The Pasteur Museum is housed in the apartment where Louis Pasteur spent his final seven years and offers a rare behind-the-scenes look at the living and working environment of the world-renowned scientist. Visitors can gain a unique insight into his everyday life alongside his wife and can admire his rich and diverse scientific work.
The Institut Pasteur’s scientific strategy focuses on developing original and innovative topics and promoting interdisciplinary and multidisciplinary cooperation and approaches. The Institut Pasteur teams have access to the technological resources needed to speed up and further improve the quality of their outstanding research.
Ever since the introduction of the world’s first "Technical Microbiology" course in 1889, teaching has been a priority for the Institut Pasteur. The Institut Pasteur has an international reputation for quality teaching that attracts students from all over the world who come to further their training or top up their degree programs.
The mission of the Industrial Partnership team is to detect, promote, assist and protect the inventive activities from research (inventions, know-how and biological materials) conducted at the Institut Pasteur (and in some Institutes of its international network), and transfer there to industrial and/or institutional partners, in order to serve the patient needs and for the benefit of the society, as well as to contribute to sustainability of the Institut Pasteur’s resources.
With international courses, PhD and postdoctoral traineeship, each institute of the Institut Pasteur International Network (RIIP) contributes to the transmission of knowledge with the training of young researchers all around the world. In this context, doctoral and postdoctoral programmes, study and traineeship fellowships are available to scientists. Alongside training, dynamism and attractiveness of RIIP will result in the creation of 4-year group for the young researchers.
Statistical and Mathematical Modeling in Biological Applications
The seminar series Statistical and Mathematical Modeling in Biological Applications (SaMMBA) features monthly lectures and discussions with leading modelers in biological sciences. Presentations are in English or French and target an interdisciplinary audience. Seminars are held on the campus of Institut Pasteur. Access is free. However, to enter the campus, one needs a badge that is obtained at the main gate in exchange of a piece of ID (passport, citizen card or driver licence).
Contact Network Epidemiology: Effective Disease Spread Control from Hospitals to Urban Areas
Abstract: Two crucial elements facilitate the understanding and control of communicable disease spread within a social setting. These components are the underlying contact structure among individuals that determine the pattern of disease transmission; and the evolution of this pattern over time. Mathematical models of infectious diseases, which are in principle analytically tractable, have taken two general approaches in incorporating these elements. The first approach, generally known as compartmental modeling, addresses the time evolution of disease spread at the expense of simplifying the pattern of transmission. On the other hand, the second approach uses contact networks to incorporate detailed information pertaining to the underlying contact structure among individuals. However, due to lack of cohesive analytical frameworks to address time evolution on contact networks without simplifying approximations, the only alternative to integrate both aspects of disease spread simultaneously has been to abandon the analytical approach and rely on computer simulations. Recent developments in contact network epidemiology have enabled us to move towards overcoming these limitations and address challenging issues of public health import. These issues may include disease spread containment in finite size settings (be it a hospital setting, or an urban area), or early estimation of key epidemiological parameters during an emerging infectious disease outbreak, when the pattern of disease spread is predominantly influenced by the probabilistic nature of infection transmission, and the only available information during this stage is the number of newly reported cases. We present network-based approaches that can be utilized to achieve these goals, thereby constituting the basis of novel decision-support tools to inform public health policy.