Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak

In the event of a disease outbreak emergency, such as COVID-19, the ability to construct detailed stochastic models of infection spread is key to determining crucial policy-relevant metrics such as the reproduction number, true prevalence of infection, and the contribution of population characteristics to transmission. In particular, the interaction between space and human mobility is key to prioritising outbreak control resources to appropriate areas of the country. Model-based epidemiological intelligence must therefore be provided in a timely fashion so that resources can be adapted to a changing disease landscape quickly. The utility of these models is reliant on fast and accurate parameter inference, with the ability to account for large amount of censored data to ensure estimation is unbiased. Yet methods to fit detailed spatial epidemic models to national-level population sizes currently do not exist due to the difficulty of marginalising over the censored data. In this paper we develop a Bayesian data-augmentation method which operates on a stochastic spatial metapopulation SEIR state-transition model, using model-constrained Metropolis-Hastings samplers to improve the efficiency of an MCMC algorithm. Coupling this method with state-of-the-art GPU acceleration enabled us to provide nightly analyses of the UK COVID-19 outbreak, with timely information made available for disease nowcasting and forecasting purposes

Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39–4.13), indicating that 58–76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6–7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090–33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’

Anticipating future learning affects current control decisions : a comparison between passive and active adaptive management in an epidemiological setting

Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic

The path to detecting extraterrestrial life with astrophotonics

Astrophysical research into exoplanets has delivered thousands of confirmed planets orbiting distant stars. These planets span a wide ranges of size and composition, with diversity also being the hallmark of system configurations, the great majority of which do not resemble our own solar system. Unfortunately, only a handful of the known planets have been characterized spectroscopically thus far, leaving a gaping void in our understanding of planetary formation processes and planetary types. To make progress, astronomers studying exoplanets will need new and innovative technical solutions. Astrophotonics -- an emerging field focused on the application of photonic technologies to observational astronomy -- provides one promising avenue forward. In this paper we discuss various astrophotonic technologies that could aid in the detection and subsequent characterization of planets and in particular themes leading towards the detection of extraterrestrial life.Comment: 9 pages, 2 figures, SPIE Optics and Photonics conferenc

Synergistic interventions to control COVID-19 : mass testing and isolation mitigates reliance on distancing

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies

Risk behaviors in a rural community with a known point-source exposure to chronic wasting disease

<p>Abstract</p> <p>Background</p> <p>The emergence and continuing spread of Chronic Wasting Disease (CWD) in cervids has now reached 14 U.S. states, two Canadian provinces, and South Korea, producing a potential for transmission of CWD prions to humans and other animals globally. In 2005, CWD spread for the first time from the Midwest to more densely populated regions of the East Coast. As a result, a large cohort of individuals attending a wild game feast in upstate New York were exposed to a deer that was subsequently confirmed positive for CWD.</p> <p>Methods</p> <p>Eighty-one participants who ingested or otherwise were exposed to a deer with chronic wasting disease at a local New York State sportsman's feast were recruited for this study. Participants were administered an exposure questionnaire and agreed to follow-up health evaluations longitudinally over the next six years.</p> <p>Results</p> <p>Our results indicate two types of risks for those who attended the feast, a <it>Feast Risk </it>and a G<it>eneral Risk</it>. The larger the number of risk factors, the greater the risk to human health if CWD is transmissible to humans. Long-term surveillance of feast participants exposed to CWD is ongoing.</p> <p>Conclusion</p> <p>The risk data from this study provide a relative scale for cumulative exposure to CWD-infected tissues and surfaces, and those in the upper tiers of cumulative risk may be most at risk if CWD is transmissible to humans.</p

Emerging variants of canine enteric coronavirus associated with seasonal outbreaks of severe canine gastroenteric disease

Canine enteric coronavirus (CECoV) variants have an emerging role in severe outbreaks of canine gastroenteritis. Here we used syndromic health data from a sentinel network of UK veterinary practices to identify an outbreak of severe canine gastroenteritis. Affected dogs frequently presented with vomiting, diarrhoea and inappetence. Data from sentinel diagnostic laboratories showed similar seasonal increases in CECoV diagnosis. Membrane glycoprotein (M) gene sequence analysis implied wide geographical circulation of a new CECoV variant. Whole genome sequencing suggested the main circulating 2022 variant was most closely related to one previously identified in 2020 with additional spike gene recombination; all variants were unrelated to CECoV-like viruses recently associated with human respiratory disease. Identifying factors that drive population-level evolution, and its implications for host protection and virulence, will be important to understand the emerging role of CECoV variants in canine and human health, and may act as a model for coronavirus population adaptation more widely

Establishing a large prospective clinical cohort in people with head and neck cancer as a biomedical resource: head and neck 5000

BACKGROUND: Head and neck cancer is an important cause of ill health. Survival appears to be improving but the reasons for this are unclear. They could include evolving aetiology, modifications in care, improvements in treatment or changes in lifestyle behaviour. Observational studies are required to explore survival trends and identify outcome predictors. METHODS: We are identifying people with a new diagnosis of head and neck cancer. We obtain consent that includes agreement to collect longitudinal data, store samples and record linkage. Prior to treatment we give participants three questionnaires on health and lifestyle, quality of life and sexual history. We collect blood and saliva samples, complete a clinical data capture form and request a formalin fixed tissue sample. At four and twelve months we complete further data capture forms and send participants further quality of life questionnaires. DISCUSSION: This large clinical cohort of people with head and neck cancer brings together clinical data, patient-reported outcomes and biological samples in a single co-ordinated resource for translational and prognostic research