Marleen Werkman

The effectiveness of fallowing strategies in disease control in salmon aquaculture assessed with an SIS model

Salmon production is an important industry in Scotland, with an estimated retail value >£1 billion. However, this salmon industry can be threatened by the invasion and spread of diseases. To reduce this risk, the industry is divided into management areas that are physically separated from each other. Pathogens can spread between farms by local processes such as water movement or by long-distance processes such as live fish movements. Here, network modelling was used to investigate the importance of transmission routes at these two scales. We used different disease transmission rates (β), where infected farms had the probability of 0.10, 0.25 or 0.50 per month to infect each contacted farm. Interacting farms were modelled in such a way that neighbours within a management area could infect each other, resulting in two contacts per farm per month. In addition, non-local transmission occurred at random. Salmon are input to marine sites where they are raised to harvest size, the site is then fallowed; in the model the effects of different fallowing strategies (synchronised, partial synchronised and unsynchronised fallowing at the management area level) on the emergence of diseases were investigated. Synchronised fallowing was highly effective at eradicating epidemics when transmission rate is low (β=0.10) even when long distance contacts were fairly common (up to 1.5farm(-1)month(-1)). However for higher transmission rates, long distance contacts have to be kept at much lower levels (0.15contactsmonth(-1) where β=0.25) when synchronised fallowing was applied. If fallowing was partially synchronised or unsynchronised then low rates of long-distance contact are required (0.75 or 0.15farm(-1)month(-1)) even if β=0.10. These results demonstrate the potential benefits of having epidemiologically isolated management areas and applying synchronised fallowing.

The impact of movements and animal density on continental scale cattle disease outbreaks in the United States.

Globalization has increased the potential for the introduction and spread of novel pathogens over large spatial scales necessitating continental-scale disease models to guide emergency preparedness. Livestock disease spread models, such as those for the 2001 foot-and-mouth disease (FMD) epidemic in the United Kingdom, represent some of the best case studies of large-scale disease spread. However, generalization of these models to explore disease outcomes in other systems, such as the United States’s cattle industry, has been hampered by differences in system size and complexity and the absence of suitable livestock movement data. Here, a unique database of US cattle shipments allows estimation of synthetic movement networks that inform a near-continental scale disease model of a potential FMD-like (i.e., rapidly spreading) epidemic in US cattle. The largest epidemics may affect over one-third of the US and 120,000 cattle premises, but cattle movement restrictions from infected counties, as opposed to national movement moratoriums, are found to effectively contain outbreaks. Slow detection or weak compliance may necessitate more severe state-level bans for similar control. Such results highlight the role of large-scale disease models in emergency preparedness, particularly for systems lacking comprehensive movement and outbreak data, and the need to rapidly implement multi-scale contingency plans during a potential US outbreak.

Decision-making for foot-and-mouth disease control: objectives matter

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Epidemic predictions in an imperfect world: modelling disease spread with partial data

‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

The potential for targeted surveillance of live fish movements in Scotland

The network structure of the movements of live fish in the Scottish aquaculture industry has recently been demonstrated for 2003. In this paper, we enlarge this analysis to a longer 3-year period from 2002 to 2004, the new data allowing complete coverage of at least one production cycle. The resulting network contains slightly more sites than that for a single year and is denser with more arcs (directed site-to-site connections) present, but otherwise features recognizable in the 1-year network are still recognizable in the 3-year network. Arc-removal algorithms (a proxy for targeted surveillance) were identified that could successfully reduce the portion of the network reachable from a node (a proxy for potential epidemic size) by approximately one-third by removing as few as four arcs. This results from the high centrality of particular nodes and arcs. A strong community structure was identified in the network, corresponding with species farmed, but only weakly geographical, with a high proportion of arcs occurring between management areas and catchments.

Seasonality and heterogeneity of live fish movements in Scottish fish farms

Movement of live animals is a key contributor to disease spread. Farmed Atlantic salmon Salmo salar, rainbow trout Onchorynchus mykiss and brown/sea trout Salmo trutta are initially raised in freshwater (FW) farms; all the salmon and some of the trout are subsequently moved to seawater (SW) farms. Frequently, fish are moved between farms during their FW stage and sometimes during their SW stage. Seasonality and differences in contact patterns across production phases have been shown to influence the course of an epidemic in livestock; however, these parameters have not been included in previous network models studying disease transmission in salmonids. In Scotland, farmers are required to register fish movements onto and off their farms; these records were used in the present study to investigate seasonality and heterogeneity of movements for each production phase separately for farmed salmon, rainbow trout and brown/sea trout. Salmon FW-FW and FW-SW movements showed a higher degree of heterogeneity in number of contacts and different seasonal patterns compared with SW-SW movements. FW-FW movements peaked from May to July and FW-SW movements peaked from March to April and from October to November. Salmon SW-SW movements occurred more consistently over the year and showed fewer connections and number of repeated connections between farms. Therefore, the salmon SW-SW network might be treated as homogeneous regarding the number of connections between farms and without seasonality. However, seasonality and production phase should be included in simulation models concerning FW-FW and FW-SW movements specifically. The number of rainbow trout FW-FW and brown/sea trout FW-FW movements were different from random. However, movements from other production phases were too low to discern a seasonal pattern or differences in contact pattern.

Identifying optimal threshold statistics for elimination of hookworm using a stochastic simulation model

BackgroundThere is an increased focus on whether mass drug administration (MDA) programmes alone can interrupt the transmission of soil-transmitted helminths (STH). Mathematical models can be used to model these interventions and are increasingly being implemented to inform investigators about expected trial outcome and the choice of optimum study design. One key factor is the choice of threshold for detecting elimination. However, there are currently no thresholds defined for STH regarding breaking transmission.MethodsWe develop a simulation of an elimination study, based on the DeWorm3 project, using an individual-based stochastic disease transmission model in conjunction with models of MDA, sampling, diagnostics and the construction of study clusters. The simulation is then used to analyse the relationship between the study end-point elimination threshold and whether elimination is achieved in the long term within the model. We analyse the quality of a range of statistics in terms of the positive predictive values (PPV) and how they depend on a range of covariates, including threshold values, baseline prevalence, measurement time point and how clusters are constructed.ResultsEnd-point infection prevalence performs well in discriminating between villages that achieve interruption of transmission and those that do not, although the quality of the threshold is sensitive to baseline prevalence and threshold value. Optimal post-treatment prevalence threshold value for determining elimination is in the range 2% or less when the baseline prevalence range is broad. For multiple clusters of communities, both the probability of elimination and the ability of thresholds to detect it are strongly dependent on the size of the cluster and the size distribution of the constituent communities. Number of communities in a cluster is a key indicator of probability of elimination and PPV. Extending the time, post-study endpoint, at which the threshold statistic is measured improves PPV value in discriminating between eliminating clusters and those that bounce back.ConclusionsThe probability of elimination and PPV are very sensitive to baseline prevalence for individual communities. However, most studies and programmes are constructed on the basis of clusters. Since elimination occurs within smaller population sub-units, the construction of clusters introduces new sensitivities for elimination threshold values to cluster size and the underlying population structure. Study simulation offers an opportunity to investigate key sources of sensitivity for elimination studies and programme designs in advance and to tailor interventions to prevailing local or national conditions.

Ensemble modelling and structured decision-making to support Emergency Disease Management.

Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application.

Prospects for elimination of soil-transmitted helminths.

Purpose of review Soil-transmitted helminths (STH) are endemic in 120 countries and are associated with substantial morbidity and loss of economic productivity. Although current WHO guidelines focus on morbidity control through mass drug administration (MDA), there is global interest in whether a strategy targeting disease elimination might be feasible in some settings. This review summarizes the prospects for switching from control to an elimination strategy. Recent findings STH control efforts have reduced the intensity of infections in targeted populations with associated reductions in morbidity. However, adults are not frequently targeted and remain important reservoirs for reinfection of treated children. Recent modeling suggests that transmission interruption may be possible through expanded community-wide delivery of MDA, the feasibility of which has been demonstrated by other programs. However, these models suggest that high levels of coverage and compliance must be achieved. Potential challenges include the risk of prematurely dismantling STH programs and the potential increased risk of antihelminthic resistance. Summary Elimination of STH may offer an opportunity to eliminate substantial STH-related morbidity while reducing resource needs of neglected tropical disease programs. Evidence from large community trials is needed to determine the feasibility of interrupting the transmission of STH in some geographic settings.

Current epidemiological evidence for predisposition to high or low intensity human helminth infection: a systematic review

BackgroundThe human helminth infections include ascariasis, trichuriasis, hookworm infections, schistosomiasis, lymphatic filariasis (LF) and onchocerciasis. It is estimated that almost 2 billion people worldwide are infected with helminths. Whilst the WHO treatment guidelines for helminth infections are mostly aimed at controlling morbidity, there has been a recent shift with some countries moving towards goals of disease elimination through mass drug administration, especially for LF and onchocerciasis. However, as prevalence is driven lower, treating entire populations may no longer be the most efficient or cost-effective strategy. Instead, it may be beneficial to identify individuals or demographic groups who are persistently infected, often termed as being “predisposed” to infection, and target treatment at them.MethodsThe authors searched Embase, MEDLINE, Global Health, and Web of Science for all English language, human-based papers investigating predisposition to helminth infections published up to October 31st, 2017. The varying definitions used to describe predisposition, and the statistical tests used to determine its presence, are summarised. Evidence for predisposition is presented, stratified by helminth species, and risk factors for predisposition to infection are identified and discussed.ResultsIn total, 43 papers were identified, summarising results from 34 different studies in 23 countries. Consistent evidence of predisposition to infection with certain species of human helminth was identified. Children were regularly found to experience greater predisposition to Ascaris lumbricoides, Schistosoma mansoni and S. haematobium than adults. Females were found to be more predisposed to A. lumbricoides infection than were males. Household clustering of infection was identified for A. lumbricoides, T. trichiura and S. japonicum. Ascaris lumbricoides and T. trichiura also showed evidence of familial predisposition. Whilst strong evidence for predisposition to hookworm infection was identified, findings with regards to which groups were affected were considerably more varied than for other helminth species.ConclusionThis review has found consistent evidence of predisposition to heavy (and light) infection for certain human helminth species. However, further research is needed to identify reasons for the reported differences between demographic groups. Molecular epidemiological methods associated with whole genome sequencing to determine ‘who infects whom’ may shed more light on the factors generating predisposition.