, and intrinsically relevant for the description of the spread of infectious diseases in human population? The HarMS-flu project proposes an interdisciplinary research effort aimed at answering this question, with the potential to transform our understanding of the population-disease-environment system and our ability to plan/react/control a newly emerging pandemic. We plan to (i) collect, analyze and understand hosts’ interactions and behaviors at different scales and under different conditions (e.g. during an epidemic or in the absence of it), as well as epidemiological data; (ii) formulate theoretical approaches and develop computational frameworks for the harmonization of the different scales at play, informed by the data collected, and assess their predictive power; (iii) develop a data-driven multi-scale computational platform, integrating the data and modeling knowledge acquired in the previous directions of the project, for the simulation of an infectious disease spread and possible interventions. By creating a collaborative framework among modelers, developers, medical doctors, epidemiologists, and public health professionals, HarMS-flu will reach a today unmet modeling capability to provide informed guidelines for an influenza pandemic spreading in France.