Using models to improve our understanding and control of pathogens transmission in health care settings
Health care institutions are frequently exposed to local outbreaks of pathogens, e.g. Influenza, antibiotic-resistant bacteria or more recently SARS-CoV-2. When they occur, such outbreaks require reactive control measures implementation, generating important disorganisation and costs. Designing efficient control measures is complex and requires a good understanding of the forces at the origin of pathogens transmissions in health care populations. In this context, hypothesis driven mathematical models can help analyse epidemics, estimate unknown parameters and simulate counterfactual scenarios to help implementing optimized control measure.
In this talk Lula will present specific modelling frameworks aimed at analysing the spread of antibiotic-resistant bacteria in health care settings to investigate the role of between-human contacts and drug exposure; and their extension to support the recent challenges faced by hospitals in the context of the COVID-19 epidemics.
About the speaker
Lulla Opatowski is professor of mathematical epidemiology at the University of Versailles Saint Quentin in Paris, France. She works in the “Epidemiology, Modelling of bacterial Escape to Antimicrobials” research group at Institut Pasteur and the “Centre for Epidemiology and Population Health” research group (Inserm/UVSQ).
Her main research interests are in mathematical and computational modelling of infectious disease and statistical inference, with a particular interest in bacterial resistance to antibiotics and host-pathogen-pathogen interactions. Her projects include the modelling of interactions between respiratory bacteria and respiratory viruses in humans, the modelling of transmission of nosocomial pathogens in hospitals using detailed contact data, the modelling of gut microbiota and the impact of its dynamics on the acquisition of pathogenic or antibiotic resistant bacteria.
Please note that the time listed is Greenwich Mean Time (GMT)
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