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Stochastic epidemic models which incorporate interactions between space and human mobility are a key tool to inform prioritisation of outbreak control to appropriate locations. However, methods for fitting such models to national-level population data are currently unfit for purpose due to the difficulty of marginalising over high-dimensional, highly-correlated censored epidemiological event data. Here we propose a new Bayesian MCMC approach to inference on a spatially-explicit stochastic SEIR meta-population model, using a suite of novel model-informed Metropolis-Hastings samplers. We apply this method to UK COVID-19 case data, showing real-time spatial results that were used to inform UK policy during the pandemic.

The aim of the review is to identify evidence of the current impact of extreme weather events (heavy rain, flooding, storms) on the chemicals in the environment, the chemical risk pathways and the populations most at risk. We aim to understand the mechanisms of harm include floods causing risk to health by mobilising toxic substantives, explosions and reactions that then lead to contamination of floodwater with chemicals. Harmful chemicals risks are those assessed as harmful to human health [details]: including POPs, PCBs, heavy metals or sources of chemicals (landfills, hazardous waste sites, mines, acid mine drainage) Justification of time period. We limited our search to papers published in and after year 2000 because a) papers published before this period unlikely to be relevant due changes in managing chemicals, and b) the previous review was ended in 2004.

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. 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, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. Data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.

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’.

BACKGROUND\ud Non-communicable lung disease and exposure to air pollution are major problems in sub-Saharan Africa. A high burden of chronic respiratory symptoms, spirometric abnormalities and air pollution exposures has been found in Malawian adults; whether the same would be true in children is unknown.\ud METHODS\ud This cross-sectional study of children aged 6-8 years, in rural Malawi, included households from communities participating in the Cooking and Pneumonia Study (CAPS), a trial of cleaner-burning biomass-fuelled cookstoves. We assessed; chronic respiratory symptoms, anthropometry, spirometric abnormalities (using Global Lung Initiative equations) and personal carbon monoxide (CO) exposure. Prevalence estimates were calculated, and multivariable analyses were done.\ud RESULTS\ud We recruited 804 children (mean age 7.1 years, 51.9% female), including 476 (260 intervention; 216 control) from CAPS households. Chronic respiratory symptoms (mainly cough (8.0%) and wheeze (7.1%)) were reported by 16.6% of children. Average height-for-age and weight-for-age z-scores were -1.04 and -1.10, respectively. Spirometric abnormalities (7.1% low forced vital capacity (FVC); 6.3% obstruction) were seen in 13.0% of children. Maximum CO exposure and carboxyhaemoglobin levels (COHb) exceeded WHO guidelines in 50.1% and 68.5% of children, respectively. Children from CAPS intervention households had lower COHb (median 3.50% vs 4.85%, p=0.006) and higher FVC z-scores (-0.22 vs -0.44, p=0.05) than controls.\ud CONCLUSION\ud The substantial burden of chronic respiratory symptoms, abnormal spirometry and air pollution exposures in children in rural Malawi is concerning; effective prevention and control strategies are needed. Our finding of potential benefit in CAPS intervention households calls for further research into clean-air interventions to maximise healthy lung development in children.

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak., Author summary Mathematical and simulation models may be used to inform policy in the early stages of an infectious disease outbreak by evaluating which control strategies will minimize the impact of the epidemic. In these early stages, significant uncertainty can limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, what is most important is the selection of the optimal control intervention, rather than accuracy of model predictions. We fit an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of alternative control interventions. These are compared to policy recommendations generated in hindsight using data from the entire outbreak. Our results show that the optimal control policy is identified accurately from an early stage in an outbreak, despite high levels of uncertainty in projections of epidemic size, and that the relative performance of control strategies is strongly mediated by our understanding of the locations of infected farms, rather than improved estimates of transmission parameters.

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Whereas host population data is typically available, for novel disease introductions there is a high chance of the pathogen utilising a vector for which data is unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times, and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds, and provides evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.

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.

The Highly Pathogenic Avian Influenza (HPAI) subtype H5N1 virus persists in many countries and has been circulating in poultry, wild birds. In addition, the virus has emerged in other species and frequent zoonotic spillover events indicate that there remains a significant risk to human health. It is crucial to understand the dynamics of the disease in the poultry industry to develop a more comprehensive knowledge of the risks of transmission and to establish a better distribution of resources when implementing control. In this paper, we develop a set of mathematical models that simulate the spread of HPAI H5N1 in the poultry industry in Thailand, utilising data from the 2004 epidemic. The model that incorporates the intensity of duck farming when assessing transmision risk provides the best fit to the spatiotemporal characteristics of the observed outbreak, implying that intensive duck farming drives transmission of HPAI in Thailand. We also extend our models using a sequential model fitting approach to explore the ability of the models to be used in “real time” during novel disease outbreaks. We conclude that, whilst predictions of epidemic size are estimated poorly in the early stages of disease outbreaks, the model can infer the preferred control policy that should be deployed to minimise the impact of the disease., Preventive Veterinary Medicine, 159, ISSN:0167-5877