Vulnerability of the British swine industry to classical swine fever

Label-Free Detection of African Swine Fever and Classical Swine Fever in the Point-of-Care Setting Using Photonic Integrated Circuits Integrated in a Microfluidic Device

Swine viral diseases have the capacity to cause significant losses and affect the sector's sustainability, a situation further exacerbated by the lack of antiviral drugs and the limited availability of effective vaccines. In this context, a novel point-of-care (POC) diagnostic device incorporating photonic integrated circuits (PICs), microfluidics and information, and communication technology into a single platform was developed for the field diagnosis of African swine fever (ASF) and classical swine fever (CSF). The device targets viral particles and has been validated using oral fluid and serum samples. Sensitivity, specificity, accuracy, precision, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated to assess the performance of the device, and PCR was the reference method employed. Its sensitivities were 80.97% and 79%, specificities were 88.46% and 79.07%, and DOR values were 32.25 and 14.21 for ASF and CSF, respectively. The proposed POC device and PIC sensors can be employed for the pen-side detection of ASF and CSF, thus introducing novel technological advancements in the field of animal diagnostics. The need for proper validation studies of POC devices is highlighted to optimize animal biosecurity.

Spatiotemporal analysis of reported classical swine fever outbreaks in China (2005-2018) and the influence of weather

Classical swine fever (CSF) is a viral disease that causes enormous economic losses in the swine industry in endemic countries including China. The aims of the current study were to describe the spatial distribution of annual CSF reports in China from 2005 to 2018, identify spatiotemporal clusters of annual CSF reports during this time period and to investigate the correlations between climate factors (rainfall, wind speed, temperature, vapour pressure and relative humidity) and the occurrence of CSF outbreaks. The strongest (Moran's index > 0.19), significant (p < .05) spatial clustering of reported outbreaks was observed during the first 4 years of the study period. This clustering was apparent in the four southern provinces of Guizhou, Guangxi, Guangdong and Yunnan. Five of the six significant (p ≤ .0001) spatiotemporal clusters occurred during the period 2005-2012. These were widely dispersed, with four clusters persisting for only 1 or 2 years, whereas two clusters (Jiangxi and Yunnan) persisted for 8 and 7 years, respectively. As a result of implementation of a national animal disease control plan and increasing coverage of vaccination, CSF outbreaks in China have generally been controlled and reduced, becoming sporadic in most provinces by 2018. We also confirmed that low relative humidity and high wind speed were significant weather variables associated with the occurrence of CSF. Furthermore, our study has confirmed that CSF is still endemic in some Chinese provinces, and we recommend that the national CSF control protocol be updated and standardized.

Bayesian nonparametric inference for heterogeneously mixing infectious disease models

Mathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

Infectious disease transmission models require assumptions about how the pathogen spreads between individuals. These assumptions may be somewhat arbitrary, particularly when it comes to describing how transmission varies between individuals of different types or in different locations, and may in turn lead to incorrect conclusions or policy decisions. We develop a general Bayesian nonparametric framework for transmission modeling that removes the need to make such specific assumptions with regard to the infection process. We use multioutput Gaussian process prior distributions to model different infection rates in populations containing multiple types of individuals. Further challenges arise because the transmission process itself is unobserved, and large outbreaks can be computationally demanding to analyze. We address these issues by data augmentation and a suitable efficient approximation method. Simulation studies using synthetic data demonstrate that our framework gives accurate results. We analyze an outbreak of foot and mouth disease in the United Kingdom, quantifying the spatial transmission mechanism between farms with different combinations of livestock.

High Prevalence and Factors Associated With the Distribution of the Integron intI1 and intI2 Genes in Scottish Cattle Herds

Integrons are genetic elements that capture and express antimicrobial resistance genes within arrays, facilitating horizontal spread of multiple drug resistance in a range of bacterial species. The aim of this study was to estimate prevalence for class 1, 2, and 3 integrons in Scottish cattle and examine whether spatial, seasonal or herd management factors influenced integron herd status. We used fecal samples collected from 108 Scottish cattle herds in a national, cross-sectional survey between 2014 and 2015, and screened fecal DNA extracts by multiplex PCR for the integrase genes intI1, intI2, and intI3. Herd-level prevalence was estimated [95% confidence interval (CI)] for intI1 as 76.9% (67.8-84.0%) and intI2 as 82.4% (73.9-88.6%). We did not detect intI3 in any of the herd samples tested. A regional effect was observed for intI1, highest in the North East (OR 11.5, 95% CI: 1.0-130.9, P = 0.05) and South East (OR 8.7, 95% CI: 1.1-20.9, P = 0.04), lowest in the Highlands. A generalized linear mixed model was used to test for potential associations between herd status and cattle management, soil type and regional livestock density variables. Within the final multivariable model, factors associated with herd positivity for intI1 included spring season of the year (OR 6.3, 95% CI: 1.1-36.4, P = 0.04) and watering cattle from a natural spring source (OR 4.4, 95% CI: 1.3-14.8, P = 0.017), and cattle being housed at the time of sampling for intI2 (OR 75.0, 95% CI: 10.4-540.5, P < 0.001). This study provides baseline estimates for integron prevalence in Scottish cattle and identifies factors that may be associated with carriage that warrant future investigation.

African swine fever outbreaks in China led to gross domestic product and economic losses

African swine fever (ASF) is a fatal and highly infectious haemorrhagic disease that has spread to all provinces in China-the world's largest producer and consumer of pork. Here we use an input-output model, partial equilibrium theory and a substitution indicator approach for handling missing data to develop a systematic valuation framework for assessing economic losses caused by ASF outbreaks in China between August 2018 and July 2019. We show that the total economic loss accounts for 0.78% of China's gross domestic product in 2019, with impacts experienced in almost all economic sectors through links to the pork industry and a substantial decrease in consumer surplus. Scenario analyses demonstrate that the worst cases of pig production reduction and price increase would trigger 1.4% and 2.07% declines in gross domestic product, respectively. These findings demonstrate an urgent need for rapid ASF containment and prevention measures to avoid future outbreaks and economic declines.

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.

Spatial super-spreaders and super-susceptibles in human movement networks

As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be “super-spreaders”. Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

When assessing the role of live animal trade networks in the spread of infectious diseases in livestock, attention has focused mainly on direct movements of animals between premises, whereas the role of haulage vehicles used during transport, an indirect route for disease transmission, has largely been ignored. Here, we have assessed the impact of sharing haulage vehicles from livestock transport service providers on the connectivity between farms as well as on the spread of swine infectious diseases in Great Britain (GB). Using all pig movement records between April 2012 and March 2014 in GB, we built a series of directed and weighted static multiplex networks consisting of two layers of identical nodes, where nodes (farms) are linked either by (a) the direct movement of pigs and (b) the shared use of haulage vehicles. The haulage contact definition integrates the date of the move and the duration Δ s that lorries are left contaminated by pathogens, hence accounting for the temporal aspect of contact events. For increasing Δ s , descriptive network analyses were performed to assess the role of haulage on network connectivity. We then explored how viruses may spread throughout the GB pig sector by computing the reproduction number R . Our results showed that sharing haulage vehicles increases the number of contacts between farms by >50% and represents an important driver of disease transmission. In particular, sharing haulage vehicles, even if Δ s  < 1 day, will limit the benefit of the standstill regulation, increase the number of premises that could be infected in an outbreak, and more easily raise R above 1. This work confirms that sharing haulage vehicles has significant potential for spreading infectious diseases within the pig sector. The cleansing and disinfection process of haulage vehicles is therefore a critical control point for disease transmission risk mitigation.

Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Mortality data are routinely collected for many livestock and poultry species, and they are often used for epidemiological purposes, including estimating transmission parameters. In this study, we infer transmission rates for African swine fever virus (ASFV), an important transboundary disease of swine, using mortality data collected from nine pig herds in the Russian Federation with confirmed outbreaks of ASFV. Parameters in a stochastic model for the transmission of ASFV within a herd were estimated using approximate Bayesian computation. Estimates for the basic reproduction number varied amongst herds, ranging from 4.4 to 17.3. This was primarily a consequence of differences in transmission rate (range: 0.7-2.2), but also differences in the mean infectious period (range: 4.5-8.3 days). We also found differences amongst herds in the mean latent period (range: 5.8-9.7 days). Furthermore, our results suggest that ASFV could be circulating in a herd for several weeks before a substantial increase in mortality is observed in a herd, limiting the usefulness of mortality data as a means of early detection of an outbreak. However, our results also show that mortality data are a potential source of data from which to infer transmission parameters, at least for diseases which cause high mortality.

A geo-computational algorithm for exploring the structure of diffusion progression in time and space

A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process.