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

Prioritization of pig farm biosecurity for control of Salmonella and hepatitis E virus infections: results of a European expert opinion elicitation

BACKGROUND

In the literature, there is absent or weak evidence on the effectiveness of biosecurity measures to the control of Salmonella spp. and hepatitis E virus (HEV) on pig farms. Therefore, the present study aimed to collect, weigh, and compare opinions from experts on the relevance of several biosecurity measures. An online questionnaire was submitted to selected experts, from multiple European countries, knowledgeable on either HEV or Salmonella spp., in either indoor or outdoor pig farming systems (settings). The experts ranked the relevance of eight biosecurity categories with regards to effectiveness in reducing the two pathogens separately, by assigning a score from a total of 80, and within each biosecurity category they scored the relevance of specific biosecurity measures (scale 1-5). Agreement among experts was analysed across pathogens and across settings.

RESULTS

After filtering for completeness and expertise, 46 responses were analysed, with 52% of the experts identified as researchers/scientists, whereas the remaining 48% consisted of non-researchers, veterinary practitioners and advisors, governmental staff, and consultant/industrial experts. The experts self-declared their level of knowledge but neither Multidimensional Scaling nor k-means cluster analyses produced evidence of an association between expertise and the biosecurity answers, and so all experts' responses were analysed together without weighting or adaptation. Overall, the top-ranked biosecurity categories were pig mixing; cleaning and disinfection; feed, water and bedding; and purchase of pigs or semen, while the lowest ranked categories were transport, equipment, animals (other than pigs and including wildlife) and humans. Cleaning and disinfection was ranked highest for both pathogens in the indoor setting, whereas pig mixing was highest for outdoor settings. Several (94/222, 42.3%) measures across all four settings were considered highly relevant. Measures with high disagreement between the respondents were uncommon (21/222, 9.6%), but more frequent for HEV compared to Salmonella spp.

CONCLUSIONS

The implementation of measures from multiple biosecurity categories was considered important to control Salmonella spp. and HEV on farms, and pig mixing activities, as well as cleaning and disinfection practices, were perceived as consistently more important than others. Similarities and differences in the prioritised biosecurity measures were identified between indoor and outdoor systems and pathogens. The study identified the need for further research especially for control of HEV and for biosecurity in outdoor farming.

Modelling infectious viral diseases in swine populations: a state of the art

Mathematical modelling is nowadays a pivotal tool for infectious diseases studies, completing regular biological investigations. The rapid growth of computer technology allowed for development of computational tools to address biological issues that could not be unravelled in the past. The global understanding of viral disease dynamics requires to account for all interactions at all levels, from within-host to between-herd, to have all the keys for development of control measures. A literature review was performed to disentangle modelling frameworks according to their major objectives and methodologies. One hundred and seventeen articles published between 1994 and 2020 were found to meet our inclusion criteria, which were defined to target papers representative of studies dealing with models of viral infection dynamics in pigs. A first descriptive analysis, using bibliometric indexes, permitted to identify keywords strongly related to the study scopes. Modelling studies were focused on particular infectious agents, with a shared objective: to better understand the viral dynamics for appropriate control measure adaptation. In a second step, selected papers were analysed to disentangle the modelling structures according to the objectives of the studies. The system representation was highly dependent on the nature of the pathogens. Enzootic viruses, such as swine influenza or porcine reproductive and respiratory syndrome, were generally investigated at the herd scale to analyse the impact of husbandry practices and prophylactic measures on infection dynamics. Epizootic agents (classical swine fever, foot-and-mouth disease or African swine fever viruses) were mostly studied using spatio-temporal simulation tools, to investigate the efficiency of surveillance and control protocols, which are predetermined for regulated diseases. A huge effort was made on model parameterization through the development of specific studies and methodologies insuring the robustness of parameter values to feed simulation tools. Integrative modelling frameworks, from within-host to spatio-temporal models, is clearly on the way. This would allow to capture the complexity of individual biological variabilities and to assess their consequences on the whole system at the population level. This would offer the opportunity to test and evaluate in silico the efficiency of possible control measures targeting specific epidemiological units, from hosts to herds, either individually or through their contact networks. Such decision support tools represent a strength for stakeholders to help mitigating infectious diseases dynamics and limiting economic consequences.

A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network

Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible co-infection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix. Between-herd transmission was modelled by coupling within-herd and network dynamics using the SimInf package. Different introduction scenarios were tested as well as a decrease in the prevalence of IMV-infected farms. After introduction of a single infected gilt, the model showed that the transmission pathway as well as the prevalence of HEV-infected pigs at slaughter age were affected by the type of the index farm, the health status of the population and the type of the infected farms. These outcomes could help design HEV control strategies at a territorial scale based on the assessment of the farms' and network's risk.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

The objectives of this study were to gain insight into the structure of the cattle trade network in Slovenia and to evaluate the potential for infectious disease spread through movements. The study considered cattle movements between different types of premises that occurred between August 1, 2011 and July 31, 2016 with the exclusion of the movements to the end nodes (e.g., slaughterhouses). In the first part, we performed a static network analysis on monthly and yearly snapshots of the network. These time scales reflect our interest in slowly spreading pathogens; namely Mycobacterium avium subsp. paratuberculosis (MAP), which causes paratuberculosis, a worldwide economically important disease. The results showed consistency in the network measures over time; nevertheless, it was evident that year to year contacts between premises were changing. The importance of individual premises for the network connectedness was highly heterogeneous and the most influential premises in the network were collection centers, mountain pastures, and pastures. Compared to random node removal, targeted removal informed by ranking based on local network measures from previous years was substantially more effective in network disassociation. Inclusion of the latest movement data improved the results. In the second part, we simulated disease spread using a Susceptible-Infectious (SI) model on the temporal network. The SI model was based on the empirically estimated true prevalence of paratuberculosis in Slovenia and four scenarios for probabilities of transmission. Different probabilities were realized by the generation of new networks with the corresponding proportion of contacts which were randomly selected from the original network. These diluted networks served as substrates for simulation of MAP spread. The probability of transmission had a significant influence on the velocity of disease spread through the network. The peaks in daily incidence rates of infected herds were observed at the end of the grazing period. Our results suggest that network analysis may provide support in the optimization of paratuberculosis surveillance and intervention in Slovenia. The approach of simulating disease spread on a diluted network may also be used to model other transmission pathways between herds.

Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweet potato seed system in the Gulu region of northern Uganda for its vulnerability to the spread of emerging epidemics and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network or "supranetwork." Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen and evaluated the influence of spread parameters on the selection of villages for surveillance and management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other, more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, whereas betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Natural viral co-infections in pig herds affect hepatitis E virus (HEV) infection dynamics and increase the risk of contaminated livers at slaughter

Hepatitis E virus (HEV) is a zoonotic pathogen, in particular genotype 3 HEV is mainly transmitted to humans through the consumption of contaminated pork products. This study aimed at describing HEV infection patterns in pig farms and at assessing the impact of immunomodulating co-infections namely Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and Porcine Circovirus Type 2 (PCV2), as well as other individual factors such as piglets' immunity and litters' characteristics on HEV dynamics. A longitudinal follow-up was conducted in three farrow-to-finish farms known to be HEV infected. Overall, 360 piglets were individually monitored from birth to slaughter with regular blood and faecal sampling as well as blood and liver samples collected at slaughterhouse. Virological and serological analyses were performed to detect HEV, PCV2 and PRRSV genome and antibodies. The links between 12 explanatory variables and four outcomes describing HEV dynamics were assessed using cox-proportional hazard models and logistic regression. HEV infection dynamics was found highly variable between farms and in a lower magnitude between batches. HEV positive livers were more likely related to short time-intervals between HEV infection and slaughter time (<40 days, OR = 4.1 [3.7-4.5]). In addition to an influence of piglets' sex and sows' parity, the sequence of co-infections was strongly associated with different HEV dynamics: a PRRSV or PCV2/PRRSV pre- or co-infection was associated with a higher age at HEV shedding (Hazard Ratio = 0.3 [0.2-0.5]), as well as a higher age at HEV seroconversion (HR = 0.5 [0.3-0.9] and HR = 0.4 [0.2-0.7] respectively). A PCV2/PRRSV pre- or co-infection was associated with a longer duration of shedding (HR = 0.5 [0.3-0.8]). Consequently, a PRRSV or PCV2/PRRSV pre- or co-infection was strongly associated with a higher risk of having positive livers at slaughter (OR = 4.1 [1.9-8.9] and OR = 6.5 [3.2-13.2] respectively). In conclusion, co-infections with immunomodulating viruses were found to affect HEV dynamics in the farrow-to-finish pig farms that were followed in this study.

Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Relationships between Microbial Indicators and Pathogens in Recreational Water Settings

Fecal pollution of recreational waters can cause scenic blight and pose a threat to public health, resulting in beach advisories and closures. Fecal indicator bacteria (total and fecal coliforms, Escherichia coli, and enterococci), and alternative indicators of fecal pollution (Clostridium perfringens and bacteriophages) are routinely used in the assessment of sanitary quality of recreational waters. However, fecal indicator bacteria (FIB), and alternative indicators are found in the gastrointestinal tract of humans, and many other animals and therefore are considered general indicators of fecal pollution. As such, there is room for improvement in terms of their use for informing risk assessment and remediation strategies. Microbial source tracking (MST) genetic markers are closely associated with animal hosts and are used to identify fecal pollution sources. In this review, we examine 73 papers generated over 40 years that reported the relationship between at least one indicator and one pathogen group or species. Nearly half of the reports did not include statistical analysis, while the remainder were almost equally split between those that observed statistically significant relationships and those that did not. Statistical significance was reported less frequently in marine and brackish waters compared to freshwater, and the number of statistically significant relationships was considerably higher in freshwater (p < 0.0001). Overall, significant relationships were more commonly reported between FIB and pathogenic bacteria or protozoa, compared to pathogenic viruses (p: 0.0022–0.0005), and this was more pronounced in freshwater compared to marine. Statistically significant relationships were typically noted following wet weather events and at sites known to be impacted by recent fecal pollution. Among the studies that reported frequency of detection, FIB were detected most consistently, followed by alternative indicators. MST markers and the three pathogen groups were detected least frequently. This trend was mirrored by reported concentrations for each group of organisms (FIB > alternative indicators > MST markers > pathogens). Thus, while FIB, alternative indicators, and MST markers continue to be suitable indicators of fecal pollution, their relationship with waterborne pathogens, particularly viruses, is tenuous at best and influenced by many different factors such as frequency of detection, variable shedding rates, differential fate and transport characteristics, as well as a broad range of site-specific factors such as the potential for the presence of a complex mixture of multiple sources of fecal contamination and pathogens.

Hepatitis E virus: zoonotic and foodborne transmission in developed countries

Hepatitis E virus (HEV), together with hepatitis A virus, transmits via the fecal–oral route. The number of domestic hepatitis E cases among Europeans has grown alarmingly during the past 5 years. Surveillance studies suggest that the number of foodborne HEV infections is increasing most rapidly. Zoonotic HEV genotype HEV-3 is prevalent among pigs and wild boars in Europe and many developed countries, whereas zoonotic genotype HEV-4 is more common in pigs in some Asian countries. This review presents the most recent data about possible foodborne transmission of HEV via pigs and other production animals and about the presence of HEV in high-risk foods, such as ready-to-eat meat products. Possible solutions about how to tackle this problem are discussed here.