Disease transmission in animal transfer networks

Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model

Background

African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data.

Material and Methods

Empirical movement data from Thailand for the year 2019 were used, and expert opinions were sought to evaluate network properties and the diffusion model. The networks were presented as live pig movement and carcass movement data at the provincial and district levels. For network analysis, a descriptive network analysis was performed using outdegree, indegree, betweenness, fragmentation, and power law distribution, and cutpoints were used to describe movement patterns. For the diffusion model, we simulated each network using spatially different infected locations, patterns, and initial infection sites. Based on expert opinions, the initial infection site, the probability of ASF occurrence, and the probability of the initial infected adopter were selected for the appropriated network. In this study, we also simulated networks under varying network parameters to predict the infection speed.

Results and Conclusions

The total number of movements recorded was 2,594,364. These were divided into 403,408 (403,408/2,594,364; 15.55%) for live pigs and 2,190,956 (2,190,956/2,594,364; 84.45%) for carcasses. We found that carcass movement at the provincial level showed the highest outdegree (mean = 342.554, standard deviation (SD) = 900.528) and indegree values (mean = 342.554, SD = 665.509). In addition, the outdegree and indegree presented similar mean values and the degree distributions of both district networks followed a power-law function. The network of live pigs at provincial level showed the highest value for betweenness (mean = 0.011, SD = 0.017), and the network of live pigs at provincial level showed the highest value for fragmentation (mean = 0.027, SD = 0.005). Our simulation data indicated that the disease occurred randomly due to live pig and carcass movements along the central and western regions of Thailand, causing the rapid spread of ASF. Without control measures, it could spread to all provinces within 5- and 3-time units and in all districts within 21- and 30-time units for the network of live pigs and carcasses, respectively. This study assists the authorities to plan control and preventive measures and limit economic losses caused by ASF.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

Social interactions of juvenile rabbits (Oryctolagus cuniculus) and their potential role in lagovirus transmission

Rabbit Haemorrhagic Disease Virus (RHDV), which is a calicivirus, is used as a biocontrol agent to suppress European wild rabbit populations in Australia. The transmission of RHDV can be influenced by social interactions of rabbits; however, there is a paucity of this knowledge about juvenile rabbits and the roles they may play in the transmission of RHDV. We aimed to quantify the social interactions of juvenile (< 900 g) and adult (> 1200 g) rabbits in a locally abundant population in the Central Tablelands of New South Wales, Australia. Twenty-six juvenile and 16 adult rabbits were fitted with VHF proximity loggers to monitor intra- and inter-group pairings. Use of multiple warrens by these rabbits was investigated using VHF base stations at nine warrens and on foot with a hand-held Yagi antenna. Juvenile rabbits were strongly interconnected with both juveniles and adults within and outside their warren of capture, and almost all juveniles were well-connected to other individuals within their own social group. Inter-group pairings were infrequent and fleeting between adults. Both juvenile and adult rabbits used multiple warrens. However, visits to warrens outside their warren of capture, particularly those within 50 m, were more common and longer in duration in juveniles than in adults. The high connectivity of juveniles within and between warrens in close proximity increases potential pathogen exchange between warrens. Therefore, juvenile rabbits could be of greater importance in lagovirus transmission than adult rabbits. The strength of juvenile rabbit inter- and intra-group pairings, and their tendency to use multiple warrens, highlight their potential to act as ‘superspreaders’ of both infection and immunity for lagoviruses and other pathogens with similar lifecycles. Confirmation of this potential is required through examination of disease progress and rabbit age-related immune responses during outbreaks.

Animal movement in a pastoralist population in the Maasai Mara Ecosystem in Kenya and implications for pathogen spread and control

Livestock movements are important drivers for infectious disease transmission. However, paucity of such data in pastoralist communities in rangeland ecosystems limits our understanding of their dynamics and hampers disease surveillance and control. The aim of this study was to investigate animal movement networks in a pastoralist community in Kenya, and assess network-based strategies for disease control. We used network analysis to characterize five types of between-village animal movement networks. We then evaluated implications of these networks for disease spread and control by quantifying topological changes in the network associated with targeted and random removal of nodes. To construct these networks, data were collected using standardized questionnaires (N = 165 households) from communities living within the Maasai Mara Ecosystem in southwestern Kenya. Our analyses show that the Maasai Mara National Reserve (MMNR), a protected wildlife area, was critical for maintaining village connectivity in the agistment network (dry season grazing), with MMNR-adjacent villages being highly utilized during the dry season. In terms of disease dynamics, the network-based basic reproduction number, R₀, was sufficient to allow disease invasion in all the five networks, and removal of villages based on degree or betweenness was not efficient in reducing R₀. However, we show that villages with high degree or betweenness may play an important role in maintaining network connectivity, which may not be captured by assessment of R₀ alone. Such villages may function as potential "firebreaks." For example, targeted removal of highly connected village nodes was more effective at fragmenting each network than random removal of nodes, indicating that network-based targeting of interventions such as vaccination could potentially disrupt transmission pathways in the ecosystem. In conclusion, this work shows that animal movements have the potential to shape patterns of disease transmission in this ecosystem, with targeted interventions being a practical and efficient measure for disease control.

Characterization of potential superspreader farms for bovine tuberculosis: A review

Background

Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’.

Objectives and Methods

We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number ‐ R_f: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain.

Results

Farms exhibit marked heterogeneity in contact rates arising from between‐farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within‐herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites.

Conclusions

Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real‐time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.

Busting biofilms: free-living amoebae disrupt preformed methicillin-resistant Staphylococcus aureus (MRSA) and Mycobacterium bovis biofilms

Biofilm-associated infections are difficult to eradicate because of their ability to tolerate antibiotics and evade host immune responses. Amoebae and/or their secreted products may provide alternative strategies to inhibit and disperse biofilms on biotic and abiotic surfaces. We evaluated the potential of five predatory amoebae - Acanthamoeba castellanii, Acanthamoeba lenticulata, Acanthamoeba polyphaga, Vermamoeba vermiformis and Dictyostelium discoideum - and their cell-free secretions to disrupt biofilms formed by methicillin-resistant Staphylococcus aureus (MRSA) and Mycobacterium bovis. The biofilm biomass produced by MRSA and M. bovis was significantly reduced when co-incubated with A. castellanii, A. lenticulata and A. polyphaga, and their corresponding cell-free supernatants (CFS). Acanthamoeba spp. generally produced CFS that mediated biofilm dispersal rather than directly killing the bacteria; however, A. polyphaga CFS demonstrated active killing of MRSA planktonic cells when the bacteria were present at low concentrations. The active component(s) of the A. polyphaga CFS is resistant to freezing, but can be inactivated to differing degrees by mechanical disruption and exposure to heat. D. discoideum and its CFS also reduced preformed M. bovis biofilms, whereas V. vermiformis only decreased M. bovis biofilm biomass when amoebae were added. These results highlight the potential of using select amoebae species or their CFS to disrupt preformed bacterial biofilms.

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R0 = 3) and 'slow' ( R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Evaluation of the impact of live pig trade network, vaccination coverage and socio-economic factors in the classical swine fever eradication program in Peru

Classical swine fever (CSF) is a viral infectious disease of swine with significant economic impact in the affected countries due to the limitation of trade, culling of infected animals and production losses. In Latin America, CSF is endemic in several countries including Ecuador, Bolivia, Brazil and Peru. Since 2010, the National Veterinary Services of Peru have been working to better control and eradicate the disease with an intensive vaccination program. The aim of this study was to evaluate the effectiveness of the vaccination program and determine which factors are still contributing to the persistence of the disease in certain regions of Peru. We integrated the data from the vaccination campaign, the live pig movement network and other socioeconomic indicators into a multilevel logistic regression model to evaluate their association with CSF occurrence at district level. The results revealed that high vaccination coverage significantly reduces the risk of CSF occurrence (OR = 0.07), supporting the effectiveness of the vaccination program. Districts belonging to large and medium pig trade network communities (as identified with walktrap algorithm) had higher probability to CSF occurrence (OR = 2.83 and OR = 5.83, respectively). The human development index (HDI) and the presence of a slaughterhouse in the district was also significantly associated with an increased likelihood of CSF occurrence (OR = 1.52 and OR = 3.25, respectively). Districts receiving a high proportion of the movements from districts that were infected in the previous year were also at higher risk of CSF occurrence (OR = 3.30). These results should be useful to guide the prioritization of vaccination strategies and may help to design other intervention strategies (e.g., target education, movement restrictions, etc.) in high-risk areas to more rapidly advance in the eradication of CSF in Peru.

Combining Salmonella Dublin genome information and contact-tracing to substantiate a new approach for improved detection of infectious transmission routes in cattle populations

This study presents a new method for detection of between-herd livestock movements to facilitate disease tracing and more accurately describe network behaviour of relevance for spread of infectious diseases, including within-livestock business risk-carrying contacts that are not necessarily recorded anywhere. The study introduces and substantiates the concept of grouping livestock herds into business-units based on ownership and location in the tracing analysis of animal movement-based contact networks. To test the utility of this approach, whole core genome sequencing of 196 Salmonella Dublin isolates stored from previous surveillance and project activities was combined with information on cattle movements recorded in the Danish Cattle Database between 1997 and 2017. The aim was to investigate alternative explanations for S. Dublin circulation in groups of herds connected by ownership, but without complete records of livestock movements. The EpiContactTrace R-package was used to trace the contact networks between businesses and compare the network characteristics of businesses sharing strains of S. Dublin with different levels of genetic relatedness. The ownership-only definition proved to be an unreliable grouping approach for large businesses, which could have internal distances larger than 250 km and therefore do not represent useful epidemiological units. Therefore, the grouping was refined using spatial analysis. More than 90% of final business units formed were composed of one single cattle property, whereas multi-property businesses could reach up to eight properties in a given year, with up to 15 cattle herds having been part of the same business through the study period. Results showed markedly higher probabilities of introduction of infectious animals between proposed businesses from which the same clone of S. Dublin had been isolated, when compared to businesses with non-related strains, thus substantiating the business-unit as an important epidemiological feature to consider in contact network analysis and tracing of infection routes. However, this approach may overestimate real-life contacts between cattle properties and putatively overestimate the degree of risk-contacts within each business, since it is based solely on information about property ownership and location. This does not consider administrative and individual farmers behaviours that essentially keep two properties separated. Despite this, we conclude that defining epidemiological units based on businesses is a promising approach for future disease tracing tasks.

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

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A method is proposed to explore the role of pig movements on pathogen epidemiology.

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Pig farm centrality in the network is associated with higher HEV seroprevalence.

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Some local areas are more at risk for HEV due to incoming pig movements.

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Animal movements should be included in risk-based surveillance strategies.

Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p < 0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.