Estimating blue mussel (Mytilus edulis) connectivity and settlement capacity in mid-latitude fjord regions.

The mussel industry faces challenges such as low and inconsistent levels of larvae settlement and poor-quality spat, leading to variable production. However, mussel farming remains a vital sustainable and environmentally responsible method for producing protein, fostering ecological responsibility in the aquaculture sector. We investigate the population connectivity and larval dispersion of blue mussels (Mytilus edulis) in Scottish waters, as a case study, using a multidisciplinary approach that combined genetic data and particle modelling. This research allows us to develop a thorough understanding of blue mussel population dynamics in mid-latitude fjord regions, to infer gene-flow patterns, and to estimate population divergence. Our findings reveal a primary south-to-north particle transport direction and the presence of five genetic clusters. We discover a significant and continuous genetic material exchange among populations within the study area, with our biophysical model's outcomes aligning with our genetic observations. Additionally, our model reveals a robust connection between the southwest coast and the rest of the west coast. This study will guide the preservation of mussel farming regions, ensuring sustainable populations that contribute to marine ecosystem health and resilience.

When and why direct transmission models can be used for environmentally persistent pathogens.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

Quantifying the roles of host movement and vector dispersal in the transmission of vector-borne diseases of livestock.

The role of host movement in the spread of vector-borne diseases of livestock has been little studied. Here we develop a mathematical framework that allows us to disentangle and quantify the roles of vector dispersal and livestock movement in transmission between farms. We apply this framework to outbreaks of bluetongue virus (BTV) and Schmallenberg virus (SBV) in Great Britain, both of which are spread by Culicoides biting midges and have recently emerged in northern Europe. For BTV we estimate parameters by fitting the model to outbreak data using approximate Bayesian computation, while for SBV we use previously derived estimates. We find that around 90% of transmission of BTV between farms is a result of vector dispersal, while for SBV this proportion is 98%. This difference is a consequence of higher vector competence and shorter duration of viraemia for SBV compared with BTV. For both viruses we estimate that the mean number of secondary infections per infected farm is greater than one for vector dispersal, but below one for livestock movements. Although livestock movements account for a small proportion of transmission and cannot sustain an outbreak on their own, they play an important role in establishing new foci of infection. However, the impact of restricting livestock movements on the spread of both viruses depends critically on assumptions made about the distances over which vector dispersal occurs. If vector dispersal occurs primarily at a local scale (99% of transmission occurs <25 km), movement restrictions are predicted to be effective at reducing spread, but if dispersal occurs frequently over longer distances (99% of transmission occurs <50 km) they are not.

Time-to-response toxicity analysis as a method for drug susceptibility assessment in salmon lice.

The salmon louse Lepeophtheirus salmonis (Krøyer, 1837) is an ectoparasite causing infections of wild and farmed Atlantic salmon (Salmo salar L.) in the Northern hemisphere. While L. salmonis control at commercial mariculture sites increasingly employs non-medicinal approaches, such as cage designs reducing infection rates and biological control through cleaner fish, anti-parasitic drugs are still a requirement for effective fish health care. With only a limited range of salmon delousing agents available, all of which have been in use for more than a decade, drug resistance formation has been reported for different products. Successful resistance management requires reliable susceptibility assessment, which is usually achieved through L. salmonis bioassays. These tests involve the exposure of parasites to different drug concentrations and require significant numbers of suitable L. salmonis stages. The present study reports an alternative bioassay that is based on time-to-response toxicity analyses and can be carried out with limited parasite numbers. The assay determines the median effective time (ET50), i.e., the time required until impaired swimming and/or attachment behaviour becomes apparent in 50% of parasites, by conducting repeated examinations of test animals starting at the time point where exposure to a set drug concentration commences. This experimental approach further allows the estimation of the apparent drug susceptibility of individual L. salmonis by determining their time to response, which may prove useful in experiments designed to elucidate associations between genetic factors and the drug susceptibility phenotype of parasites. Three laboratory strains of L. salmonis differing in susceptibility to emamectin benzoate were characterised using standard 24 h bioassays and time-to-response toxicity assays. While both the median effective concentration (EC50) and the ET50 showed variability between experimental repeats, both types of bioassay consistently discriminated susceptible and drug-resistant L. salmonis laboratory strains. STATEMENT OF RELEVANCE: Infections by sea lice cause significant costs to the global salmon farming industry, which have been estimated to exceed €300 million per year worldwide. Control of sea lice still relies to a significant extent on chemical delousing; however, chemical control is threatened by resistance formation. Resistance can be combated by rotation between different drugs and strategic implementation of non-medicinal strategies. However, resistance management requires reliable and feasible methods of susceptibility assessment. The present study is a technical note introducing a novel approach to susceptibility assessments in sea lice. The method can be applied in susceptibility assessments on farms, where it offers the advantage of a reduced requirement of parasites for testing. In addition, the novel method allows deriving the times of parasite require to show a response after drug treatment has started, thus providing a variable characterizing the drug susceptibility phenotype of individual parasites. Accordingly, the bioassay approach presented here will be useful for studies aiming at unravelling the genetic determinants of drug resistance.

The effects of feeding ß-glucan to Pangasianodon hypophthalmus on immune gene expression and resistance to Edwardsiella ictaluri.

Pangasianodon hypophthalmus (striped catfish) is an important aquaculture species and intensification of farming has increased disease problems, particularly Edwardsiella ictaluri. The effects of feeding ß-glucans on immune gene expression and resistance to E. ictaluri in P. hypophthalmus were explored. Fish were fed 0.1% fungal-derived ß-glucan or 0.1% commercial yeast-derived ß-glucan or a basal control diet without glucan. After 14 days of feeding, the mRNA expression of immune genes (transferrin, C-reactive protein, precerebellin-like protein, Complement C3 and factor B, 2a MHC class II and interleukin-1 beta) in liver, kidney and spleen were determined. Following this fish from each of the three diet treatment groups were infected with E. ictaluri and further gene expression measured 24 h post-infection (h.p.i.), while the remaining fish were monitored over 2 weeks for mortalities. Cumulative percentage mortality at 14 days post-infection (d.p.i.) was less in ß-glucan fed fish compared to controls. There was no difference in gene expression between dietary groups after feeding for 14 days, but there was a clear difference between infected and uninfected fish at 24 h.p.i., and based on principal component analysis ß-glucans stimulated the overall expression of immune genes in the liver, kidney and spleen at 24 h.p.i.

The effects of feeding immunostimulant ß-glucan on the immune response of Pangasianodon hypophthalmus.

Immunostimulants are food additives used by the aquaculture industry to enhance the immune response of fish, and although ß-glucans are now commonly used for this purpose in aquaculture, little is known about their effects on the immune response of Pangasianodon hypophthalmus. Thus, a variety of immune parameters (e.g. phagocytosis, respiratory burst, lysozyme, complement, peroxidase, total protein, total anti-protease, total IgM, natural antibody titres, and specific IgM titres) was examined in this species after feeding fish with a basal control diet or diets supplemented with 0.05, 0.1, or 0.2 g/kg fungal-derived ß-glucan or 0.1% commercial yeast-derived ß-glucan, as a positive control diet, for a period of four weeks. The effect of the glucans on disease resistance was then evaluated by experimentally infecting the fish with Edwardsiella ictaluri by immersion and mortalities monitored for 14 days. Samples were collected from fish for analysis at 0, 1, 3, 7, 14, 21 and 28 days post-feeding (dpf), and also at 14 days post infection (dpi). The lowest dose of fungal-derived ß-glucan (0.05%) appeared insufficient to effectively stimulate the immune response of the fish, while those fed with the two highest levels of fungal-derived ß-glucan had enhanced immune responses compared to the control group. Significantly elevated levels of respiratory burst activity on all days examined (P < 0.05) and lysozyme activity on 7 dpf were found in the group fed 0.2% fungal-derived ß-glucan, while plasma anti-protease activity was significantly enhanced (P < 0.05) by 21 dpf, natural antibody titres by 3 dpf and complement activity by 7 dpf and also at 14 dpi in the group fed 0.1% fungal-derived ß-glucan. No statistical differences was seen in the level of mortalities between the dietary groups, although the group fed with the control diet had the highest level of mortalities and the groups fed with commercial yeast-derived ß-glucan and 0.2% fungal-derived ß-glucan the lowest.

Factors affecting variation in mortality of marine Atlantic salmon Salmo salar in Scotland.

Databases of site production have an important role to play in the investigation and understanding of diseases, since they store valuable amounts of disease and management data. Diseases pose an important constraint to economic expansion of aquaculture. They are dependent on the complex interacting factors of pathogen, environment, and host, and the causes of death can be related to nutritional, environmental, and genetic factors of the host or infectious agents. We examined the drivers of mortality from a single site-production database, which represented one-third of Scottish farmed salmon Salmo salar L. production in 2005, to determine whether mortality 'benchmarking' data could be generalised across sites and production cycles. We show that farm mortality records play an important role in studying mortality losses and identifying of management problems in production. We found that mortalities varied across the months of the year and with the time of year of initial stocking. Production cycles that started in the third quarter of the year had the highest mortality overall. Furthermore, we found site-to-site variation in mortality that may have been caused by either random occurrence of epidemics and environmental events or other local effects.

The impact of escaped farmed Atlantic salmon (Salmo salar L.) on catch statistics in Scotland.

In Scotland and elsewhere, there are concerns that escaped farmed Atlantic salmon (Salmo salar L.) may impact on wild salmon stocks. Potential detrimental effects could arise through disease spread, competition, or inter-breeding. We investigated whether there is evidence of a direct effect of recorded salmon escape events on wild stocks in Scotland using anglers' counts of caught salmon (classified as wild or farmed) and sea trout (Salmo trutta L.). This tests specifically whether documented escape events can be associated with reduced or elevated escapes detected in the catch over a five-year time window, after accounting for overall variation between areas and years. Alternate model frameworks were somewhat inconsistent, however no robust association was found between documented escape events and higher proportion of farm-origin salmon in anglers' catch, nor with overall catch size. A weak positive correlation was found between local escapes and subsequent sea trout catch. This is in the opposite direction to what would be expected if salmon escapes negatively affected wild fish numbers. Our approach specifically investigated documented escape events, contrasting with earlier studies examining potentially wider effects of salmon farming on wild catch size. This approach is more conservative, but alleviates some potential sources of confounding, which are always of concern in observational studies. Successful analysis of anglers' reports of escaped farmed salmon requires high data quality, particularly since reports of farmed salmon are a relatively rare event in the Scottish data. Therefore, as part of our analysis, we reviewed studies of potential sensitivity and specificity of determination of farmed origin. Specificity estimates are generally high in the literature, making an analysis of the form we have performed feasible.

From Markovian to pairwise epidemic models and the performance of moment closure approximations.

Many if not all models of disease transmission on networks can be linked to the exact state-based Markovian formulation. However the large number of equations for any system of realistic size limits their applicability to small populations. As a result, most modelling work relies on simulation and pairwise models. In this paper, for a simple SIS dynamics on an arbitrary network, we formalise the link between a well known pairwise model and the exact Markovian formulation. This involves the rigorous derivation of the exact ODE model at the level of pairs in terms of the expected number of pairs and triples. The exact system is then closed using two different closures, one well established and one that has been recently proposed. A new interpretation of both closures is presented, which explains several of their previously observed properties. The closed dynamical systems are solved numerically and the results are compared to output from individual-based stochastic simulations. This is done for a range of networks with the same average degree and clustering coefficient but generated using different algorithms. It is shown that the ability of the pairwise system to accurately model an epidemic is fundamentally dependent on the underlying large-scale network structure. We show that the existing pairwise models are a good fit for certain types of network but have to be used with caution as higher-order network structures may compromise their effectiveness.

Mapping of AF1 transactivation domains in duplicated rainbow trout glucocorticoid receptors.

The glucocorticoid receptor (GR) is a ligand-dependent transcription factor mediating the genomic effects of glucocorticoids. Two activation functions (AFs) are present in the GR. While the N-terminal AF1 is ligand independent, the C-terminal AF2 overlaps with the ligand-binding domain and is ligand dependent. In this study, we have mapped AF1 in duplicated rainbow trout GRs, called rtGR1 and rtGR2, showing a limited homology (24.5%) in the N-terminal domain. Ablation of this domain from rtGR1 or rtGR2 resulted in a marked decrease (>97%) in maximal hormone-dependent transactivation, but did not affect dexamethasone-binding activity or expression levels. This suggested that, similar to the situation in the human GR (hGR), AF1 is the main AF in the trout GRs. Sequence alignments with hGR suggested a localisation of AF1 to residues 70-230 of rtGR1 and 1-119 of rtGR2. These assignments were generally confirmed in the transactivation experiments with rtGR1- and rtGR2-derived mutants showing partial deletions of their N-terminal domains. In dexamethsone-treated cells (10⁻7  M, 2  h), the subcellular distribution of rtGR1 and rtGR2 mutants lacking the entire N-terminal domain, as well that of an rtGR1 mutant lacking the most N-terminal 234 amino acids, was similar to that of the corresponding wild-type GRs, suggesting that the disruption of transactivation activity was not caused by impairment of nuclear access of the mutants. Bioinformatic analyses predicted the presence of potential helical segments in the core of AF1 of rtGR1 and rtGR2, and further revealed that AF1 in rtGR1, rtGR2, and hGR shares a motif composed of hydrophobic and acidic amino acids.

A prospective longitudinal study of "Candidatus arthromitus"-associated rainbow trout gastroenteritis in the UK.

Rainbow trout gastroenteritis (RTGE) is an emerging disease of farmed rainbow trout (Oncorhynchus mykiss) reported in Croatia, France, Italy, Spain and the UK. The impact of RTGE at the site-level varies, and daily mortalities of 0.5-1% are common. Gross lesions include severe enteritis with congestion and oedema, and the segmented filamentous bacterium "Candidatus arthromitus" has been suggested as a possible aetiological agent. This paper presents findings from a prospective longitudinal epidemiology study that took place from June to November 2006 in 12 RTGE-positive UK sites, examining the impact and presentation of the disease, spread pattern, risk factors for disease presence and severity and the efficacy of current control strategies. RTGE was present in a total of 164 production units across the 12 sites, representing 39% of all stocked units. Fish losses due to RTGE totalled 61.4tonnes. The plotting of epidemic curves revealed a propagating epidemic pattern in 51% affected units, suggesting RTGE is infectious. This was supported by risk analysis of fish transfers and site layouts, which revealed an increased risk of becoming RTGE positive after contiguity or fish transfer to and from a previously affected unit. Conditional multivariate logistic regression analysis identified eight variables significantly associated with the presence of RTGE. Seven variables were risk factors, including higher stocking densities, mortalities due to predation or handling, higher water temperature, higher daily feed input and contiguity or fish movement to and from a previous case. One variable was protective: the absence of an aeration system in a unit. General linear modelling identified a significant association of mean feed input per fish during an outbreak with RTGE cumulative mortality. Finally, great variability was observed with regards to the control strategies targeted to RTGE in these sites, suggesting an absence of a common strategy. In feed NaCl treatments apparently presented a palliative effect if given to affected fish during the clinical outbreaks, but they did not prevent recurrence.

A strategic model for epidemic control in aquaculture.

A stochastic metapopulation model of infectious disease was developed to model the spread of disease within and between sites of a region of an aquaculture industry. The study was a theoretical one examining the effect of transmission parameters through a sensitivity analysis. Production was modelled as either dispersed over many sites, or concentrated into small areas to provide 'firebreaks' between such areas as a disease control strategy. The effectiveness of such a control strategy could then be examined for different industry and disease parameters (for example, overall production, and rates of within- and between-site infection). At the within-site level, contact was modelled as either frequency or density dependent, either of these extreme formulations being potentially appropriate for different diseases. Under density dependence, the effect of high host density of increasing the basic reproduction number R(0) dominates, in contrast to the frequency-dependent model. However, for both model types, concentration of production into separate areas successfully slows the spread of simulated disease, particularly where long-distance transmission of the pathogen is weak due to fast attenuation of infectious agent over distance and time $.

Large-scale properties of clustered networks: implications for disease dynamics.

We consider previously proposed procedures for generating clustered networks and investigate how these procedures lead to differences in network properties other than clustering. We interpret our findings in terms of the effect of the network structure on the disease outbreak threshold and disease dynamics. To generate null-model networks for comparison, we implement an assortativity-conserving rewiring algorithm that alters the level of clustering while causing minimal impact on other properties. We show that many theoretical network models used to generate networks with a particular property often lead to significant changes in network properties other than that of interest. For high levels of clustering, different procedures lead to networks that differ in degree heterogeneity and assortativity, and in broader scale measures such as R(0) and the distribution of shortest path lengths. Hence, care must be taken when investigating the implications of network properties for disease transmission or other dynamic process that the network supports.

Comment on "properties of highly clustered networks".

We consider a procedure for generating clustered networks previously reported by Newman [Phys. Rev. E 68, 026121 (2003)]. In the same study, clustered networks generated according to the proposed model have been reported to have a lower epidemic threshold under susceptible-infective-recovered-type network epidemic dynamics. By rewiring networks generated by this model, such that the degree distribution is conserved, we show that the lower epidemic threshold can be closely reproduced by rewired networks with close to zero clustering. The reported lower epidemic threshold can be explained by different degree distributions observed in the networks corresponding to different levels of clustering. Clustering results in networks with high levels of heterogeneity in node degree, a higher proportion of nodes with zero connectivity, and links concentrated within highly interconnected components of small size. Hence, networks generated by this model differ in both clustering and degree distribution, and the lower epidemic threshold is not explained by clustering alone.

Estimates for local and movement-based transmission of bovine tuberculosis in British cattle.

Both badgers and livestock movements have been implicated in contributing to the ongoing epidemic of bovine tuberculosis (BTB) in British cattle. However, the relative contributions of these and other causes are not well quantified. We used cattle movement data to construct an individual (premises)-based model of BTB spread within Great Britain, accounting for spread due to recorded cattle movements and other causes. Outbreak data for 2004 were best explained by a model attributing 16% of herd infections directly to cattle movements, and a further 9% unexplained, potentially including spread from unrecorded movements. The best-fit model assumed low levels of cattle-to-cattle transmission. The remaining 75% of infection was attributed to local effects within specific high-risk areas. Annual and biennial testing is mandatory for herds deemed at high risk of infection, as is pre-movement testing from such herds. The herds identified as high risk in 2004 by our model are in broad agreement with those officially designated as such at that time. However, border areas at the edges of high-risk regions are different, suggesting possible areas that should be targeted to prevent further geographical spread of disease. With these areas expanding rapidly over the last decade, their close surveillance is important to both identify infected herds qucikly, and limit their further growth.

The effect of network mixing patterns on epidemic dynamics and the efficacy of disease contact tracing.

In networks, nodes may preferentially contact other nodes with similar (assortatively mixed) or dissimilar (disassortatively mixed) numbers of contacts. Different patterns of contact support different epidemic dynamics, potentially affecting the efficacy of control measures such as contact tracing, which aims to identify and isolate nodes with infectious contacts. We used stochastic simulations to investigate the effects of mixing patterns on epidemic dynamics and contact-tracing efficacy. For uncontrolled epidemics, outbreaks occur at lower infection rates for more assortatively mixed networks, with faster initial epidemic growth rate and shorter epidemic duration than for disassortatively mixed networks. Contact tracing performs better for assortative mixing where epidemic size is large and tracing rate low, but it performs better for disassortative mixing at higher contact rates. For assortatively mixed networks, disease spreads first to highly connected nodes, but this is balanced by contact tracing quickly identifying these same nodes. The converse is true for disassortative mixing, where both disease and tracing are less likely to target highly connected nodes. For small epidemics, contact tracing is more effective on disassortative networks due to the greater resilience of assortative networks to link removal. Multi-step contact tracing is more effective than single-step tracing for assortative mixing, but this effect is smaller for disassortatively mixed networks.

Demographic risk factors for classical and atypical scrapie in Great Britain.

Following the bovine spongiform encephalopathy (BSE) crisis, the European Union has introduced policies for eradicating transmissible spongiform encephalopathies (TSEs), including scrapie, from large ruminants. However, recent European Union surveillance has identified a novel prion disease, 'atypical' scrapie, substantially different from classical scrapie. It is unknown whether atypical scrapie is naturally transmissible or zoonotic, like BSE. Furthermore, cases have occurred in scrapie-resistant genotypes that are targets for selection in legislated selective breeding programmes. Here, the first epidemiological study of British cases of atypical scrapie is described, focusing on the demographics and trading patterns of farms and using databases of recorded livestock movements. Triplet comparisons found that farms with atypical scrapie stock more sheep than those of the general, non-affected population. They also move larger numbers of animals than control farms, but similar numbers to farms reporting classical scrapie. Whilst there is weak evidence of association through sheep trading of farms reporting classical scrapie, atypical scrapie shows no such evidence, being well-distributed across regions of Great Britain and through the sheep-trading network. Thus, although cases are few in number so far, our study suggests that, should natural transmission of atypical scrapie be occurring at all, it is doing so slowly.

Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK.

We analyse the relationship between the network of livestock movements in the UK and the dynamics of two diseases: foot-and-mouth disease (FMD), which has an incubation period of days, and scrapie, which incubates over years. For FMD, the time-scale of expected epidemics is similar to the time-scale of the evolution of the network. We argue that, under appropriate conditions, a static network analysis can be an appropriate tool for gaining insights into disease dynamics even when the relevant time-scales are similar, as with FMD. We show that a subclass of 'linkage moves' maintains the network structure, and so removing these links has a dramatic effect on the number of potentially infected farms, an effect corroborated by simulations. In contrast, because scrapie has a low probability of transmission per contact and a long incubation period, a static network representation is probably appropriate; however, the signature of the network in the pattern of transmission is likely to be faint. Scrapie-notifying farms were more likely to be associated with each other via trading at markets than were control farms; however, network community structure proves to be less representative of prevalence patterns than geographical region. These contradictory indicators emphasize that appropriate observation time frames and good discrimination among types of potentially infectious contacts are vital in order for network analysis to be a valuable epidemiological tool.

The network of sheep movements within Great Britain: Network properties and their implications for infectious disease spread.

During the 2001 foot and mouth disease epidemic in the UK, initial dissemination of the disease to widespread geographical regions was attributed to livestock movement, especially of sheep. In response, recording schemes to provide accurate data describing the movement of large livestock in Great Britain (GB) were introduced. Using these data, we reconstruct directed contact networks within the sheep industry and identify key epidemiological properties of these networks. There is clear seasonality in sheep movements, with a peak of intense activity in August and September and an associated high risk of a large epidemic. The high correlation between the in and out degree of nodes favours disease transmission. However, the contact networks were largely dissasortative: highly connected nodes mostly connect to nodes with few contacts, effectively slowing the spread of disease. This is a result of bipartite-like network properties, with most links occurring between highly active markets and less active farms. When comparing sheep movement networks (SMNs) to randomly generated networks with the same number of nodes and node degrees, despite structural differences (such as disassortativity and higher frequency of even path lengths in the SMNs), the characteristic path lengths within the SMNs are close to values computed from the corresponding random networks, showing that SMNs have 'small-world'-like properties. Using the network properties, we show that targeted biosecurity or surveillance at highly connected nodes would be highly effective in preventing a large and widespread epidemic.

Infectious disease control using contact tracing in random and scale-free networks.

Contact tracing aims to identify and isolate individuals that have been in contact with infectious individuals. The efficacy of contact tracing and the hierarchy of traced nodes-nodes with higher degree traced first-is investigated and compared on random and scale-free (SF) networks with the same number of nodes N and average connection K. For values of the transmission rate larger than a threshold, the final epidemic size on SF networks is smaller than that on corresponding random networks. While in random networks new infectious and traced nodes from all classes have similar average degrees, in SF networks the average degree of nodes that are in more advanced stages of the disease is higher at any given time. On SF networks tracing removes possible sources of infection with high average degree. However a higher tracing effort is required to control the epidemic than on corresponding random networks due to the high initial velocity of spread towards the highly connected nodes. An increased latency period fails to significantly improve contact tracing efficacy. Contact tracing has a limited effect if the removal rate of susceptible nodes is relatively high, due to the fast local depletion of susceptible nodes.