Quentin J. Leclerc's research while affiliated with Conservatoire National des Arts et Métiers and other places

... Our work represents an EHR-based method and shows the value of the inclusion of such a dataset for inferring population-level trends in infectious diseases. The use of such datasets remains rare [15], although they have been showcased primarily with data from hospitals in the UK [43,44]. We showed how such databases can provide an understanding of trends in antibiotic prescribing and bacterial infections at a population level. ...

... Transfer of pathogenic agents can occur through contact with fomites, vectors (e.g., insects or animals), airborne aerosols, droplets, or direct physical contact [1]. Notably, viral respiratory infections, primarily transmitted during social interactions via droplets, aerosols and physical contact continue to be major contributors to global morbidity and mortality [2]. Recognising the significance of information on close interactions is pivotal for elucidating disease transmission [3]; thus, social contact data is often employed as a valuable proxy for identifying transmission routes. ...

... Therefore, an interdisciplinary approach, combining experiments and simulations, was adopted to predict how phage-bacteria dynamics lead to the evolution of multidrug-resistant bacteria. The estimated transduction frequency was approximately 10 −8 (Leclerc et al., 2023). Based on this, the model was further extended to simulate the impact of antibiotics on the phage-bacteria system under the same environmental conditions (Leclerc et al., 2022). ...

... The natural environment of the perhaps somewhat onesided but still symbiotic relation between phage and bacteria [57] is not the well-stirred chemostat of the microbiology laboratory [58]. Spatial and temporal inhomogeneities in the distribution of stress factors (selection pressure) have a profound impact on evolutionary processes, changing population numbers and the invasion of resistant strains into sensitive ones. ...

... How good at forecasting mechanistic models should be is unclear. If we continue to consider SARS-CoV-2 (Knock et al, 2021; Barnard et al, 2022;Keeling et al, 2022), it is not immediately clear whether a mechanistic model being good at forecasting 2 wk into the future tells us anything about its accuracy when predicting the effect of counterfactual scenarios regarding, e. g., lockdowns. If the model produces a predicted pandemic trajectory for a counterfactual scenario where the imagined conditions of this scenario are very similar to what in fact occurred, then it seems reasonable to retrospectively treat the trajectory as a forecast and compare the output to what was observed in reality. ...

... The bacteriophage interference carries the risk of inducing the evolution of multiple antibiotic-resistant bacteria, and its potential impact on ARG diffusion in microcosms may be underestimated. After 7 h of incubation with S. aureus carrying different ARGs in an identical environment, bacteriophage transduction contributes to the generation of bi-resistant bacteria, with one transducing bacteriophage carrying an ARG among 10 8 newly generated bacteriophages [79]. It has been suggested that ARGs are rare in bacteriophages but widely present in phage-plasmids (P-Ps, nucleic acid sequences of lysogenic bacteriophages not embedded in the host genome that replicate and translocate independently) [80]. ...

... This may indicate varying rates of gain and loss for different genes or fitness effects such as a high cost of resistance (e.g. for rifampicin [51]). The major mechanism for horizontal gene transfer in S. aureus is transduction by bacteriophage, with phage capable of transduction found in at least 50% of S. aureus within-host populations [10,[52][53][54]. Previous work suggested that different genes may not be transferred at the same rate by this process, which may explain the variations we have seen here [32,54]. ...

... To model bed occupancy, data as patient flux, Length of Stay (LoS), and the type of bed (ward or ICU) were taken into account [39]. Other studies, as the work of Leclerc et al., use patient bed pathways (sequence of transfers of individual patients between bed types during a hospital stay) as a variable [40]. ...

... Mathematical models of disease transmission and contact tracing processes have previously been used to quantify the impact of different contact tracing strategies on transmission of various pathogens [5][6][7][8]. While many studies have explored the impact of delays at various steps of the case and contact management process [8][9][10][11], case interview prioritisation strategies have not been investigated. Furthermore, very few modelling studies of contact tracing have considered the limited capacity of the public health system [7,12]. ...

... Generally, one in five of COVID-19 cases requires hospitalization and up to 16% of all hospitalized patients need ICU admission. [3,4] However, the hospital's in-patient care capacity is limited and exceeding healthcare capacity can lead to reduced quality of care and increased mortality. [5,6] This has raised serious concerns about the potential impact on health systems in low-and middle-income countries (LMICs) where some healthcare approaches were about to collapse in response to the COVID-19 pandemic. ...