The Annual General Meeting will be followed by two talks, the first talk will be given by Alicia Gill and the second one by Kristian Romano.
The Annual General Meeting of the RSS WMLG will take place on place on 5th
December at 17:30 in the Mathematical Science Building of the University of Warwick, room MB0.07. The Annual General Meeting will be followed by two talks. Light refreshments will be available after the talks.
The first talk will be given by Alicia Gill (University of Warwick)
Inferring reproduction number from epidemic and genomic data
Inference of the reproduction number through time is of vital importance during an epidemic outbreak. Typically, epidemiologists tackle this using observed prevalence or incidence data. However, prevalence or incidence data alone are often noisy or partial. Models can also have identifiability issues with determining whether a large amount of a small epidemic or a small amount of a large epidemic has been observed. Sequencing data however is becoming more abundant, so approaches which can incorporate genomic data are an active area of research. In this talk, I will discuss how to extract information about disease outbreaks out of genomic trees and then present a Bayesian approach to incorporate this information into models.
The second talk will be given by Kristian Romano (University of Warwick)
Real Time Telemetric Monitoring of the Circadian Rhythm via a Wearable Device for Advanced Pancreatic Cancer Patients undergoing Chemotherapy
Disruptions of the Circadian Timing System in cancer patients are associated with poorer treatment outcomes, and short progression-free and overall-survival. The MultiDom clinical trial (NCT04263948) telemonitors patients with advanced or metastatic pancreatic adenocarcinoma undergoing standard mFOLFIRINOX chemotherapy aiming to reduce the rate of patients undergoing toxicity-related emergency admissions (Bouchahda et al. BMJ Open. 2023 Jun 7;13(6):e069973). The use of telecommunicating wearable devices in the trial provides a continuous flow of physical activity and body temperature data that allows for near-real time analyses and estimation of circadian parameters dynamics to inform proactive decision making by the medical team. Here, we develop a new methodology, based on the Hidden Markov Model, that allows for the computation - in real time - of circadian parameters from actimetry that are of interest to daily remote monitoring. To the best of our knowledge this is also the first method which also provides a systematic quantification of circadian parameters uncertainty. We will present some profiles of circadian parameters as they evolve over 2 months, while the patient is undergoing several chemotherapeutic treatment courses, thus highlighting the potential of our new algorithms for circadian digital and precision medicine.