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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 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 groupings into a mathematical livestock disease model, we explored how heterogeneity in behaviour impacts epidemiological 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 health economic measures.

Increasing levels of antibiotic resistance in many bacterial pathogen populations is a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria is exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset ofNeisseria gonorrhoeaegenomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

BackgroundThere has been concern throughout the COVID-19 pandemic over highly stressed healthcare capacities being further pressured by seasonal influenza epidemics. Interventions to tackle the spread of SARS-CoV-2, the causative agent of COVID-19, have unsettled the respiratory pathogen landscape, including worldwide patterns of influenza activity. The implications of these disruptions for subsequent influenza seasons has been uncertain.MethodsTo conduct scenario analyses ahead of the 2020/2021 and 2021/2022 influenza seasons in England, we used a pre-existing age-structured, multi-strain compartmental model of influenza transmission and case severity, which included propagation of immunity between influenza seasons and had been previously fit to historical data. For the pre-2020/2021 influenza season, our scenarios varied the level of vaccine uptake and the inclusion/exclusion of nonpharmaceutical interventions (NPIs). We estimated the relative amount of health episode occurrences: symptomatic cases resulting in a GP consultation, hospital inpatient admissions, fatalities. In the pre-2021/2022 influenza season analysis, compared with a counterfactual case where influenza activity remained at historic levels in the 2020/2021 influenza season, we estimated the change in the same set of health episode occurrences in the 2021/2022 influenza season when assuming there was no influenza in circulation during the 2020/2021 influenza season.ResultsAttaining coverage of 75% in target groups for the 2020/2021 influenza season reduced health episode occurrences by 40–50% when compared to maintaining the 2019/2020 vaccination programme coverage and uptake levels. Having NPIs maintained throughout the entire influenza season saw 60-80% reductions in severe case outcomes. Combining an expanded vaccination programme and the use of NPIs could suppress the seasonal influenza epidemic, with reductions as much as 90–100%. In the absence of influenza transmission during the 2020/2021 influenza season, under our modelling assumption of mixing patterns returning to pre-2020 levels we projected a compensatory influenza epidemic with 1.2 to 2.2 times as many severe health episode occurrences in the subsequent 2021/2022 influenza season.ConclusionsIn the context of the time the work was originally conducted, the modelled scenarios indicated how bolstering vaccine coverage and reduction in contacts could likely allay resurgent seasonal influenza epidemics. Our analyses of the winter pressures that may be inflicted by other respiratory infections during the COVID-19 pandemic are one example of the modelling insights provided to the Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) for the Scientific Advisory Group for Emergencies (SAGE) in the UK.

Since May 2022, monkeypox virus (MPXV) has spread rapidly in high-income countries through close human-to-human contact primarily amongst communities of gay, bisexual and men who have sex with men (GBMSM). Behavioural change arising from increased knowledge and health warnings may have reduced the rate of transmission and Vaccinia-based vaccination is likely to be an effective longer-term intervention. We investigate the UK epidemic presenting 26-week projections using a stochastic discrete-population transmission model which includes GBMSM status, rate of formation of new sexual partnerships, and clique partitioning of the population. We find that MPXV cases peaked in mid-July, declining due to decreased transmission rate per infected individual and infection-induced immunity among GBMSM, especially those with the highest rate of new partners. We predict cases will remain low from October 2022 to March 2023 (20-65 cases a week), and a rebound in cases due to behaviour reversion prevented by high-risk group-targeted vaccination.

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

In late 2020, the JCVI (the Joint Committee on Vaccination and Immunisation, which provides advice to the Department of Health and Social Care, England) made two important recommendations for the initial roll-out of the COVID-19 vaccine. The first was that vaccines should be targeted to the elderly and vulnerable, with the aim of maximally preventing disease rather than infection. The second was to increase the interval between first and second doses from 3 to 12 weeks. Here, we re-examine these recommendations through a mathematical model of SARS-CoV-2 infection in England. We show that targeting the most vulnerable had the biggest immediate impact (compared to targeting younger individuals who may be more responsible for transmission). The 12-week delay was also highly beneficial, estimated to have averted between 32-72 thousand hospital admissions and 4-9 thousand deaths over the first ten months of the campaign (December 2020 - September 2021) depending on the assumed interaction between dose interval and efficacy.

One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics.

BackgroundChildren and young persons are known to have a high number of close interactions, often within the school environment, which can facilitate rapid spread of infection; yet for SARS-CoV-2 it is the elderly and vulnerable that suffer the greatest health burden. Vaccination, initially targeting the elderly and vulnerable but later expanded to the entire adult population, has been transformative in the control of SARS-CoV-2 in England. However, early concerns over adverse events and the lower risk associated with infection in younger individuals means that the expansion of the vaccine programme to those under 18 year of age needs to be rigorously and quantitatively assessed.MethodsHere, using a bespoke mathematical model matched to case and hospital data for England, we consider the potential impact of vaccinating 12-17 and 5-11 year olds. This analysis is reported from an early model (generated in June 2021) that formed part of the evidence base for the decisions in England, and a later model (from November 2021) that benefits from a richer understanding of vaccine efficacy, greater knowledge of the Delta variant wave and uses data on the rate of vaccine administration. For both models we consider the population wide impact of childhood vaccination as well as the specific impact on the age-group targeted for vaccination.ResultsProjections from June suggested that an expansion of the vaccine programme to those 12-17 years old could generate substantial reductions in infection, hospital admission and deaths in the entire population, depending on population behaviour following the relaxation of control measures. The benefits within the 12-17 year old cohort were less marked, saving between 656 and 1077 (95% prediction interval 281-2260) hospital admissions and between 22 and 38 (95% PI 9-91) deaths depending on assumed population behaviour. For the more recent model, the benefits within this age group are reduced, saving on average 631 (95% PI 304-1286) hospital admissions and 11 (95% PI 5-28) deaths for 80% vaccine uptake, while the benefits to the wider population represent a reduction of 8-10% in hospital admissions and deaths. The vaccination of 5-11 year olds is projected to have a far smaller impact, in part due to the later roll-out of vaccines to this age-group.ConclusionsVaccination of 12-17 year olds and 5-11 year olds is projected to generate a reduction in infection, hospital admission and deaths for both the age-groups involved and the population in general. For any decision involving childhood vaccination, these benefits needs to be balanced against potential adverse events from the vaccine, the operational constraints on delivery and the potential for diverting resources from other public health campaigns.

Background: Vector control is a vital tool utilised by malaria control and elimination programmes worldwide, and as such it is important that we can accurately quantify the expected public health impact of a range of vector control methods. There are very few previous models that consider vector control induced changes in the age-structure of the vector population and the resulting impact this will have on transmission.Methods: The steady-state solution of a novel age-structured deterministic compartmental model describing the mosquito gonotrophic cycle is analytically derived, with the age of each mosquito measured in the number of gonotrophic cycles (or successful blood meals) completed. From this model we derive analytical expressions for key transmission measures, such as the effective reproductive ratio under control, Rc, and investigate the impact of combinations of commonly used vector control methods on the age-structure of the vector population.Results: Our model output is an explicit solution that can be used to directly quantify key transmission statistics and investigate the age-structured impact of vector control. Application of this model confirms current knowledge thatadult-acting interventions, such as IRS or LLINs, can be highly effective at reducing transmission, due to the dual effects of repelling and killing mosquitoes. However, we demonstrate how larval measures can be implemented in addition to adult-acting measures to reduce Rc and mitigate the impact of waning insecticidal efficacy. We also find that mid-ranges of LLIN coverage see the largest effect of reduced net integrity on transmission.Conclusions: Whilst well-maintain adult-acting vector control measures are substantially more effective than larval-based interventions, incorporating larval control in existing LLIN or IRS programmes could substantially reduce transmission. This would most benefit areas with low coverage or poor maintenance of interventions.

Understanding how disease spreads through populations is important when designing and implementing control measures. MetaWards implements a stochastic metapopulation modelof disease transmission that enables geographical modelling of disease spread that can scaleall the way from modelling local transmission up to full national- or international-scale outbreaks. It is built in Python and has a flexible plugin architecture to support complex scenariomodelling. This enables the code to be adapted to model new situations and new controlmeasures as they arise, e.g. emergence of new variants of disease, enaction of different typesof movement restrictions, availability of different types of vaccines etc. It implements a userdefinable compartmental transmission model, such as an SIR model, that can be extendedmulti-dimensionally via multiple demographics or sub-populations, and multiple geographicalregions. Models can be constructed from the various sources of movement and demographicdata that are available, and are accelerated via Cython (Behnel et al., 2020), OpenMP, Scoop(Hold & Gagnon, 2019) and MPI4Py (Dalcin & Fang, 2021) to scale efficiently from runningon personal laptops to large supercomputers. Python, R and command line interfaces and acomplete set of tutorials empower researchers to adapt their models to a variety of scenarios.

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.

Several strains of simian immunodeficiency virus (SIV), including uncloned and molecularly cloned SIV strains, can cross intact mucosal surfaces after oral exposure in both adult and neonatal rhesus macaques, resulting in viremia and disease. Cell-free SIV strains as well as infected whole blood have resulted in systemic infection after oral inoculation. Neonatal macaques, exposed orally to the chimeric SHIV-vpu+, a derivative of SIVmac239 that encodes the env gene of the T cell-tropic HIV-IIIB, have also become persistently infected. These data indicate that oral exposure to various virus strains, including T cell-tropic variants, leads to infection. After nontraumatic inoculation, the oral route was more efficient than the rectal route in permitting SIV entry in adult macaques. Infection and AIDS resulting from oral exposure of adult macaques have implications for the transmission of the human immunodeficiency virus type 1 (HIV-1) during oral-genital contact.

A study by Courtenay and others (2006) demonstrated that\ud the probability of detecting Mycobacterium bovis by PCR in\ud soil samples from the spoil heaps of main badger setts correlated\ud with the prevalence of excretion (infectiousness) of\ud captured badgers belonging to the social group. It has been\ud proposed that such a test could be used to target badger culling\ud to setts containing infectious animals (Anon 2007). This\ud short communication discusses the issues surrounding this\ud concept, with the intention of dispelling any misconceptions\ud among relevant stakeholders (farmers, policy makers and\ud conservationists).