Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.

Research areas

Research areas

Systems Biology
Statistical genomics and Epidemiology
Medical Imaging
Precision and Stratified Medicine
Analysis of clinical trials, observational and longitudinal studies
Infectious Disease Epidemiology

Publications

Citation

BibTex format

@article{Myall:2021:10.1007/s41109-021-00376-5,
author = {Myall, AC and Peach, RL and Weiße, AY and Davies, F and Mookerjee, S and Holmes, A and Barahona, M},
doi = {10.1007/s41109-021-00376-5},
journal = {Applied Network Science},
title = {Network memory in the movement of hospital patients carrying drug-resistant bacteria},
url = {http://dx.doi.org/10.1007/s41109-021-00376-5},
volume = {6},
year = {2021}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.
AU - Myall,AC
AU - Peach,RL
AU - Weiße,AY
AU - Davies,F
AU - Mookerjee,S
AU - Holmes,A
AU - Barahona,M
DO - 10.1007/s41109-021-00376-5
PY - 2021///
SN - 2364-8228
TI - Network memory in the movement of hospital patients carrying drug-resistant bacteria
T2 - Applied Network Science
UR - http://dx.doi.org/10.1007/s41109-021-00376-5
UR - http://arxiv.org/abs/2009.14480v2
VL - 6
ER -

Contact us

If you are interested in meeting with members of the group please contact Marina Evangelou