Publications by authors named "Christopher Overton"

Objectives: Influenza-like-illness (ILI) is a commonly used symptom categorization in seasonal disease surveillance focusing on influenza in community and clinical settings. However, SARS-CoV-2 often causes presentation with a similar symptom profile. We explore how SARS-CoV-2-positive individuals can influence surveillance trends for the World Health Organization, the United States Centre for Disease Control, and the European Centre for Disease Control (ECDC) ILI criteria.

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Article Synopsis
  • Epidemiological delays are crucial for shaping public health policies and informing clinical practices, often used in models to develop control strategies.
  • Recent findings indicate that common errors in estimating these delays, such as censoring and dynamical bias, can significantly impact various applications.
  • The text presents best practices and a flowchart to help practitioners better estimate and report these delays, particularly focusing on the incubation period and serial interval, which are vital for managing outbreaks.
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During the SARS-CoV-2 pandemic, polymerase chain reaction (PCR) and lateral flow device (LFD) tests were frequently deployed to detect the presence of SARS-CoV-2. Many of these tests were singleplex, and only tested for the presence of a single pathogen. Multiplex tests can test for the presence of several pathogens using only a single swab, which can allow for: surveillance of more pathogens, targeting of antiviral interventions, a reduced burden of testing, and lower costs.

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Background: We quantified SARS-CoV-2 dynamics in different community settings and the direct and indirect effect of the BNT162b2 mRNA vaccine in Monaco for different variants of concern (VOC).

Methods: Between July 2021 and September 2022, we prospectively investigated 20,443 contacts from 6320 index cases using data from the Monaco COVID-19 Public Health Programme. We calculated secondary attack rates (SARs) in households (n = 13,877), schools (n = 2508) and occupational (n = 6499) settings.

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The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.

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Article Synopsis
  • - The COVID-19 pandemic has put immense pressure on healthcare workers and experts, leading to serious psychological issues like stress, burnout, and moral injury, which affect their wellbeing and productivity.
  • - A workshop was held with diverse professionals to reflect on the personal and professional impacts of pandemic response work, leading to collective recommendations for improving future epidemics responses.
  • - Key concerns identified included inadequate institutional support and mental health challenges; recommendations focused on enhancing collaboration, recognition, and sustainable practices within public health work.
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The basic reproduction number, , is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not.

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  • * During a study from June to September 2022, 3,375 confirmed monkeypox cases were analyzed, revealing that the average time from infection to hospital admission was about 15 days and the average hospital stay was a bit over 7 days.
  • * The study estimated the Infection Hospitalisation Risk at 4.13%, with a higher Case Hospitalisation Risk of 17.86% for females compared to 4.99% for males, suggesting potential differences in infection severity between sexes.
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Objectives: To identify risk factors that contribute to outbreaks of COVID-19 in the workplace and quantify their effect on outbreak risk.

Methods: We identified outbreaks of COVID-19 cases in the workplace and investigated the characteristics of the individuals, the workplaces, the areas they work and the mode of commute to work, through data linkages based on Middle Layer Super Output Areas in England between 20 June 2021 and 20 February 2022. We estimated population-level associations between potential risk factors and workplace outbreaks, adjusting for plausible confounders identified using a directed acyclic graph.

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Background: Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

Methods: We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England.

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In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model.

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In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men.

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Article Synopsis
  • The study analyzed how various leading indicators, like Google Trends and NHS triage calls, can predict hospital admissions related to COVID-19 in England.
  • It found that Google Trends and NHS triage calls consistently showed a time lag of 5 to 20 days before influencing admissions, while other indicators like the ZOE app had inconsistent relationships.
  • The research highlights the potential of using novel data sources for public health planning, but emphasizes that the variability of these indicators across different locations affects their effectiveness.
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The reproduction number has been a central metric of the COVID-19 pandemic response, published weekly by the UK government and regularly reported in the media. Here, we provide a formal definition and discuss the advantages and most common misconceptions around this quantity. We consider the intuition behind different formulations of , the complexities in its estimation (including the unavoidable lags involved), and its value compared to other indicators (e.

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New SARS-CoV-2 variants causing COVID-19 are a major risk to public health worldwide due to the potential for phenotypic change and increases in pathogenicity, transmissibility and/or vaccine escape. Recognising signatures of new variants in terms of replacing growth and severity are key to informing the public health response. To assess this, we aimed to investigate key time periods in the course of infection, hospitalisation and death, by variant.

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Objective: To analyse the transmission dynamics of the monkeypox outbreak in the UK, declared a Public Health Emergency of International Concern in July 2022.

Design: Contact tracing study, linking data on case-contact pairs and on probable exposure dates.

Setting: Case questionnaires from the UK Health Security Agency (UKHSA), United Kingdom.

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The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool.

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The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.

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When comparing the risk of a post-infection binary outcome, for example, hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection. Typically, the infection time is unknown and positive test time used as a proxy for it. Positive test time may also be used when assessing how risk of the outcome changes over calendar time.

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The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.

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Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks.

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The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement.

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This study aimed to address the significant problems of bacterial biofilms found in medical fields and many industries. It explores the potential of classic photoactive carbon dots (CDots), with 2,2'-(ethylenedioxy)bis (ethylamine) (EDA) for dot surface functionalization (thus, EDA-CDots) for their inhibitory effect on biofilm formation and the inactivation of cells within established biofilm. The EDA-CDots were synthesized by chemical functionalization of selected small carbon nanoparticles with EDA molecules in amidation reactions.

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