Investigating the feasibility and potential of combining industry AMR monitoring systems: a comparison with WHO GLASS.

Background: Efforts to estimate the global burden of antimicrobial resistance (AMR) have highlighted gaps in existing surveillance systems. Data gathered from hospital networks globally by pharmaceutical industries to monitor antibiotic efficacy in different bacteria represent an underused source of information to complete our knowledge of AMR burden..

Using contact network dynamics to implement efficient interventions against pathogen spread in hospital settings: A modelling study.

Background: Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation.

An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.

Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics.

Quantifying patient- and hospital-level antimicrobial resistance dynamics in from routinely collected data.

Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen . The reported prevalence of these resistances varies, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures.

Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic.

Background: During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences.

Methods: A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study.

Quantifying patient- and hospital-level antimicrobial resistance dynamics in from routinely collected data.

Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures.

Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: Killers and drivers of resistance evolution.

Bacteriophage (phage) are bacterial predators that can also spread antimicrobial resistance (AMR) genes between bacteria by generalised transduction. Phage are often present alongside antibiotics in the environment, yet evidence of their joint killing effect on bacteria is conflicted, and the dynamics of transduction in such systems are unknown. Here, we combine in vitro data and mathematical modelling to identify conditions where phage and antibiotics act in synergy to remove bacteria or drive AMR evolution.

Growth-Dependent Predation and Generalized Transduction of Antimicrobial Resistance by Bacteriophage.

Bacteriophage (phage) are both predators and evolutionary drivers for bacteria, notably contributing to the spread of antimicrobial resistance (AMR) genes by generalized transduction. Our current understanding of this complex relationship is limited. We used an interdisciplinary approach to quantify how these interacting dynamics can lead to the evolution of multidrug-resistant bacteria.

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence.

Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks.

Contact tracing has the potential to control outbreaks without the need for stringent physical distancing policies, e.g. civil lockdowns.

Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England.

The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data.

Antimicrobial resistance and COVID-19: Intersections and implications.

Before the coronavirus 2019 (COVID-19) pandemic began, antimicrobial resistance (AMR) was among the top priorities for global public health. Already a complex challenge, AMR now needs to be addressed in a changing healthcare landscape. Here, we analyse how changes due to COVID-19 in terms of antimicrobial usage, infection prevention, and health systems affect the emergence, transmission, and burden of AMR.

Comparing the Analysis and Results of a Modified Social Accounting Matrix Framework with Conventional Methods of Reporting Indirect Non-Medical Costs.

Background: Assessing the societal perspective in economic evaluations of new interventions requires estimates of indirect non-medical costs caused by the disease. Different methods exist for measuring the labor input function as a surrogate for these costs. They rarely specify the effect of health on labor and who gains and who loses money.

What settings have been linked to SARS-CoV-2 transmission clusters?

: Concern about the health impact of novel coronavirus SARS-CoV-2 has resulted in widespread enforced reductions in people's movement ("lockdowns"). However, there are increasing concerns about the severe economic and wider societal consequences of these measures. Some countries have begun to lift some of the rules on physical distancing in a stepwise manner, with differences in what these "exit strategies" entail and their timeframes.

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets.

Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level.

Morbidity, perioperative outcomes and complications of robot-assisted radical prostatectomy in kidney transplant patients: A French multicentre study.

Objective: Evaluate the safety, feasibility and efficiency of robot-assisted radical prostatectomy (RARP) in kidney transplant recipients, performed in high-volume French referral centres, and describe intra- and postoperative, oncological and functional outcomes.

Materials And Methods: A multicentre study was conducted on prospective RARP databases from 5 centres between 2008 and 2017. We retrospectively identified a first group (G1) of transplant patients.

Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations.

Antimicrobial resistance (AMR) is one of the greatest public health challenges we are currently facing. To develop effective interventions against this, it is essential to understand the processes behind the spread of AMR. These are partly dependent on the dynamics of horizontal transfer of resistance genes between bacteria, which can occur by conjugation (direct contact), transformation (uptake from the environment) or transduction (mediated by bacteriophages).