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.

Socioeconomic gradients in health are well documented in developed countries, but incompletely explained. Moreover, many environmental exposures are suspected to be health risk factors, some of them being well-established. However, research in these different domains is essentially carried out independently. Since a part of health socioeconomic gradients may be explained by environmental exposures, our objective is to explore the relationship between socio-economic position, environmental exposure and health. We chose to study a short term effect of an environmental exposure, that is, asthma attacks and atmospheric pollution in relation to socio-economic status (SES). The design of our studies is ecological and will test the associations between i) emergency visits of physicians for asthma attacks and ii) figures of respiratory drug sales and levels of atmospheric pollution in relation to SES. The setting will be the Greater Strasbourg. The statistical unit will be the smallest area (IRIS) for which socio-economic data are available through the French National Institute of Statistics (INSEE). Data on emergency visits will be obtained from the emergency and healthcare network SOS-Médecins Strasbourg and the Strasbourg SAMU emergency service (2000-2002). Data on respiratory drug sales for 2004 will be obtained from all the 3 French Medical Insurance Systems. Levels of PM10, O3, NO2, and SO2 in each IRIS will be estimated on an hourly basis from the emission inventories and measurements of the Strasbourg Air Monitoring Association (ASPA) modelled using 2 overlaid deterministic models, CHIMERE (CNRS/INERIS) and ADMS-Urban (CERC/NUMTECH). An uncertainty and sensitivity analysis of the air contaminants modelling will be carried out, allowing analyzing the effects of modelling uncertainty on observed associations. We’ll study i) the distribution of both health indicators across different socio-economic groups; ii) the distribution of ambient air pollutants concentrations across IRISes exhibiting contrasted socio economic status; iii) the modulating role of IRISes SES on the relationship between air pollution levels and asthma attacks using both case crossover and time-series approaches. Influenza epidemics, pollen release bursts as well as meteorological factors will be taken into account in the analyses. Age and sex specific analyses will be carried out as far as possible, depending on the number of health events registered.

The I-MOVE+ Consortium includes European Union (EU) Public Health Institutes, SME and Universities. It aims at measuring and comparing the effectiveness (VE) and impact (VI) of influenza and Pneumococcal vaccines and vaccination strategies a in the elderly population in Europe. The goal is to develop a sustainable platform of primary care practices, hospitals and laboratory networks that share validated methods to evaluate post marketing vaccine performances. The objectives are to identify, pilot test, and disseminate in EU the best study designs to measure, on a real time basis, VE (direct effect) and the VI of vaccination programmes (indirect and overall effect) against laboratory confirmed cases of influenza (types/subtypes) and pneumococcal disease (serotypes), and clinical outcomes. Cost effectiveness analysis will be conducted. Results will allow to understand factors affecting specific VE, the duration of protection of influenza and pneumococcal vaccines, the interaction between vaccines, the role of repeated vaccinations, the occurrence of serotype replacement (pneumococcus); identify vaccine types and brands with low VE; guide the decision of the WHO committees on vaccine strain selection (influenza); provide robust benefit indicators (VE and VI) and cost benefit and effectiveness results; guide vaccination strategies (schedules, doses, boosters). This EU member state collaboration will respond to questions that require studies based on large sample sizes and sharing of expertise that cannot be achieved by one country alone. It will allow the best methods to be used and results to benefit to all EU countries whatever their current public health achievements. Results will be shared with international partners.