The Viroscopy project essentially aims at developing stochastic
mathematical models for the spread of transmissible infectious diseases,
together with dedicated statistical methodologies, with the intent to
deliver efficient diagnostic/prediction tools for epidemiologists.
In our
era of massive automatic data collection, many measurements related to the
spread of infections are now systematically obtained and gathered in
databases, without necessarily knowing which ones will be relevant for
describing/understanding/predicting the epidemic of interest. The enormous
progress made in the last ten years for gathering such data encourages
applied mathematicians and epidemiologists to develop new models,
incorporating more features in order to account for real-life situations.
Although many variants of the standard SIR model have been proposed in the
Biostatistics literature during the last two decades (far too numerous for
being listed here), the question of analyzing epidemic data in its whole
complexity remains a very challenging task. Recent advances in Probability
& Statistics suggest that new analytical and computational tools, based on
computer-intensive simulation methods interpretable in terms of
interacting particle systems for instance, may be implemented from
epidemic data in order to produce useful estimates for epidemiologists and
the public-health community. The design of mathematical methodologies for
analyzing epidemic data and tackling important questions related to
epidemiological modelling being at the center of the present research
project, the latter is clearly involved in interdisciplinary fundamental
research. Beyond expected wide-reaching results (guaranteeing a validity
framework for the models considered and convergence of numerical
procedures elaborated), the Viroscopy project members are also committed
to confront theory with data.