Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation

BACKGROUND

Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied.

METHODS AND FINDINGS

We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation.

CONCLUSIONS

Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.

Emergence of SARS-CoV-2 Delta Variant and Effect of Nonpharmaceutical Interventions, British Columbia, Canada

In British Columbia, Canada, initial growth of the SARS-CoV-2 Delta variant was slower than that reported in other jurisdictions. Delta became the dominant variant (>50% prevalence) within ≈7-13 weeks of first detection in regions within the United Kingdom and United States. In British Columbia, it remained at <10% of weekly incident COVID-19 cases for 13 weeks after first detection on March 21, 2021, eventually reaching dominance after 17 weeks. We describe the growth of Delta variant cases in British Columbia during March 1-June 30, 2021, and apply retrospective counterfactual modeling to examine factors for the initially low COVID-19 case rate after Delta introduction, such as vaccination coverage and nonpharmaceutical interventions. Growth of COVID-19 cases in the first 3 months after Delta emergence was likely limited in British Columbia because additional nonpharmaceutical interventions were implemented to reduce levels of contact at the end of March 2021, soon after variant emergence.

Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model

Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.

Design and optimization of a high-energy optical parametric amplifier for broadband, spectrally incoherent pulses

Spectrally incoherent laser pulses with sufficiently large fractional bandwidth are in demand for the mitigation of laser-plasma instabilities occurring in high-energy laser-target interactions. Here, we modeled, implemented, and optimized a dual-stage high-energy optical parametric amplifier for broadband, spectrally incoherent pulses in the near-infrared. The amplifier delivers close to 400 mJ of signal energy through noncollinear parametric interaction of 100-nJ-scale broadband, spectrally incoherent seed pulses near 1053 nm with a narrowband high-energy pump operating at 526.5 nm. Mitigation strategies for high-frequency spatial modulations in the amplified signal caused by index inhomogeneities in the Nd:YLF rods of the pump laser are explored and discussed in detail.

Coughs, colds and "freshers' flu" survey in the University of Cambridge, 2007-2008

Universities provide many opportunities for the spread of infectious respiratory illnesses. Students are brought together into close proximity from all across the world and interact with one another in their accommodation, through lectures and small group teaching and in social settings. The COVID-19 global pandemic has highlighted the need for sufficient data to help determine which of these factors are important for infectious disease transmission in universities and hence control university morbidity as well as community spillover. We describe the data from a previously unpublished self-reported university survey of coughs, colds and influenza-like symptoms collected in Cambridge, UK, during winter 2007-2008. The online survey collected information on symptoms and socio-demographic, academic and lifestyle factors. There were 1076 responses, 97% from University of Cambridge students (5.7% of the total university student population), 3% from staff and <1% from other participants, reporting onset of symptoms between September 2007 and March 2008. Undergraduates are seen to report symptoms earlier in the term than postgraduates; differences in reported date of symptoms are also seen between subjects and accommodation types, although these descriptive results could be confounded by survey biases. Despite the historical and exploratory nature of the study, this is one of few recent detailed datasets of influenza-like infection in a university context and is especially valuable to share now to improve understanding of potential transmission dynamics in universities during the current COVID-19 pandemic.

Modelling the epidemiological implications for SARS-CoV-2 of Christmas household bubbles in England

The emergence of SARS-CoV-2 saw severe detriments to public health being inflicted by COVID-19 disease throughout 2020. In the lead up to Christmas 2020, the UK Government sought an easement of social restrictions that would permit spending time with others over the Christmas period, whilst limiting the risk of spreading SARS-CoV-2. In November 2020, plans were published to allow individuals to socialise within 'Christmas bubbles' with friends and family. This policy involved a planned easing of restrictions in England between 23-27 December 2020, with Christmas bubbles allowing people from up to three households to meet throughout the holiday period. We estimated the epidemiological impact of both this and alternative bubble strategies that allowed extending contacts beyond the immediate household. We used a stochastic individual-based model for a synthetic population of 100,000 households, with demographic and SARS-CoV-2 epidemiological characteristics comparable to England as of November 2020. We evaluated five Christmas bubble scenarios for the period 23-27 December 2020, assuming our populations of households did not have symptomatic infection present and were not in isolation as the eased social restrictions began. Assessment comprised incidence and cumulative infection metrics. We tested the sensitivity of the results to a situation where it was possible for households to be in isolation at the beginning of the Christmas bubble period and also when there was lower adherence to testing, contact tracing and isolation interventions. We found that visiting family and friends over the holiday period for a shorter duration and in smaller groups was less risky than spending the entire five days together. The increases in infection from greater amounts of social mixing disproportionately impacted the eldest. We provide this account as an illustration of a real-time contribution of modelling insights to a scientific advisory group, the Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) for the Scientific Advisory Group for Emergencies (SAGE) in the UK, during the COVID-19 pandemic. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, [Formula: see text], has taken on special significance in terms of the general understanding of whether the epidemic is under control ([Formula: see text]). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

Comparison of the 2021 COVID-19 roadmap projections against public health data in England

Control and mitigation of the COVID-19 pandemic in England has relied on a combination of vaccination and non-pharmaceutical interventions (NPIs). Some of these NPIs are extremely costly (economically and socially), so it was important to relax these promptly without overwhelming already burdened health services. The eventual policy was a Roadmap of four relaxation steps throughout 2021, taking England from lock-down to the cessation of all restrictions on social interaction. In a series of six Roadmap documents generated throughout 2021, models assessed the potential risk of each relaxation step. Here we show that the model projections generated a reliable estimation of medium-term hospital admission trends, with the data points up to September 2021 generally lying within our 95% prediction intervals. The greatest uncertainties in the modelled scenarios came from vaccine efficacy estimates against novel variants, and from assumptions about human behaviour in the face of changing restrictions and risk.

The ’Roadmap’ for relaxation of COVID-19 restrictions in England in 2021 was informed by mathematical modelling. Here, the authors perform a retrospective assessment of the accuracy of modelling predictions and identify the main sources of uncertainty that led to observed values deviating from projections.

Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study

Background. Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, be optimized to maximize economic benefits while achieving substantial reductions in disease. Methods. Here, we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay (WTP) for health improvement. Results. We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the WTP per quality adjusted life year loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusion. It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

Descriptive analysis of national bovine viral diarrhoea test data in England (2016-2020)

Background

Bovine viral diarrhoea virus (BVDV) causes substantial economic losses to the cattle industry; however, control and eradication can be achieved by identifying and removing persistently infected cattle from the herd. Each UK nation has separate control programmes. The English scheme, BVDFree, started in 2016 and is voluntary.

Methods

We analysed the test results submitted to BVDFree from 5847 herds between 2016 and 2020.

Results

In 2020, 13.5% of beef breeders and 20.0% of dairy herds that submitted tests had at least one positive (virus/antibody) test result. Although lower than in previous years, there was no clear trend in the proportion of positive tests over time. In virus testing herds, 0.4% of individual tests were positive in 2020, and 1.5% of individual tests were positive in BVDV‐positive virus testing herds. Dairy herds and larger herds were more likely to join BVDFree, and dairy herds were also more likely to virus test than beef breeder herds. Larger herds, herds that used virus testing and herds that had BVDV‐positive test results were more likely to continue submitting tests to BVDFree.

Conclusions

The findings provide a benchmark for the status of BVDV control in England; continued analysis of test results will be important to assess progress towards eradication.

Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour

The spread of infection amongst livestock depends not only on the traits of the pathogen and the livestock themselves, but also on the veterinary health behaviours of farmers and how this impacts their implementation of disease control measures. Controls that are costly may make it beneficial for individuals to rely on the protection offered by others, though that may be sub-optimal for the population. Failing to account for socio-behavioural properties may produce a substantial layer of bias in infectious disease models. We investigated the role of heterogeneity in vaccine response across a population of farmers on epidemic outbreaks amongst livestock, caused by pathogens with differential speed of spread over spatial landscapes of farms for two counties in England (Cumbria and Devon). Under different compositions of three vaccine behaviour groups (precautionary, reactionary, non-vaccination), we evaluated from population- and individual-level perspectives the optimum threshold distance to premises with notified infection that would trigger responsive vaccination by the reactionary vaccination group. We demonstrate a divergence between population and individual perspectives in the optimal scale of reactive voluntary vaccination response. In general, minimising the population-level perspective cost requires a broader reactive uptake of the intervention, whilst optimising the outcome for the average individual increased the likelihood of larger scale disease outbreaks. When the relative cost of vaccination was low and the majority of premises had undergone precautionary vaccination, then adopting a perspective that optimised the outcome for an individual gave a broader spatial extent of reactive response compared to a perspective wanting to optimise outcomes for everyone in the population. Under our assumed epidemiological context, the findings identify livestock disease intervention receptiveness and cost combinations where one would expect strong disagreement between the intervention stringency that is best from the perspective of a stakeholder responsible for supporting the livestock industry compared to a sole livestock owner. Were such discord anticipated and achieving a consensus view across perspectives desired, the findings may also inform those managing veterinary health policy the requisite reduction in intervention cost and/or the required extent of nurturing beneficial community attitudes towards interventions.

The COVID-19 pandemic has shown how crucial human behaviour is in controlling the spread of an infectious disease. The same is true of livestock, where farmer behaviour is critical to reduce the spread of an infection to enhance animal welfare and reduce economic losses. An ongoing concern for livestock owners is therefore ensuring they have adequate disease management procedures. However, what an individual farmer considers an appropriate way to control an infection in their own livestock may not be the best way to prevent an infection for every farmer’s livestock in the population. We describe a mathematical model combining epidemiological and behavioural elements to study the tension between individual and population-level control of livestock diseases. Applied to representative livestock systems in two counties in England (Cumbria and Devon), and splitting farmers into three types of vaccine behaviour groups (precautionary, reactionary, non-vaccination), we show what individual farmers see as an effective way to reduce infection is not the same as would benefit every farmer. The preferred response to protect every farmer’s livestock is to encourage wider uptake of reactive vaccination, whereas optimising the spatial extent of reactive vaccination for the average individual increases the chance of larger disease outbreaks.

Assessing the impact of lateral flow testing strategies on within-school SARS-CoV-2 transmission and absences: A modelling study

Rapid testing strategies that replace the isolation of close contacts through the use of lateral flow device tests (LFTs) have been suggested as a way of controlling SARS-CoV-2 transmission within schools that maintain low levels of pupil absences. We developed an individual-based model of a secondary school formed of exclusive year group bubbles (five year groups, with 200 pupils per year) to assess the likely impact of strategies using LFTs in secondary schools over the course of a seven-week half-term on transmission, absences, and testing volume, compared to a policy of isolating year group bubbles upon a pupil returning a positive polymerase chain reaction (PCR) test. We also considered the sensitivity of results to levels of participation in rapid testing and underlying model assumptions. While repeated testing of year group bubbles following case detection is less effective at reducing infections than a policy of isolating year group bubbles, strategies involving twice weekly mass testing can reduce infections to lower levels than would occur under year group isolation. By combining regular testing with serial contact testing or isolation, infection levels can be reduced further still. At high levels of pupil participation in lateral flow testing, strategies replacing the isolation of year group bubbles with testing substantially reduce absences, but require a high volume of testing. Our results highlight the conflict between the goals of minimising within-school transmission, minimising absences and minimising testing burden. While rapid testing strategies can reduce school transmission and absences, they may lead to a large number of daily tests.

During the COVID-19 pandemic, a range of measures have been implemented to reduce transmission in schools. If an infected pupil is detected, one approach involves ‘isolating’ their contacts, who must then remain at home and not attend school. However, this may lead to high levels of pupil absences, which in turn may impact educational attainment. An alternative approach involves testing the contacts of infected individuals each day, who are allowed to attend school if they test negative. This can be achieved using lateral flow device tests, which can be taken at home and return a result in under thirty minutes. In this paper, using an individual-based model, we compare the impact of strategies that use lateral flow device tests to rapidly test secondary school pupils to strategies that require the contacts of infected individuals to isolate. We find that a strategy that combines testing pupils regularly with testing the contacts of identified infected individuals for a period of seven days has the potential to keep both transmission and absences low. However, such a strategy requires a high number of tests. Our results demonstrate the conflict between the competing aims of minimising transmission, minimising absences, and minimising test burden.

Cattle farmer psychosocial profiles and their association with control strategies for bovine viral diarrhea

Bovine viral diarrhea (BVD) is endemic in the United Kingdom and causes major economic losses. Control is largely voluntary for individual farmers and is likely to be influenced by psychosocial factors, such as altruism, trust, and psychological proximity (feeling close) to relevant “others,” such as farmers, veterinarians, the government, and their cows. These psychosocial factors (factors with both psychological and social aspects) are important determinants of how people make decisions related to their own health, many of which have not been studied in the context of infectious disease control by farmers. Farmer psychosocial profiles were investigated using multiple validated measures in an observational survey of 475 UK cattle farmers using the capability, opportunity, motivation-behavior (COM-B) framework. Farmers were clustered by their BVD control practices using latent class analysis. Farmers were split into 5 BVD control behavior classes, which were tested for associations with the psychosocial and COM-B factors using multinomial logistic regression, with doing nothing as the baseline class. Farmers who were controlling disease both for themselves and others were more likely to do something to control BVD (e.g., test, vaccinate). Farmers who did not trust other farmers, had high psychological capability (knowledge and understanding of how to control disease), and had high physical opportunity (time and money to control disease) were more likely to have a closed, separate herd and test. Farmers who did not trust other farmers were also more likely to undertake many prevention strategies with an open herd. Farmers with high automatic motivation (habits and emotions) and reflective motivation (decisions and goals) were more likely to vaccinate and test, alone or in combination with other controls. Farmers with high psychological proximity (feeling of closeness) to their veterinarian were more likely to undertake many prevention strategies in an open herd. Farmers with high psychological proximity to dairy farmers and low psychological proximity to beef farmers were more likely to keep their herd closed and separate and test or vaccinate and test. Farmers who had a lot of trust in other farmers and invested in them, rather than keeping everything for themselves, were more likely to be careful introducing new stock and test. In conclusion, farmer psychosocial factors were associated with strategies for BVD control in UK cattle farmers. Psychological proximity to veterinarians was a novel factor associated with proactive BVD control and was more important than the more extensively investigated trust. These findings highlight the importance of a close veterinarian-farmer relationship and are important for promoting effective BVD control by farmers, which has implications for successful nationwide BVD control and eradication schemes.

Precautionary breaks: Planned, limited duration circuit breaks to control the prevalence of SARS-CoV2 and the burden of COVID-19 disease

COVID-19 in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities that slowed during the summer as control measures were relaxed. From August 2020, infections, hospitalisations and deaths began rising once more and various NPIs were applied locally throughout the UK in response. Controlling any rise in infection is a compromise between public health and societal costs, with more stringent NPIs reducing cases but damaging the economy and restricting freedoms. Typically, NPI imposition is made in response to the epidemiological state, are of indefinite length and are often imposed at short notice, greatly increasing the negative impact. An alternative approach is to consider planned, limited duration periods of strict NPIs aiming to purposefully reduce prevalence before such emergency NPIs are required. These "precautionary breaks" may offer a means of keeping control of the epidemic, while their fixed duration and the forewarning may limit their societal impact. Here, using simple analysis and age-structured models matched to the UK SARS-CoV-2 epidemic, we investigate the action of precautionary breaks. In particular we consider their impact on the prevalence of SARS-CoV-2 infection, as well as the total number of predicted hospitalisations and deaths caused by COVID-19 disease. We find that precautionary breaks provide the biggest gains when the growth rate is low, but offer a much needed brake on increasing infection when the growth rate is higher, potentially allowing other measures to regain control.

Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics

Viral reproduction of SARS-CoV-2 provides opportunities for the acquisition of advantageous mutations, altering viral transmissibility, disease severity, and/or allowing escape from natural or vaccine-derived immunity. We use three mathematical models: a parsimonious deterministic model with homogeneous mixing; an age-structured model; and a stochastic importation model to investigate the effect of potential variants of concern (VOCs). Calibrating to the situation in England in May 2021, we find epidemiological trajectories for putative VOCs are wide-ranging and dependent on their transmissibility, immune escape capability, and the introduction timing of a postulated VOC-targeted vaccine. We demonstrate that a VOC with a substantial transmission advantage over resident variants, or with immune escape properties, can generate a wave of infections and hospitalisations comparable to the winter 2020-2021 wave. Moreover, a variant that is less transmissible, but shows partial immune-escape could provoke a wave of infection that would not be revealed until control measures are further relaxed.

Modelling SARS-CoV-2 transmission in a UK university setting

Around 40% of school leavers in the UK attend university and individual universities generally host thousands of students each academic year. Bringing together these student communities during the COVID-19 pandemic may require strong interventions to control transmission. Prior modelling analyses of SARS-CoV-2 transmission within universities using compartmental modelling approaches suggest that outbreaks are almost inevitable. We constructed a network-based model to capture the interactions of a student population in different settings (housing, social and study). For a single academic term of a representative campus-based university, we ran a susceptible-latent-infectious-recovered type epidemic process, parameterised according to available estimates for SARS-CoV-2. We investigated the impact of: adherence to (or effectiveness of) isolation and test and trace measures; room isolation of symptomatic students; and supplementary mass testing. With all adhering to test, trace and isolation measures, we found that 22% (7%-41%) of the student population could be infected during the autumn term, compared to 69% (56%-76%) when assuming zero adherence to such measures. Irrespective of the adherence to isolation measures, on average a higher proportion of students resident on-campus became infected compared to students resident off-campus. Room isolation generated minimal benefits. Regular mass testing, together with high adherence to isolation and test and trace measures, could substantially reduce the proportion infected during the term compared to having no testing. Our findings suggest SARS-CoV-2 may readily transmit in a university setting if there is limited adherence to nonpharmaceutical interventions and/or there are delays in receiving test results. Following isolation guidance and effective contact tracing curbed transmission and reduced the expected time an adhering student would spend in isolation.

Comparison between one and two dose SARS-CoV-2 vaccine prioritization for a fixed number of vaccine doses

The swift development of SARS-CoV-2 vaccines has been met with worldwide commendation. However, in the context of an ongoing pandemic there is an interplay between infection and vaccination. While infection can grow exponentially, vaccination rates are generally limited by supply and logistics. With the first SARS-CoV-2 vaccines receiving medical approval requiring two doses, there has been scrutiny on the spacing between doses; an elongated period between doses allows more of the population to receive a first vaccine dose in the short-term generating wide-spread partial immunity. Focusing on data from England, we investigated prioritization of a one dose or two dose vaccination schedule given a fixed number of vaccine doses and with respect to a measure of maximizing averted deaths. We optimized outcomes for two different estimates of population size and relative risk of mortality for at-risk groups within the Phase 1 vaccine priority order. Vaccines offering relatively high protection from the first dose favour strategies that prioritize giving more people one dose, although with increasing vaccine supply eventually those eligible and accepting vaccination will receive two doses. While optimal dose timing can substantially reduce the overall mortality risk, there needs to be careful consideration of the logistics of vaccine delivery.

SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return

In this paper, we present work on SARS-CoV-2 transmission in UK higher education settings using multiple approaches to assess the extent of university outbreaks, how much those outbreaks may have led to spillover in the community, and the expected effects of control measures. Firstly, we found that the distribution of outbreaks in universities in late 2020 was consistent with the expected importation of infection from arriving students. Considering outbreaks at one university, larger halls of residence posed higher risks for transmission. The dynamics of transmission from university outbreaks to wider communities is complex, and while sometimes spillover does occur, occasionally even large outbreaks do not give any detectable signal of spillover to the local population. Secondly, we explored proposed control measures for reopening and keeping open universities. We found the proposal of staggering the return of students to university residence is of limited value in terms of reducing transmission. We show that student adherence to testing and self-isolation is likely to be much more important for reducing transmission during term time. Finally, we explored strategies for testing students in the context of a more transmissible variant and found that frequent testing would be necessary to prevent a major outbreak.

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R < 1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first-wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

The impact of school reopening on the spread of COVID-19 in England

By mid-May 2020, cases of COVID-19 in the UK had been declining for over a month; a multi-phase emergence from lockdown was planned, including a scheduled partial reopening of schools on 1 June 2020. Although evidence suggests that children generally display mild symptoms, the size of the school-age population means the total impact of reopening schools is unclear. Here, we present work from mid-May 2020 that focused on the imminent opening of schools and consider what these results imply for future policy. We compared eight strategies for reopening primary and secondary schools in England. Modifying a transmission model fitted to UK SARS-CoV-2 data, we assessed how reopening schools affects contact patterns, anticipated secondary infections and the relative change in the reproduction number, R. We determined the associated public health impact and its sensitivity to changes in social distancing within the wider community. We predicted that reopening schools with half-sized classes or focused on younger children was unlikely to push R above one. Older children generally have more social contacts, so reopening secondary schools results in more cases than reopening primary schools, while reopening both could have pushed R above one in some regions. Reductions in community social distancing were found to outweigh and exacerbate any impacts of reopening. In particular, opening schools when the reproduction number R is already above one generates the largest increase in cases. Our work indicates that while any school reopening will result in increased mixing and infection amongst children and the wider population, reopening schools alone in June 2020 was unlikely to push R above one. Ultimately, reopening decisions are a difficult trade-off between epidemiological consequences and the emotional, educational and developmental needs of children. Into the future, there are difficult questions about what controls can be instigated such that schools can remain open if cases increase. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Developing a Framework for Public Involvement in Mathematical and Economic Modelling: Bringing New Dynamism to Vaccination Policy Recommendations

OBJECTIVES

The Mathematical and Economic Modelling for Vaccination and Immunisation Evaluation (MEMVIE) programme aimed to explore, capture and support the potential contribution of the public to mathematical and economic modelling, in order to identify the values that underpin public involvement (PI) in modelling and co-produce a framework that identifies the nature and type of PI in modelling and supports its implementation.

METHODS

We established a PI Reference Group, who worked collaboratively with the academic contributors to create a deliberative knowledge space, which valued different forms of knowledge, expertise and evidence. Together, we explored the key steps of mathematical and economic methods in 21 meetings during 2015-2020. These deliberations generated rich discussion, through which we identified potential points of public contribution and the values that underpin PI in modelling. We iteratively developed a framework to guide future practice of PI in modelling.

RESULTS

We present the MEMVIE Public Involvement Framework in two forms: a short form to summarise key elements, and a long form framework to provide a detailed description of each potential type of public contribution at each stage of the modelling process. At a macro level, the public can contribute to reviewing context, reviewing relevance, assessing data and justifying model choice, troubleshooting, and interpreting and reviewing outcomes and decision making. The underpinning values that drive involvement include the public contributing to the validity of the model, potentially enhancing its relevance, utility and transparency through diverse inputs, and enhancing the credibility, consistency and continuous development through scrutiny, in addition to contextualising the model within a wider societal view.

DISCUSSION AND CONCLUSION

PI in modelling is in its infancy. The MEMVIE Framework is the first attempt to identify potential points of collaborative public contribution to modelling, but it requires further evaluation and refinement that we are undertaking in a subsequent study.

Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.

An analysis of school absences in England during the COVID-19 pandemic

BACKGROUND

The introduction of SARS-CoV-2, the virus that causes COVID-19 infection, in the UK in early 2020, resulted in the introduction of several control policies to reduce disease spread. As part of these restrictions, schools were closed to all pupils in March (except for vulnerable and key worker children), before re-opening to certain year groups in June. Finally, all school children returned to the classroom in September.

METHODS

Here, we analyse data on school absences in late 2020 as a result of COVID-19 infection and how that varied through time as other measures in the community were introduced. We utilise data from the Department for Education Educational Settings database and examine how pupil and teacher absences change in both primary and secondary schools.

RESULTS

Our results show that absences as a result of COVID-19 infection rose steadily following the re-opening of schools in September. Cases in teachers declined during the November lockdown, particularly in regions previously in tier 3, the highest level of control at the time. Cases in secondary school pupils increased for the first 2 weeks of the November lockdown, before decreasing. Since the introduction of the tier system, the number of absences with confirmed infection in primary schools was observed to be (markedly) lower than that in secondary schools. In December, we observed a large rise in the number of absences per school in secondary school settings in the South East and London, but such rises were not observed in other regions or in primary school settings. We conjecture that the increased transmissibility of the new variant in these regions may have contributed to this rise in secondary school cases. Finally, we observe a positive correlation between cases in the community and cases in schools in most regions, with weak evidence suggesting that cases in schools lag behind cases in the surrounding community.

CONCLUSIONS

We conclude that there is no significant evidence to suggest that schools are playing a substantial role in driving spread in the community and that careful monitoring may be required as schools re-open to determine the effect associated with open schools upon community incidence.

Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study

BACKGROUND

The dynamics of vaccination against SARS-CoV-2 are complicated by age-dependent factors, changing levels of infection, and the relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines, necessitating the use of mathematical models. Our aims were to use epidemiological data from the UK together with estimates of vaccine efficacy to predict the possible long-term dynamics of SARS-CoV-2 under the planned vaccine rollout.

METHODS

In this study, we used a mathematical model structured by age and UK region, fitted to a range of epidemiological data in the UK, which incorporated the planned rollout of a two-dose vaccination programme (doses 12 weeks apart, protection onset 14 days after vaccination). We assumed default vaccine uptake of 95% in those aged 80 years and older, 85% in those aged 50-79 years, and 75% in those aged 18-49 years, and then varied uptake optimistically and pessimistically. Vaccine efficacy against symptomatic disease was assumed to be 88% on the basis of Pfizer-BioNTech and Oxford-AstraZeneca vaccines being administered in the UK, and protection against infection was varied from 0% to 85%. We considered the combined interaction of the UK vaccination programme with multiple potential future relaxations (or removals) of NPIs, to predict the reproduction number (R) and pattern of daily deaths and hospital admissions due to COVID-19 from January, 2021, to January, 2024.

FINDINGS

We estimate that vaccination alone is insufficient to contain the outbreak. In the absence of NPIs, even with our most optimistic assumption that the vaccine will prevent 85% of infections, we estimate R to be 1·58 (95% credible intervals [CI] 1·36-1·84) once all eligible adults have been offered both doses of the vaccine. Under the default uptake scenario, removal of all NPIs once the vaccination programme is complete is predicted to lead to 21 400 deaths (95% CI 1400-55 100) due to COVID-19 for a vaccine that prevents 85% of infections, although this number increases to 96 700 deaths (51 800-173 200) if the vaccine only prevents 60% of infections. Although vaccination substantially reduces total deaths, it only provides partial protection for the individual; we estimate that, for the default uptake scenario and 60% protection against infection, 48·3% (95% CI 48·1-48·5) and 16·0% (15·7-16·3) of deaths will be in individuals who have received one or two doses of the vaccine, respectively.

INTERPRETATION

For all vaccination scenarios we investigated, our predictions highlight the risks associated with early or rapid relaxation of NPIs. Although novel vaccines against SARS-CoV-2 offer a potential exit strategy for the pandemic, success is highly contingent on the precise vaccine properties and population uptake, both of which need to be carefully monitored.

FUNDING

National Institute for Health Research, Medical Research Council, and UK Research and Innovation.

A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create 'COVID-secure' workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels. In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease.

Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a "stay at home" order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

Optimising age coverage of seasonal influenza vaccination in England: A mathematical and health economic evaluation

For infectious disease prevention, policy-makers are typically required to base policy decisions in light of operational and monetary restrictions, prohibiting implementation of all candidate interventions. To inform the evidence-base underpinning policy decision making, mathematical and health economic modelling can be a valuable constituent.

Applied to England, this study aims to identify the optimal target age groups when extending a seasonal influenza vaccination programme of at-risk individuals to those individuals at low risk of developing complications following infection. To perform this analysis, we utilise an age- and strain-structured transmission model that includes immunity propagation mechanisms which link prior season epidemiological outcomes to immunity at the beginning of the following season. Making use of surveillance data from the past decade in conjunction with our dynamic model, we simulate transmission dynamics of seasonal influenza in England from 2012 to 2018. We infer that modified susceptibility due to natural infection in the previous influenza season is the only immunity propagation mechanism to deliver a non-negligible impact on the transmission dynamics. Further, we discerned case ascertainment to be higher for young infants compared to adults under 65 years old, and uncovered a decrease in case ascertainment as age increased from 65 to 85 years of age.

Our health economic appraisal sweeps vaccination age space to determine threshold vaccine dose prices achieving cost-effectiveness under differing paired strategies. In particular, we model offering vaccination to all those low-risk individuals younger than a given age (but no younger than two years old) and all low-risk individuals older than a given age, while maintaining vaccination of at-risk individuals of any age. All posited strategies were deemed cost-effective. In general, the addition of low-risk vaccination programmes whose coverage encompassed children and young adults (aged 20 and below) were highly cost-effective. The inclusion of elder age-groups to the low-risk programme typically lessened the cost-effectiveness. Notably, elderly-centric programmes vaccinating from 65-75 years and above had the least permitted expense per vaccine.

Vaccination is an established method to provide protection against seasonal influenza and its complications. Yet, a need to administer an updated vaccine on an annual basis presents significant operational challenges and sizeable costs. Consequently, policy makers typically have to decide how to deploy a finite amount of resource in a cost-effective manner. A combination of mathematical and health economic modelling can be used to address such a question. Here, we developed an age- and strain-structured mathematical model for seasonal influenza transmission dynamics that incorporates mechanisms for immunity propagation, which we used to reconstruct transmission dynamics of seasonal influenza in England from 2012 to 2018.

We then performed a health economic evaluation assessing the cost-effectiveness of extending a seasonal influenza vaccination programme of at-risk individuals to also include, for targeted age groups, those individuals at low risk of developing complications following infection. The findings suggest the inclusion of low-risk vaccination programmes whose coverage encompassed children and young adults (aged 20 and below) to be highly cost-effective. In contrast, the inclusion of elder age-groups to the low-risk programme typically lessened the cost-effectiveness.

High-energy parametric amplification of spectrally incoherent broadband pulses

We study and demonstrate the efficient parametric amplification of spectrally incoherent broadband nanosecond pulses to high energies. Signals composed of mutually incoherent monochromatic lines or amplified spontaneous emission are amplified in a sequence of optical parametric amplifiers pumped at 526.5 nm, with the last amplifier set in a collinear geometry. This configuration results in 70% conversion efficiency from the pump to the combined signal and idler, with a combined energy reaching 400 mJ and an optical spectrum extending over 60 nm around 1053 nm. The spatial, spectral, and temporal properties of the amplified waves are investigated. The demonstrated high conversion efficiency, spectral incoherence, and large bandwidth open the way to a new generation of high-energy, solid-state laser drivers that mitigate laser-plasma instabilities and laser-beam imprint via enhanced spectral bandwidth.

Seasonal influenza: Modelling approaches to capture immunity propagation

Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.

Influenza, the flu, is a highly infectious respiratory disease that can cause serious health complications. Characterised by seasonal outbreaks, a key challenge for policy-makers is implementing measures to successfully lessen the public health burden on an annual basis. Seasonal influenza vaccine programmes are an established method to deliver cost-effective prevention against influenza and its complications. Transmission models have been a fundamental component of vaccine programme analysis, informing the efficient use of limited resources. However, these models generally treat each influenza season and each strain circulating within that season in isolation. By developing a mathematical model explicitly including multiple immunity propagation mechanisms, then fit to influenza-related vaccine and epidemiological data from England via statistical methods, we sought to quantify the extent that epidemiological events in the previous influenza season alter susceptibility at the onset of the following season. The findings suggest that susceptibility in the next season to a given influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current season. Residual vaccine immunity has a lesser role. Prospectively, the adoption of influenza transmission modelling frameworks with immunity propagation would provide a comprehensive manner to assess the impact of seasonal vaccination programmes.

Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil

BACKGROUND

The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models.

METHODS

We have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.

RESULTS

We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites.

CONCLUSIONS

Establishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

The impact of surveillance and control on highly pathogenic avian influenza outbreaks in poultry in Dhaka division, Bangladesh

In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh.

Ongoing circulation of avian influenza H5N1 viruses in poultry pose a global public health risk and cause extensive damage to the livestock industry. One of several countries in South Asia gravely affected is Bangladesh, where the poultry industry is an economically and socially important sector. Identifying the optimal control response in anticipation of further outbreaks is therefore a key challenge for policy-makers. This study tested a series of culling, vaccination and active surveillance intervention actions, assessing specific control objectives related to the burden or duration of H5N1 outbreaks among commercial poultry farms in the Dhaka division. This assessment was achieved through performing computational simulations of a previously developed H5N1 influenza transmission mathematical model. The findings of this assessment indicate that the impact of reactive culling and vaccination control policies are dependent upon transmission characteristics, control objectives and availability of resources to enact the control action, whereas proactive surveillance schemes significantly outperform reactive surveillance procedures irrespective of these conditions.

Spectrally tunable, temporally shaped parametric front end to seed high-energy Nd:glass laser systems

We describe a parametric-amplification-based front end for seeding high-energy Nd:glass laser systems. The front end delivers up to 200 mJ by parametric amplification in 2.5-ns flat-in-time pulses tunable over more than 15 nm. Spectral tunability over a range larger than what is typically achieved by laser media at similar energy levels is implemented to investigate cross-beam energy transfer in multibeam target experiments. The front-end operation is simulated to explain the amplified signal's sensitivity to the input pump and signal. A large variety of amplified waveforms are generated by closed-loop pulse shaping. Various properties and limitations of this front end are discussed.

Ornamental plants on sale to the public are a significant source of pesticide residues with implications for the health of pollinating insects

Garden centres frequently market nectar- and pollen-rich ornamental plants as "pollinator-friendly", however these plants are often treated with pesticides during their production. There is little information on the nature of pesticide residues present at the point of purchase and whether these plants may actually pose a threat to, rather than benefit, the health of pollinating insects. Using mass spectrometry analyses, this study screened leaves from 29 different 'bee-friendly' plants for 8 insecticides and 16 fungicides commonly used in ornamental production. Only two plants (a Narcissus and a Salvia variety) did not contain any pesticide and 23 plants contained more than one pesticide, with some species containing mixtures of 7 (Ageratum houstonianum) and 10 (Erica carnea) different agrochemicals. Neonicotinoid insecticides were detected in more than 70% of the analysed plants, and chlorpyrifos and pyrethroid insecticides were found in 10% and 7% of plants respectively. Boscalid, spiroxamine and DMI-fungicides were detected in 40% of plants. Pollen samples collected from 18 different plants contained a total of 13 different pesticides. Systemic compounds were detected in pollen samples at similar concentrations to those in leaves. However, some contact (chlorpyrifos) and localised penetrant pesticides (iprodione, pyroclastrobin and prochloraz) were also detected in pollen, likely arising from direct contamination during spraying. The neonicotinoids thiamethoxam, clothianidin and imidacloprid and the organophosphate chlorpyrifos were present in pollen at concentrations between 6.9 and 81 ng/g and at levels that overlap with those known to cause harm to bees. The net effect on pollinators of buying plants that are a rich source of forage for them but simultaneously risk exposing them to a cocktail of pesticides is not clear. Gardeners who wish to gain the benefits without the risks should seek uncontaminated plants by growing their own from seed, plant-swapping or by buying plants from an organic nursery.

Spreading of components of mood in adolescent social networks

Recent research has provided evidence that mood can spread over social networks via social contagion, but that, in seeming contradiction to this, depression does not. Here, we investigate whether there is evidence for the individual components of mood (such as appetite, tiredness and sleep) spreading through US adolescent friendship networks while adjusting for confounding by modelling the transition probabilities of changing mood state over time. We find that having more friends with worse mood is associated with a higher probability of an adolescent worsening in mood and a lower probability of improving, and vice versa for friends with better mood, for the overwhelming majority of mood components. We also show, however, that this effect is not strong enough in the negative direction to lead to a significant increase in depression incidence, helping to resolve the seeming contradictory nature of existing research. Our conclusions, therefore, link in to current policy discussions on the importance of subthreshold levels of depressive symptoms and could help inform interventions against depression in high schools.

Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk

Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human-animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for these discrepancies in transmission behaviour between epidemics, such as changes in surveillance sensitivity and biosecurity practices.

Spreading of healthy mood in adolescent social networks

Depression is a major public health concern worldwide. There is evidence that social support and befriending influence mental health, and an improved understanding of the social processes that drive depression has the potential to bring significant public health benefits. We investigate transmission of mood on a social network of adolescents, allowing flexibility in our model by making no prior assumption as to whether it is low mood or healthy mood that spreads. Here, we show that while depression does not spread, healthy mood among friends is associated with significantly reduced risk of developing and increased chance of recovering from depression. We found that this spreading of healthy mood can be captured using a non-linear complex contagion model. Having sufficient friends with healthy mood can halve the probability of developing, or double the probability of recovering from, depression over a 6–12-month period on an adolescent social network. Our results suggest that promotion of friendship between adolescents can reduce both incidence and prevalence of depression.

A picosecond beam-timing system for the OMEGA laser

A timing system is demonstrated for the OMEGA Laser System that guarantees all 60 beams will arrive on target simultaneously with a root mean square variability of 4 ps. The system relies on placing a scattering sphere at the target position to couple the ultraviolet light from each beam into a single photodetector.

Single-shot high-resolution characterization of optical pulses by spectral phase diversity

The concept of spectral phase diversity is proposed and applied to the temporal characterization of optical pulses. The experimental trace is composed of the measured power of a plurality of ancillary optical pulses derived from the pulse under test by adding known amounts of chromatic dispersion. The spectral phase of the pulse under test is retrieved by minimizing the error between the experimental trace and a trace calculated using the known optical spectrum and diagnostic parameters. An assembly composed of splitters and dispersive delay fibers has been used to generate 64 ancillary pulses whose instantaneous power can be detected in a single shot with a high-bandwidth photodiode and oscilloscope. The diagnostic is experimentally shown to accurately characterize pulses from a chirped-pulse-amplification system when its stretcher is detuned from the position for optimal recompression. Pulse-shape reconstruction for pulses shorter than the photodetection impulse response has been demonstrated. Various investigations of the performance with respect to the number of ancillary pulses and the range of chromatic dispersion generated in the diagnostic are presented.

Mechanisms of action of therapeutic antibodies for cancer

The therapeutic utility of antibodies and their derivatives is achieved by various means. The FDA has approved several targeted antibodies that disrupt signaling of various growth factor receptors for the treatment of a number of cancers. Rituximab, and other anti-CD20 monoclonal antibodies are active in B cell malignancies. As more experience has been gained with anti-CD20 monoclonal antibodies, the multifactorial nature of their anti-tumor mechanisms has emerged. Other targeted antibodies function to dampen inhibitory checkpoints. These checkpoint inhibitors have recently achieved dramatic results in several cancers, including melanoma. These and related antibodies continue to be investigated in the clinical and pre-clinical settings. Novel antibody structures that target two or more antigens have also made their way into clinical use. Tumor targeted antibodies can also be conjugated to chemo- or radiotherapeutic agents, or catalytic toxins, as a means to deliver toxic payloads to cancer cells. Here we provide a review of these mechanisms and a discussion of their relevance to current and future clinical applications.

Intersex related gene expression profiles in clams Scrobicularia plana: Molecular markers and environmental application

Intersex, the appearance of female characteristics in male gonads, has been identified in several aquatic species. It is a widespread phenomenon in populations of the bivalve, Scrobicularia plana, from the southwest coast of the U.K. Genes previously identified as differentially expressed (ferritin, testicular haploid expressed gene, THEG, proliferating cell nuclear antigen, PCNA; receptor activated protein kinase C, RACK; cytochrome B, CYB; and cytochrome c oxidase 1, COX1) in intersex clams relative to normal male clams, were selected for characterisation and an environmental survey of the Channel region. Transcripts were significantly differentially expressed at sites with varying intersex incidence and contaminant burdens. Significant correlations between specific gene expressions, key contaminants and sampling locations have been identified, though no single gene was associated with intersex incidence. The results highlight the difficulty in understanding the intersex phenomenon in molluscs where there is still a lack of knowledge on the control of normal reproduction.

Design and preliminary recruitment results of the Cluster randomised triAl of PSA testing for Prostate cancer (CAP)

BACKGROUND

Screening for prostate cancer continues to generate controversy because of concerns about over-diagnosis and unnecessary treatment. We describe the rationale, design and recruitment of the Cluster randomised triAl of PSA testing for Prostate cancer (CAP) trial, a UK-wide cluster randomised controlled trial investigating the effectiveness and cost-effectiveness of prostate-specific antigen (PSA) testing.

METHODS

Seven hundred and eighty-five general practitioner (GP) practices in England and Wales were randomised to a population-based PSA testing or standard care and then approached for consent to participate. In the intervention arm, men aged 50-69 years were invited to undergo PSA testing, and those diagnosed with localised prostate cancer were invited into a treatment trial. Control arm practices undertook standard UK management. All men were flagged with the Health and Social Care Information Centre for deaths and cancer registrations. The primary outcome is prostate cancer mortality at a median 10-year-follow-up.

RESULTS

Among randomised practices, 271 (68%) in the intervention arm (198,114 men) and 302 (78%) in the control arm (221,929 men) consented to participate, meeting pre-specified power requirements. There was little evidence of differences between trial arms in measured baseline characteristics of the consenting GP practices (or men within those practices).

CONCLUSIONS

The CAP trial successfully met its recruitment targets and will make an important contribution to international understanding of PSA-based prostate cancer screening.

Profiles of environmental and endogenous estrogens in the zebra mussel Dreissena polymorpha

Contamination of freshwater environments by estrogenic compounds has led to concern over potential impacts on invertebrate species. The uptake of the environmental estrogen 17beta-estradiol (E2) by the freshwater bivalve Dreissena polymorpha and the nature of estrogenic substances in tissues of D. polymorpha mussels collected from four freshwater sites were investigated. Exposure of mussels to [(14)C]-E2 (7.5 ngl(-1), 13 days) revealed that the estrogen bioconcentrated 840+/-58 (males) and 580+/-77 (females) fold (mean+/-95% confidence limits) and was metabolised in tissues to a persistent lipophilic ester. Estrogenic activity, measured using a recombinant human estrogen receptor transcription screen (YES), was detected in tissue extracts of all mussels sampled from freshwater sites. At two reference sites the estrogenic activities of mussel tissues were <1ng E2 equivalents g(-1) wet weight tissue (ng EEQ g(-1) ww) which increased to 7.4-45.7ng EEQg(-1) ww for both free and esterified estrogens extracted from hydrolysed tissue extracts. In mussels collected from two contaminated river sites, estrogenic activity was 0.2-6.7ng EEQ g(-1) ww (free estrogens) and 25.6-316.2ng EEQ g(-1) ww for total estrogens. Fractionation of the tissue extracts revealed that E2 (as the ester) was the predominant estrogen detected in both sexes of D. polymorpha, however, the xenoestrogen nonylphenol (NP) was also detected in mussels sampled from contaminated rivers. The detection of endogenous esterified E2 and the potential for accumulation of exogenous E2 and NP in D. polymorpha tissues suggests that this bivalve could be susceptible to exposure to estrogenic contaminants in the aquatic environment.

Laboratory exposure to 17beta-estradiol fails to induce vitellogenin and estrogen receptor gene expression in the marine invertebrate Mytilus edulis

To investigate the effect of estrogenic compounds on the marine mussel Mytilus edulis, an assay was developed to measure the expression of two vertebrate estrogen responsive genes-estrogen receptor (ER) and vitellogenin (VTG) genes. Expression was measured in M. edulis gonads following a 10-day exposure to 200 ng/l 17beta-estradiol (estradiol). The concentrations of esterified estradiol in mussel tissue increased 15-fold in a time-dependent manner-confirming uptake of the compound by the mussels, however there was no significant increase of free estradiol in mussel tissues during the exposure period. The ER and VTG mRNA levels in the gonads of both sexes were measured at days 1-3, 5, and 10 in control and exposed mussels. However, no significant change in the expression of either the ER or VTG genes was recorded at any of the sampled time points. The results suggest that either a regulatory mechanism exists in a mussel that is able to maintain constant levels of free estradiol by converting the excess estradiol into esterified products which may have reduced affinity for the estrogen receptor, or alternatively, that the ER and VTG genes are unresponsive to estrogens in these organisms. The significance of these findings in terms of the utility of ER and VTG as biomarkers of endocrine disruption in bivalve species is discussed.

Accounting for differences in estrogenic responses in rainbow trout (Oncorhynchus mykiss: Salmonidae) and roach (Rutilus rutilus: Cyprinidae) exposed to effluents from wastewater treatment works

Effluents from wastewater treatment works (WwTWs) contain estrogenic substances that induce feminizing effects in fish, including vitellogenin (VTG) synthesis and gonadal intersex. Fish vary in their responsiveness to estrogenic effluents, but the physiological basis for these differences are not known. In this study, uptake of estrogen from two WwTW effluents (measured in hydrolyzed bile) and estrogenic response (VTG induction) were compared in a salmonid (rainbow trout, Onchorhynchus mykiss) and a cyprinid fish (roach, Rutilus rutilus). Immature rainbow trout were more responsive than maturing roach to the estrogenic effluents. The more potent of the two estrogenic effluents (containing between 24.3 and 104.1 ng estradiol-17beta equivalents/L [E2eq/L]) resulted in a 700-fold and 240-fold induction of plasma VTG in male and female trout, respectively, but only a 4-fold induction in roach (and in males only). The less potent effluent (varying between 4.1 and 6.8 ng E2eq/L) induced VTG in the trout only, with a 4-fold and 18-fold induction in males and females, respectively. In fish exposed to tap water, the estrogenicity of the hydrolyzed bile was 0.03+/-0.01 ng E2eq/microL (for both sexes in trout), 0.18+/-0.04 ng E2eq/microL in male roach, and 0.88+/-0.15 ng E2eq/microL in female roach. The higher bile content of estrogen in control roach reflected their more advanced sexual status (and thus higher endogenous estrogen) compared with the immature female trout. In trout maintained in effluents, the bile content of estrogen was 100-fold and 30-fold higher than controls at WwTW A and B, respectively. Bioconcentration factors (BCFs) for estrogenic activity in bile were between 16 344 and 46 134 in trout and between 3543 and 60 192 in roach (no gender differences were apparent). There were strong correlations between VTG induction and the estrogenic activity of bile extracts for both trout and roach. The results confirm that estrogenic contaminants bioconcentrate to a high degree in fish bile and that the level (and nature) of this accumulation may accountfor responsiveness to the endocrine disruptive effects of estrogenic effluents. Immature fish were the more appropriate life stage for quantifying estrogen exposure and uptake in bile, as they contain little circulating endogenous oestrogen compared with sexual maturing fish. The nature of the estrogenic contaminants is detailed in an accompanying paper.