[Statlist] Research Webinar in Statistics *FRIDAY, 11 DECEMBER 2020* GSEM, University of Geneva

[gsem-support-instituts]

MMN - A ENVOYER LE 07.12.2020


Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organizers :                                                                                   
E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti
S. Sperlich - F. Trojani - M.-P. Victoria-Feser


FRIDAY, 11 DECEMBER 2020 at 11:15am
ONLINE
Please join the Zoom research webinar: https://unige.zoom.us/j/99238951053?pwd=dkd5UlRlYXkvNzZicnY0UlBCeW5rdz09
Password: 419459


Understanding Spatio-Temporal Patterns of Drug Resistant Infections
Using a Mechanistic Bayesian Hierarchical Model
Tamsin E. LEE - Swiss Tropical and Public Health Institute, Basel

ABSTRACT:
Antimicrobial resistance (AMR) is the ability of an infection to stop antimicrobials, such as antibiotics, antivirals and antimalarials, from working against it. In the EU, every year AMR is responsible for thousands of deaths and costs millions of euros. Yet spatio-temporal datasets with the presence of infections with mutations associated with resistance are generally used as a monitoring tool only.

Low drug efficacy or poor adherence promotes drug resistant strains. Identifying treatment access points where resistance is more likely to occur can serve as a foundation for onward learning, and thus guide strategies that delay drug resistance, without compromising treatment availability.

We present a mechanistic model where the input data is the location of treatment access points (unchanged in time), and the presence of drug resistant infections (spatio-temporal). Our definition of a `drug resistant infection' may be interpreted in different ways, depending on the data available. For example, it may be defined as an infection with all mutations associated with drug resistance, or perhaps only one mutation.

We assume that each binary data point (absent / present) follows a Bernoulli distribution; where the probability of detecting a drug resistant infection depends on the true density of drug resistant infections at the corresponding location and time, and a sampling probability that depends on factors of the infected individual, such as age.  The true density of drug resistant infections is modelled using a partial differential equation that accounts for onwards transmission of drug resistant infections (which depends on the prevalence of the infection at the corresponding location and time), and newly drug resistant infections occurring because hosts who had a sensitive infection were treated, and subsequently developed resistance. The parameters of the model are estimated using Markov Chain Monte Carlo.

Using a toy dataset, the model recreates the true density of drug resistant infections, and identifies treatment access points that promote drug resistant infections.


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