Molecular phylogenyMolecular phylogenetics aims at reconstructing the evolutionary history of organisms from present (or recent) molecular data (mostly DNA and RNA sequences). Combined with other data such as spatial, temporal, phenotypic, etc…, these methods allow to infer about the biological processes that occurs in the past such as population dynamics, movements, etc… Applied to sequence of infectious diseases agents, this allows better understanding of the origin, the transmission intensity and the spread of infectious diseases in space and time. Combined with clinical data, phylogenies can also help understanding the determinant (pathogen vs host) of disease traits such as severity. [Read more]

(meta)genomicsMetagenomics, the study of genetic materials recovered directly from environmental samples without isolating and culturing organisms, has become one of the principal tools of “meta-omic” analysis. It can be used to explore the diversity, function, and ecology of whole microbial ecosystems. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. While traditional microbiology and and genomics rely on cultivated clonal cultures, early environmental gene sequencing cloned specific marker genes (often the 16S/18S rRNA gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial diversity had been missed by cultivation-based methods. Recent studies use either shotgun (WGS) or amplicon (16S/18S) sequencing to get largely unbiased samples from all the members of the sampled communities. Shotgun metagenomics (also known as quantitative metagenomics) is more expensive but with a much higher resolution. This course will cover all the steps from sampling to data analysis. [Read more]

Transmission dynamics: this group is about understanding infectious diseases transmission from the analysis of incidence and/or surveillance data from surveillance systems. We will explore a number of techniques of data visualization and time series analysis in order to characterize meaningful signals in the data. We will also develop mathematical and computational models of disease transmission, fit them to real data and compare different modeling techniques as a function of the type of data and the kind of question under investigation. Models will be used to test different hypotheses regarding transmission mechanism, to estimate key epidemiological parameters such as R0 and to explore optimal interventions to reduce disease incidence.  [Read more]

Epidemics forecasting: his group introduces the application of statistical and machine learning techniques to forecasting infectious diseases epidemics. The rationale is to use information on past incidence data in order to forecast future epidemics in terms of magnitude, timing and duration. This can be with or without of covariates such as demographic, immunological or climatic data. Such so-called “black-box” methods do not aim at understanding the details of diseases transmission but simply aim at forecasting future epidemics as accurately as possible by whatever means it takes. Such forecasting are crucial for deploying efficient infectious diseases prevention and control measures.  [Read more]