Rift Valley fever (RVF) is an emerging disease with devastating impacts on livestock health and livelihoods. The risk of RVF virus (RVFV) emergence in new regions and the effectiveness of a strategy for preventing establishment are impacted by how infection persists at local scales. Multiple mechanisms have been proposed for its persistence in regions prone to epidemics, including maintenance via transovarial transmission (TOT) but whether and how TOT can support local persistence is not well understood. Through the development of host- and multi-vector climate-driven simulation models to recreate observed patterns of prevalence and outbreak frequency, we show that TOT has the potential to play an important role in local persistence through seasonal cold or dry periods. Local persistence required annual low-level transmission of RVFV concurrently with substantial TOT, whereas the infrequent large outbreaks hampered long-term persistence in our simulations. We show that under this mode of local persistence, large outbreaks can be prevented with low-level vaccination, but that the long-term local persistence can only be interrupted with many years of sustained vaccination. Determining the role of TOT in persistence is critical for designing countermeasures to prevent establishment after emergence.

BACKGROUND:Genetic selection of individuals that are less susceptible to infection, less infectious once infected, and recover faster, offers an effective and long-lasting solution to reduce the incidence and impact of infectious diseases in farmed animals. However, computational methods for simultaneously estimating genetic parameters for host susceptibility, infectivity and recoverability from real-word data have been lacking. Our previously developed methodology and software tool SIRE 1.0 (Susceptibility, Infectivity and Recoverability Estimator) allows estimation of host genetic effects of a single nucleotide polymorphism (SNP), or other fixed effects (e.g. breed, vaccination status), for these three host traits using individual disease data typically available from field studies and challenge experiments. SIRE 1.0, however, lacks the capability to estimate genetic parameters for these traits in the likely case of underlying polygenic control. RESULTS:This paper introduces novel Bayesian methodology and a new software tool SIRE 2.0 for estimating polygenic contributions (i.e. variance components and additive genetic effects) for host susceptibility, infectivity and recoverability from temporal epidemic data, assuming that pedigree or genomic relationships are known. Analytical expressions for prediction accuracies (PAs) for these traits are derived for simplified scenarios, revealing their dependence on genetic and phenotypic variances, and the distribution of related individuals within and between contact groups. PAs for infectivity are found to be critically dependent on the size of contact groups. Validation of the methodology with data from simulated epidemics demonstrates good agreement between numerically generated PAs and analytical predictions. Genetic correlations between infectivity and other traits substantially increase trait PAs. Incomplete data (e.g. time censored or infrequent sampling) generally yield only small reductions in PAs, except for when infection times are completely unknown, which results in a substantial reduction. CONCLUSIONS:The method presented can estimate genetic parameters for host susceptibility, infectivity and recoverability from individual disease records. The freely available SIRE 2.0 software provides a valuable extension to SIRE 1.0 for estimating host polygenic effects underlying infectious disease transmission. This tool will open up new possibilities for analysis and quantification of genetic determinates of disease dynamics.

For the last two decades, the human infection frequency of Escherichia coli O157 (O157) in Scotland has been 2.5-fold higher than in England and Wales. Results from national cattle surveys conducted in Scotland and England and Wales in 2014/2015 were combined with data on reported human clinical cases from the same time frame to determine if strain differences in national populations of O157 in cattle could be associated with higher human infection rates in Scotland. Shiga toxin subtype (Stx) and phage type (PT) were examined within and between host (cattle vs human) and nation (Scotland vs England and Wales). For a subset of the strains, whole genome sequencing (WGS) provided further insights into geographical and host association. All three major O157 lineages (I, II, I/II) and most sub-lineages (Ia, Ib, Ic, IIa, IIb, IIc) were represented in cattle and humans in both nations. While the relative contribution of different reservoir hosts to human infection is unknown, WGS analysis indicated that the majority of O157 diversity in human cases was captured by isolates from cattle. Despite comparable cattle O157 prevalence between nations, strain types were localized. PT21/28 (sub-lineage Ic, Stx2a+) was significantly more prevalent in Scottish cattle [odds ratio (OR) 8.7 (2.3–33.7; P <0.001] and humans [OR 2.2 (1.5–3.2); P <0.001]. In England and Wales, cattle had a significantly higher association with sub-lineage IIa strains [PT54, Stx2c; OR 5.6 (1.27–33.3); P =0.011] while humans were significantly more closely associated with sub-lineage IIb [PT8, Stx1 and Stx2c; OR 29 (4.9–1161); P <0.001]. Therefore, cattle farms in Scotland were more likely to harbour Stx2a+O157 strains compared to farms in E and W ( P <0.001). There was evidence of limited cattle strain migration between nations and clinical isolates from one nation were more similar to cattle isolates from the same nation, with sub-lineage Ic (mainly PT21/28) exhibiting clear national association and evidence of local transmission in Scotland. While we propose the higher rate of O157 clinical cases in Scotland, compared to England and Wales, is a consequence of the nationally higher level of Stx2a+O157 strains in Scottish cattle, we discuss the multiple additional factors that may also contribute to the different infection rates between these nations.

Rift Valley fever virus (RVFV) has spread beyond continental Africa and threatens to follow West Nile, chikungunya and Zika viruses into the Americas. Its impact in new localities and the capacity to control future outbreaks, depends on whether and how RVFV persists at small spatial scales. Transovarial transmission (TOT) is hypothesized as an important mechanism for local persistence, yet its role in RVFV ecology remains poorly understood. We examine whether RVFV can persist locally via TOT while maintaining a realistic seroprevalence pattern of interepidemic and epidemic transmission. We developed a mechanistic, compartmental model of RVFV dynamics within a single host (sheep) and two vector (mosquito) populations, driven by temperate climatic factors. Decades-long persistence was possible in our simulations, which generally captured the observed outbreak patterns in central South Africa with a mean annual seroprevalence (∼23%) within the range reported during interepidemic periods (5-40%). Persistence was only possible with a substantial TOT fraction and over a narrow range of parameters. The basic reproduction number ( R 0 ) was close to one at mean vector population sizes, suggesting a relatively limited expansion of the infected vector population during outbreaks. This limited expansion provides the system with the flexibility to support both low-level transmission and large outbreaks and, counterintuitively, large outbreaks resulted in smaller infected Aedes egg populations. This has important consequences for control: low-level vaccination may prevent large outbreaks without eliminating RVFV and local control efforts may be most effective immediately following an outbreak, suggesting elimination may be possible after emergence in temperate regions.

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Culling wildlife to control disease can lead to both decreases and increases in disease levels, with apparently conflicting responses observed, even for the same wildlife-disease system. There is therefore a pressing need to understand how culling design and implementation influence culling's potential to achieve disease control. We address this gap in understanding using a spatial metapopulation model representing wildlife living in distinct groups with density-dependent dispersal and framed on the badger-bovine tuberculosis (bTB) system. We show that if population reduction is too low, or too few groups are targeted, a 'perturbation effect' is observed, whereby culling leads to increased movement and disease spread. We also demonstrate the importance of culling across appropriate time scales, with otherwise successful control strategies leading to increased disease if they are not implemented for long enough. These results potentially explain a number of observations of the dynamics of both successful and unsuccessful attempts to control TB in badgers including the Randomized Badger Culling Trial in the UK, and we highlight their policy implications. Additionally, for parametrizations reflecting a broad range of wildlife-disease systems, we characterize 'Goldilocks zones', where, for a restricted combination of culling intensity, coverage and duration, the disease can be reduced without driving hosts to extinction.

Anthelmintic resistance is a threat to global food security. In order to alleviate the selection pressure for resistance and maintain drug efficacy, management strategies increasingly aim to preserve a proportion of the parasite population in ‘refugia’, unexposed to treatment. While persuasive in its logic, and widely advocated as best practice, evidence for the ability of refugia-based approaches to slow the development of drug resistance in parasitic helminths is currently limited. Moreover, the conditions needed for refugia to work, or how transferable those are between parasite-host systems, are not known. This review, born of an international workshop, seeks to deconstruct the concept of refugia and examine its assumptions and applicability in different situations. We conclude that factors potentially important to refugia, such as the fitness cost of drug resistance, the degree of mixing between parasite sub-populations selected through treatment or not, and the impact of parasite life-history, genetics and environment on the population dynamics of resistance, vary widely between systems. The success of attempts to generate refugia to limit anthelmintic drug resistance are therefore likely to be highly dependent on the system in hand. Additional research is needed on the concept of refugia and the underlying principles for its application across systems, as well as empirical studies within systems that prove and optimise its usefulness.

Over-dispersed count data typically pose a challenge to analysis using standard statistical methods, particularly when evaluating the efficacy of an intervention through the observed effect on the mean. We outline a novel statistical method for analysing such data, along with a statistically coherent framework within which the observed efficacy is assigned one of four easily interpretable classifications relative to a target efficacy: "adequate", "reduced", "borderline" or "inconclusive". We illustrate our approach by analysing the anthelmintic efficacy of mebendazole using a dataset of egg reduction rates relating to three intestinal parasites from a treatment arm of a randomised controlled trial involving 91 children on Pemba Island, Tanzania. Numerical validation of the type I error rates of the novel method indicate that it performs as well as the best existing computationally-simple method, but with the additional advantage of providing valid inference in the case of an observed efficacy of 100%. The framework and statistical analysis method presented also allow the required sample size of a prospective study to be determined via simulation. Both the framework and method presented have high potential utility within medical parasitology, as well as other fields where over-dispersed count datasets are commonplace. In order to facilitate the use of these methods within the wider medical community, user interfaces for both study planning and analysis of existing datasets are freely provided along with our open-source code via: http://www.fecrt.com/framework

Livestock disease controls are often linked to movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of herd-to-herd transmission. Household models of human infections make use of R, the number of groups infected by an initial infected group, which is a metapopulation level analogue of the basic reproduction number R-0 that provides a better characterization of disease spread in a metapopulation. However, existing approaches to calculate R do not account for individual movements between locations which means we lack suitable tools for livestock systems. We address this gap using next-generation matrix approaches to capture movements explicitly and introduce novel tools to calculate R in any populations coupled by individual movements. We show that depletion of infectives in the source group, which hastens its recovery, is a phenomenon with important implications for design and efficacy of movement-based controls. Underpinning our results is the observation that R peaks at intermediate livestock movement rates. Consequently, under movement-based controls, infection could be controlled at high movement rates but persist at intermediate rates. Thus, once control schemes are present in a livestock system, a reduction in movements can counterintuitively lead to increased disease prevalence. We illustrate our results using four important livestock diseases (bovine viral diarrhoea, bovine herpes virus, Johne's disease and Escherichia coli O157) that each persist across different movement rate ranges with the consequence that a change in livestock movements could help control one disease, but exacerbate another.

Livestock disease can be tackled through a number of routes including on farm vaccination programs, biosecurity, herd health schemes (which restrict animal movements to between accredited farms), testing of animals when they move between farms, and quarantining of new arrivals on farms. Designing the most effective strategies benefits from accurate assessment of the herd-to-herd spread of disease. However, the capacity for disease transmission with populations is frequently measured using the basic reproduction number, R0, which provides a poor measure of disease spread in a highly structured population, for example a population of cattle herds, and says little about between-group transmission dynamics. Here we discuss the properties of an analogous value for between-group transmission, Rpop, which is the expected number of secondary groups infected while the disease persists in the primary group. An important result is that when disease transmission is driven by movement of infective individuals, rather than by between-group contact, then the movement acts to deplete numbers of infectives in the primary group, and hasten its recovery. This feature distinguishes our formulation from the usual phenomenological models of group-to-group transmission. Moreover, it has important implications for movement-based control options (e.g. testing and treating, or quarantining animals when they move between herds) and therefore for optimal disease control strategies. Using structured metapopulation models, we show that Rpop peaks at intermediate movement rates. In combination with testing and treating prior to movement, or post movement quarantine; this can lead to islands of movement rates that permit the disease to persist. Under such control schemes, a reduction in movement rates can, counter-intuitively, lead to an increase in disease prevalence. Examining the effects of heterogeneity in herd size, herd movement rates, and individual infectiousness, we show that metapopulation disease spread is dependent on within-farm dynamics. We explore the implications of these results for disease problems (e.g. BVD, bTB, E. coli O157) with different characteristic R0, infectious periods and movement rates.

Population reduction is often used as a control strategy when managing infectious diseases in wildlife populations in order to reduce host density below a critical threshold. However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission. Such effects have been observed in several disease systems, notably badgers and bovine tuberculosis. Here we characterise the fundamental properties of disease systems for which such effects undermine the disease control benefits of population reduction. By quantifying the size of response to population reduction in terms of enhanced transmission within a generic non-spatial model, the properties of disease systems in which such effects reduce or even reverse the disease control benefits of population reduction are identified. If population reduction is not sufficiently severe, then enhanced transmission can lead to the counter intuitive perturbation effect, whereby disease levels increase or persist where they would otherwise die out. Perturbation effects are largest for systems with low levels of disease, e.g. low levels of endemicity or emerging disease. Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions. Moreover, enhanced transmission itself is found to arise as an emergent property of density dependent dispersal in such systems. This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species. Our results suggest that the impact of population reduction on social and demographic structures is likely to undermine disease control in many systems, and in severe cases leads to the perturbation effect. Social and demographic mechanisms that enhance transmission following population reduction should therefore be routinely considered when designing control programmes.