Threat to the French Swine Industry of African Swine Fever: Surveillance, Spread, and Control Perspectives

Analyzing the intrastate and interstate swine movement network in the United States

Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.

A systematic review of mechanistic models used to study avian influenza virus transmission and control

The global spread of avian influenza A viruses in domestic birds is causing increasing socioeconomic devastation. Various mechanistic models have been developed to better understand avian influenza transmission and evaluate the effectiveness of control measures in mitigating the socioeconomic losses caused by these viruses. However, the results of models of avian influenza transmission and control have not yet been subject to a comprehensive review. Such a review could help inform policy makers and guide future modeling work. To help fill this gap, we conducted a systematic review of the mechanistic models that have been applied to field outbreaks. Our three objectives were to: (1) describe the type of models and their epidemiological context, (2) list estimates of commonly used parameters of low pathogenicity and highly pathogenic avian influenza transmission, and (3) review the characteristics of avian influenza transmission and the efficacy of control strategies according to the mechanistic models. We reviewed a total of 46 articles. Of these, 26 articles estimated parameters by fitting the model to data, one evaluated the effectiveness of control strategies, and 19 did both. Values of the between-individual reproduction number ranged widely: from 2.18 to 86 for highly pathogenic avian influenza viruses, and from 4.7 to 45.9 for low pathogenicity avian influenza viruses, depending on epidemiological settings, virus subtypes and host species. Other parameters, such as the durations of the latent and infectious periods, were often taken from the literature, limiting the models' potential insights. Concerning control strategies, many models evaluated culling (n = 15), while vaccination received less attention (n = 6). According to the articles reviewed, optimal control strategies varied between virus subtypes and local conditions, and depended on the overall objective of the intervention. For instance, vaccination was optimal when the objective was to limit the overall number of culled flocks. In contrast, pre-emptive culling was preferred for reducing the size and duration of an epidemic. Early implementation consistently improved the overall efficacy of interventions, highlighting the need for effective surveillance and epidemic preparedness.

Estimating the effectiveness of control actions on African swine fever transmission in commercial swine populations in the United States

Given the proximity of African swine fever (ASF) to the U.S., there is an urgent need to better understand the possible dissemination pathways of the virus within the U.S. swine industry and to evaluate mitigation strategies. Here, we extended PigSpread, a farm-level spatially-explicit stochastic compartmental transmission model incorporating six transmission routes including between-farm swine movements, vehicle movements, and local spread, to model the dissemination of ASF. We then examined the effectiveness of control actions similar to the ASF national response plan. The average number of secondary infections during the first 60 days of the outbreak was 49 finisher farms, 17 nursery farms, 5 sow farms, and less than one farm in other production types. The between-farm movements of swine were the predominant route of ASF transmission with an average contribution of 71.1%, while local spread and movement of vehicles were less critical with average contributions of 14.6% and 14.4%. We demonstrated that the combination of quarantine, depopulation, movement restrictions, contact tracing, and enhanced surveillance, was the most effective mitigation strategy, resulting in an average reduction of 79.0% of secondary cases by day 140 of the outbreak. Implementing these control actions led to a median of 495,619 depopulated animals, 357,789 diagnostic tests, and 54,522 movement permits. Our results suggest that the successful elimination of an ASF outbreak is likely to require the deployment of all control actions listed in the ASF national response plan for more than 140 days, as well as estimating the resources needed for depopulation, testing, and movement permits under these controls.

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.

Stochastic modelling of African swine fever in wild boar and domestic pigs: Epidemic forecasting and comparison of disease management strategies

African swine fever (ASF), caused by the African swine fever virus (ASFV), is highly virulent in domestic pigs and wild boar (Sus scrofa), causing up to 100% mortality. The recent epidemic of ASF in Europe has had a serious economic impact and poses a threat to global food security. Unfortunately, there is no effective treatment or vaccine against ASFV, limiting the available disease management strategies. Mathematical models allow us to further our understanding of infectious disease dynamics and evaluate the efficacy of disease management strategies. The ASF Challenge, organised by the French National Research Institute for Agriculture, Food, and the Environment, aimed to expand the development of ASF transmission models to inform policy makers in a timely manner. Here, we present the model and associated projections produced by our team during the challenge. We developed a stochastic model combining transmission between wild boar and domestic pigs, which was calibrated to synthetic data corresponding to different phases describing the epidemic progression. The model was then used to produce forward projections describing the likely temporal evolution of the epidemic under various disease management scenarios. Despite the interventions implemented, long-term projections forecasted persistence of ASFV in wild boar, and hence repeated outbreaks in domestic pigs. A key finding was that it is important to consider the timescale over which different measures are evaluated: interventions that have only limited effectiveness in the short term may yield substantial long-term benefits. Our model has several limitations, partly because it was developed in real-time. Nonetheless, it can inform understanding of the likely development of ASF epidemics and the efficacy of disease management strategies, should the virus continue its spread in Europe.

Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach

In Thailand, pork is one of the most consumed meats nationwide. Pig farming is hence an important business in the country. However, 95% of the farms were considered smallholders raising only 50 pigs or less. With limited budgets and resources, the biosecurity level in these farms is relatively low. Pig movements have been previously identified as a risk factor in the spread of infectious diseases. Therefore, the present study aimed to explicitly analyze the pig movement network structure and assess its vulnerability to the spread of emerging diseases in Thailand. We used official electronic records of nationwide pig movements throughout the year 2021 to construct a directed weighted one-mode network. Degree centrality, degree distribution, connected components, network community, and modularity were measured to explore the network architectures and properties. In this network, 484,483 pig movements were captured. In which, 379,948 (78.42%) were moved toward slaughterhouses and hence excluded from further analyses. From the remaining links, we suggested that the pig movement network in Thailand was vulnerable to the spread of emerging infectious diseases. Within the network, we found a strongly connected component (SCC) connecting 1044 subdistricts (38.6% of the nodes), a giant weakly connected component (GWCC) covering 98.2% of the nodes (2654/2704), and inter-regional communities with overall network modularity of 0.68. The disease may rapidly spread throughout the country. A better understanding of the nationwide pig movement networks is helpful in tailoring control interventions to cope with the newly emerged diseases once introduced.

Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision-makers

African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur. This article is protected by copyright. All rights reserved.

Modelling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and shipment restrictions. Here, we developed a nested multiscale model for the transmission of ASF, combining a spatially explicit network model of animal shipments with a deterministic compartmental model for the dynamics of two ASF strains within 3 km × 3 km pixels in one Brazilian state. The model outcomes are epidemic duration, number of secondary infected farms and pigs, and distance of ASF spread. The model also shows the spatial distribution of ASF epidemics. We analyzed quarantine-based control interventions in the context of mortality trigger thresholds for the deployment of control strategies. The mean epidemic duration of a moderately virulent strain was 11.2 days, assuming the first infection is detected (best-case scenario), and 15.9 days when detection is triggered at 10% mortality. For a highly virulent strain, the epidemic duration was 6.5 days and 13.1 days, respectively. The distance from the source to infected locations and the spatial distribution was not dependent on strain virulence. Under the best-case scenario, we projected an average number of infected farms of 23.77 farms and 18.8 farms for the moderate and highly virulent strains, respectively. At 10% mortality-trigger, the predicted number of infected farms was on average 46.27 farms and 42.96 farms, respectively. We also demonstrated that the establishment of ring quarantine zones regardless of size (i.e. 5 km, 15 km) was outperformed by backward animal movement tracking. The proposed modelling framework provides an evaluation of ASF epidemic potential, providing a ranking of quarantine-based control strategies that could assist animal health authorities in planning the national preparedness and response plan.

African Swine Fever in Wild Boar in Europe-A Review

The introduction of genotype II African swine fever (ASF) virus, presumably from Africa into Georgia in 2007, and its continuous spread through Europe and Asia as a panzootic disease of suids, continues to have a huge socio-economic impact. ASF is characterized by hemorrhagic fever leading to a high case/fatality ratio in pigs. In Europe, wild boar are especially affected. This review summarizes the currently available knowledge on ASF in wild boar in Europe. The current ASF panzootic is characterized by self-sustaining cycles of infection in the wild boar population. Spill-over and spill-back events occur from wild boar to domestic pigs and vice versa. The social structure of wild boar populations and the spatial behavior of the animals, a variety of ASF virus (ASFV) transmission mechanisms and persistence in the environment complicate the modeling of the disease. Control measures focus on the detection and removal of wild boar carcasses, in which ASFV can remain infectious for months. Further measures include the reduction in wild boar density and the limitation of wild boar movements through fences. Using these measures, the Czech Republic and Belgium succeeded in eliminating ASF in their territories, while the disease spread in others. So far, no vaccine is available to protect wild boar or domestic pigs reliably against ASF.

Is transportation a risk factor for African swine fever transmission in Australia: a review

African swine fever (ASF) is a viral disease of the pigs that was first described in Africa during the early part of the twentieth century. The disease has periodically occurred outside of Africa, including an ongoing epidemic in Europe and Asia that started in 2007; the disease has never occurred in Australia or New Zealand. Once introduced into a country, spread can occur through direct and indirect routes of transmission. Infected feral pig populations have the potential to act as a long-term reservoir for the virus, making eradication difficult. Just before and throughout the period of clinical signs, ASF virus is shed in oronasal fluids, urine, faeces and blood. This results in contamination of the pig's environment, including flooring, equipment and vehicles. Transportation-related risk factors therefore are likely to play an important role in ASF spread, though evidence thus far has been largely anecdotal. In addition to the existing AUSVETPLAN ASF plan, efforts should be made to improve transportation biosecurity, from the time a pig leaves the farm to its destination. Collection of data that could quantify the capabilities and capacity of Australia to clean and disinfect livestock trucks would help to determine if private and/or public sector investment should be made in this area of biosecurity. No peer-reviewed research was identified that described a specific process for cleaning and disinfecting a livestock truck known to be contaminated with ASF virus, though literature suggests that transportation is an important route of transmission for moving the virus between farms and countries.

African swine fever and outdoor farming of pigs

This opinion describes outdoor farming of pigs in the EU, assesses the risk of African swine fewer (ASF) introduction and spread associated with outdoor pig farms and proposes biosecurity and control measures for outdoor pig farms in ASF-affected areas of the EU. Evidence was collected from Member States (MSs) veterinary authorities, farmers' associations, literature and legislative documents. An Expert knowledge elicitation (EKE) was carried out to group outdoor pig farms according to their risk of introduction and spread of ASF, to rank biosecurity measures regarding their effectiveness with regard to ASF and propose improvements of biosecurity for outdoor pig farming and accompanying control measures. Outdoor pig farming is common and various farm types are present throughout the EU. As there is no legislation at European level for categorising outdoor pig farms in the EU, information is limited, not harmonised and needs to be interpreted with care. The baseline risk of outdoor pig farms for ASFV introduction and its spread is high but with considerable uncertainty. The Panel is 66-90% certain that, if single solid or double fences were fully and properly implemented on all outdoor pig farms in areas of the EU where ASF is present in wild boar and in domestic pigs in indoor farms and outdoor farms (worst case scenario not considering different restriction zones or particular situations), without requiring any other outdoor-specific biosecurity measures or control measures, this would reduce the number of new ASF outbreaks occurring in these farms within a year by more than 50% compared to the baseline risk. The Panel concludes that the regular implementation of independent and objective on-farm biosecurity assessments using comprehensive standard protocols and approving outdoor pig farms on the basis of their biosecurity risk in an official system managed by competent authorities will further reduce the risk of ASF introduction and spread related to outdoor pig farms.

Mechanistic modelling of African swine fever: A systematic review

The spread of African swine fever (ASF) poses a grave threat to the global swine industry. Without an available vaccine, understanding transmission dynamics is essential for designing effective prevention, surveillance, and intervention strategies. These dynamics can often be unraveled through mechanistic modelling. To examine the assumptions on transmission and objectives of the mechanistic models of ASF, a systematic review of the scientific literature was conducted. Articles were examined across multiple epidemiological and model characteristics, with filiation between models determined through the creation of a neighbor-joined tree using phylogenetic software. Thirty-four articles qualified for inclusion, with four main modelling objectives identified: estimating transmission parameters (11 studies), assessing determinants of transmission (7), examining consequences of hypothetical outbreaks (5), assessing alternative control strategies (11). Population-based (17), metapopulation (5), and individual-based (12) model frameworks were represented, with population-based and metapopulation models predominantly used among domestic pigs, and individual-based models predominantly represented among wild boar. The majority of models (25) were parameterized to the genotype II isolates currently circulating in Europe and Asia. Estimated transmission parameters varied widely among ASFV strains, locations, and transmission scale. Similarly, parameter assumptions between models varied extensively. Uncertainties on epidemiological and ecological parameters were usually accounted for to assess the impact of parameter values on the modelled infection trajectory. To date, almost all models are host specific, being developed for either domestic pigs or wild boar despite the fact that spillover events between domestic pigs and wild boar are evidenced to play an important role in ASF outbreaks. Consequently, the development of more models incorporating such transmission routes is crucial. A variety of codified and hypothetical control strategies were compared however they were all a priori defined interventions. Future models, built to identify the optimal contributions across many control methods for achieving specific outcomes should provide more useful information for policy-makers. Further, control strategies were examined in competition with each other, which is opposed to how they would actually be synergistically implemented. While comparing strategies is beneficial for identifying a rank-order efficacy of control methods, this structure does not necessarily determine the most effective combination of all available strategies. In order for ASFV models to effectively support decision-making in controlling ASFV globally, these modelling limitations need to be addressed.

Scientific Opinion on the assessment of the control measures of the category A diseases of Animal Health Law: African Swine Fever

EFSA received a mandate from the European Commission to assess the effectiveness of some of the control measures against diseases included in the Category A list according to Regulation (EU) 2016/429 on transmissible animal diseases (‘Animal Health Law’). This opinion belongs to a series of opinions where these control measures will be assessed, with this opinion covering the assessment of control measures for African Swine Fever (ASF). In this opinion, EFSA and the AHAW Panel of experts reviewed the effectiveness of: (i) clinical and laboratory sampling procedures, (ii) monitoring period and (iii) the minimum radius of the protection and surveillance zone, and the minimum length of time the measures should be applied in these zones. The general methodology used for this series of opinions has been published elsewhere; nonetheless, specific details of the model used for the assessment of the laboratory sampling procedures for ASF are presented here. Here, also, the transmission kernels used for the assessment of the minimum radius of the protection and surveillance zones are shown. Several scenarios for which these control measures had to be assessed were designed and agreed prior to the start of the assessment. In summary, several sampling procedures as described in the diagnostic manual for ASF were considered ineffective and a suggestion to exclude, or to substitute with more effective procedures was made. The monitoring period was assessed as non‐effective for several scenarios and a longer monitoring period was suggested to ensure detection of potentially infected herds. It was demonstrated that the surveillance zone comprises 95% of the infections from an affected establishment, and therefore is considered effective. Recommendations provided for each of the scenarios assessed aim to support the European Commission in the drafting of further pieces of legislation, as well as for plausible ad hoc requests in relation to ASF.

Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.

Genetic Analysis of African Swine Fever Virus From the 2018 Outbreak in South-Eastern Burundi

African swine fever (ASF) is a contagious viral disease that causes high mortality, approaching 100%, in domestic pigs and wild boars. The disease has neither a cure nor a vaccine, and it is caused by an ASF virus (ASFV), the only member of the family Asfarviridae, genus Asfivirus, and the only known DNA arbovirus. Twenty-four genotypes of ASFV have been described to date, and all of them have been described in Africa. ASF is endemic in Burundi, and several outbreaks have been reported in the country; the disease continues to economically impact on small-scale farmers. This study aimed at genetic characterization of ASFV that caused an ASF outbreak in the Rutana region, Burundi, in the year 2018. Tissue samples from domestic pigs that died as a result of a severe hemorrhagic disease were collected in order to confirm the disease using polymerase chain reaction (PCR) and to conduct partial genome sequencing. Nucleotide sequences were obtained for the B646L (p72) gene, the intergenic fragment between the I73R and I329L genes, and the central variable region (CVR) of the B602L gene. Phylogenetic analysis of the Burundian 2018 ASFV grouped the virus within B646L (p72) genotype X and clustered together with those reported during the 1984 and 1990 outbreaks in Burundi with high nucleotide identity to some ASFV strains previously reported in neighboring East African countries, indicating a regional distribution of this ASFV genotype. Analysis of the intergenic fragment between I73R and I329L genes showed that the Burundian 2018 ASFV described in this study lacked a 32–base pair (bp) fragment present in the reference genotype X strain, Kenya 1950. In addition, the strain described in this study had the signature AAABNAABA at the CVR (B602L) gene and showed 100% amino acid sequence identity to viruses responsible for recent ASF outbreaks in the region. The virus described in this study showed high genetic similarities with ASFV strains previously described in domestic pigs, wild suids, and soft ticks in East African countries, indicating a possible common wild source and continuous circulation in domestic pigs in the region.

Mechanical transmission of African swine fever virus by Stomoxys calcitrans: Insights from a mechanistic model

African swine fever (ASF) represents a global threat with huge economic consequences for the swine industry. Even though direct contact is likely to be the main transmission route from infected to susceptible hosts, recent epidemiological investigations have raised questions regarding the role of haematophagous arthropods, in particular the stable fly (Stomoxys calcitrans). In this study, we developed a mechanistic vector-borne transmission model for ASF virus (ASFV) within an outdoor domestic pig farm in order to assess the relative contribution of stable flies to the spread of the virus. The model was fitted to the ecology of the vector, its blood-feeding behaviour and pig-to-pig transmission dynamic. Model outputs suggested that in a context of low abundance (<5 flies per pig), stable flies would play a minor role in the spread of ASFV, as they are expected to be responsible for around 10% of transmission events. However, with abundances of 20 and 50 stable flies per pig, the vector-borne transmission would likely be responsible for almost 30% and 50% of transmission events, respectively. In these situations, time to reach a pig mortality of 10% would be reduced by around 26% and 40%, respectively. The sensitivity analysis emphasized that the expected relative contribution of stable flies was strongly dependent on the volume of blood they regurgitated and the infectious dose for pigs. This study identified crucial knowledge gaps that need to be filled in order to assess more precisely the potential contribution of stable flies to the spread of ASFV, including a quantitative description of the populations of haematophagous arthropods that could be found in pig farms, a better understanding of blood-feeding behaviours of stable flies and the quantification of the probability that stable flies partially fed with infectious blood transmit the virus to a susceptible pig during a subsequent blood-feeding attempt.

Development and clinical application of a novel CRISPR-Cas12a based assay for the detection of African swine fever virus

BACKGROUND

As no treatment or effective vaccine for African swine fever virus (ASFV) is currently available, a rapid, highly sensitive diagnostic is urgently needed to curb the spread of ASFV.

RESULTS

Here we designed a novel CRISPR-Cas12a based assay for ASFV detection. To detect different ASFV genotypes, 19 crRNAs were designed to target the conserved p72 gene in ASFV, and several crRNAs with high activity were identified that could be used as alternatives in the event of new ASFV variants. The results showed that the sensitivity of the CRISPR-Cas12a based assay is about ten times higher than either the commercial quantitative PCR (qPCR) kit or the OIE-recommended qPCR. CRISPR-Cas12a based assay could also detect ASFV specifically without cross-reactivity with other important viruses in pigs and various virus genotypes. We also found that longer incubation times increased the detection limits, which could be applied to improve assay outcomes in the detection of weakly positive samples and new ASFV variants. In addition, both the CRISPR-Cas12a based assay and commercial qPCR showed very good consistency.

CONCLUSIONS

In summary, the CRISPR-Cas12a based assay offers a feasible approach and a new diagnostic technique for the diagnosis of ASFV, particularly in resource-poor settings.

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.

Risk-based early detection system of African Swine Fever using mortality thresholds

African swine fever (ASF) is an infectious disease of swine causing major losses in the swine industry worldwide. Early detection of ASF is challenging because of the wide range of non-specific clinical signs produced and its relatively low contagiousness. Monitoring pig mortality is a promising approach for early detection of ASF, but such approach has been associated with delay in disease detection in large pig farms. The purpose of this study was to compare the effectiveness and suitability of early detection strategies for ASF in large commercial pig farms using mortality monitoring at the pen, room or barn level. The within-barn spread of the disease was modelled including the non-homogeneous probabilities of transmission within pens, between pens and between rooms. The performances of early detection surveillance based on mortality thresholds established for different epidemiological units were compared in terms of sensitivity, time to detection and number of false alarms per year. A barn with a capacity of 3,200 pigs divided into 8 rooms with 10 pens each containing 40 pigs per pen was used as an example. Our results show that using room- or pen-based mortality thresholds provided a time to detection of 8 days post-disease introduction. Similar detection performances could be achieved with barn-level mortality threshold but at the cost of an increased number of pigs to be tested each year. The different scenarios tested also show that barn characteristics such as baseline mortality rate and pen size had a limited impact on the pen-level mortality thresholds required for disease early detection. These results offer strong support for using mortality data for early detection of ASF not only in small pig herds but also in large commercial barns. Furthermore, the mortality thresholds defined in this study might be relevant to a wide range of pig production sites.

Disease-Induced Mortality Outweighs Hunting in Causing Wild Boar Population Crash After African Swine Fever Outbreak

African swine fever (ASF) has been spreading in the Eurasian continent for more than 10 years now. Although the course of ASF in domestic pigs and its negative economic impact on the pork industry are well-known, we still lack a quantitative assessment of the impact of ASF on wild boar (Sus scrofa) populations under natural conditions. Wild boar is not only a reservoir for ASF; it is also one of the key wildlife species affecting structure and functioning of ecosystems. Therefore, knowledge on how ASF affects wild boar populations is crucial to better predict ecosystem response and for the design of scientific-based wild boar management to control ASF. We used a long-term camera trap survey (2012-2017) from the Białowieza Primeval Forest (BPF, Poland), where an ASF outbreak occurred in 2015, to investigate the impact of the disease on wild boar population dynamics under two contrasting management regimes (hunted vs. non-hunted). In the hunted part of BPF ("managed area"), hunting was drastically increased prior and after the first ASF case occurred (March 2015), whereas inside the National Park, hunting was not permitted ("unmanaged area," first detected case in June 2015). Using a random encounter model (REM), we showed that the density and abundance of wild boar dropped by 84 and 95% within 1 year following ASF outbreak in the unmanaged and managed area, respectively. In the managed area, we showed that 11-22% additional mortality could be attributed to hunting. Our study suggests that ASF-induced mortality, by far, outweighs hunting-induced mortality in causing wild boar population decline and shows that intensified hunting in newly ASF-infected areas does not achieve much greater reduction of population size than what is already caused by the ASF virus.