2pm - 3pm
Thursday 28 January 2021
Informing public health policy with mathematical models and modelling microbial communities and emergence of antimicrobial resistance
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This seminar will take place online via Zoom.
This seminar comprises of two talks: Informing public health policy with mathematical models by Dr Joaquin Prada and modelling microbial communities and emergence of antimicrobial resistance by Dr Gianni Lo Iacono.
Dr Joaquin Prada
Informing public health policy with mathematical models
About the talk
Long before COVID brought mathematical models to the forefront of news sites worldwide, modelling was used extensively to inform policy for animal and human health. In this talk, I will cover how we can better inform sustainable policies using mathematical modelling, with examples on zoonotic diseases from my research group, such as Echinococcosis and Rabies.
Dr Giovanni Lo Iacono
Modelling microbial communities and emergence of antimicrobial resistance
About the talk
How does an external perturbation, such as administration of antibiotics, affect the dynamics of a microbial community? For instance, is the gut microbiome a resilient community and does bounce back to the original configuration when the perturbation stops? Is it possible to delay/accelerate the evolution of a pathogen by adequately perturbing the system?
I will present findings from an old work and something new related to the above questions. In particular, we used a mathematical model to show that in general large stochastic fluctuations in epidemics enhance extinction of the pathogen, especially of the emerging mutant strains. We know that periodically forced epidemics oscillate at larger amplitude at some frequencies than at others (resonance), then by adequately perturbing the system (e.g. by alternating different types of fungicides for plant disease or drugs in a pharmaceutical context) we can cause massive fluctuations in the small pathogen population increasing the chances of extinction.
If such hypotheses will be experimentally confirmed, we could exploit the source of stochasticity to alleviate the disease, reduce chemical control or drugs, and in general, mitigate the risk of developing highly harmful pathogens (e.g. superbugs insensitive to antibiotics).