Modelling farm-to-farm disease transmission through personnel movements: from visits to contacts, and back

Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle

Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared "fenceline" area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. "Fenceline" contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.

A foot and mouth disease ranking of risk using cattle transportation

Foot-and-mouth disease (FMD) is a highly infectious condition that affects domestic and wild cloven-hoofed animals. This disease has substantial economic consequences. Livestock movement is one of the primary causes of disease dissemination. The centrality properties of the livestock mobilization transportation network provide valuable information for surveillance and control of FMD. However, the same transportation network can be described by different centrality descriptions, making it challenging to prioritize the most vulnerable nodes in the transportation network. This work considers the construction of a single network risk ranking, which helps prioritize disease control measurements. Results show that the proposed ranking constructed on 2016 livestock mobilization data may predict an actual outbreak reported in the Cesar (Colombia) region in 2018, with a performance measured by the area under the receiver operating characteristic curve of 0.91. This result constitutes the first quantitative evidence of the predictive capacity of livestock transportation to target FMD outbreaks. This approach may help decision-makers devise strategies to control and prevent FMD.

Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Implementation of biosecurity on equestrian premises: A narrative overview

Biosecurity measures are designed to prevent the introduction and spread of pathogens, and play a vital role in the equine industry, controlling endemic diseases and reducing the threat of exotic disease incursion. Equestrian premises differ with respect to disease risks, biosecurity requirements and available facilities. This narrative review summarises reported frequency of implementation for selected biosecurity measures, as well as evidence relating to potential barriers to implementation of biosecurity on equestrian premises. Possible opportunities for improvement in the adoption of equine biosecurity measures are also discussed.

Genome-associations of extended-spectrum ß-lactamase producing (ESBL) or AmpC producing E. coli in small and medium pig farms from Khon Kaen province, Thailand

Thailand is undergoing rapid intensification of livestock production where small subsistence farms and medium sized commercial farms coexist. In medium farms, antimicrobials are prescribed by a veterinarian, whereas in small farms antimicrobial use remains largely unsupervised. The impact of these differences as well as other farming practices on the emergence and composition of antimicrobial resistance genes (ARGs) remains largely unknown. We analyzed 363 genomes of extended-spectrum ß-lactamase producing (ESBL) and/or AmpC producing Escherichia coli recovered from humans and pigs at small and medium farms from the Khon Kaen province, Thailand. We tested for genome-wide associations to identify links between ARGs, host, and farm size. Pig isolates from small farms were associated with mcr and qnr genes conferring resistance to colistin and fluoroquinolones, respectively. In contrast, pig isolates from medium farms were associated with ARGs conferring resistance to drugs commonly used on medium farms (i.e., streptomycin). ESBL plasmids from small farms co-carried ARGs conferring resistance to critically important antimicrobials more frequently compared to plasmid from medium farms. Frequent ARG combinations included bla_CTX-M-55 + qnrS1 (29.8% vs 17.5% in small and medium farms, respectively), bla_CTX-M-55 + qnrS1 + mcr-3.19 (5% vs 0%), bla_CTX-M-14 + qnrS1 (9.3% vs 6.2%), and bla_CTX-M-14 + qnrS1 + mcr-1.1 (3.1% vs 0%). The co-location on plasmids of ARGs conferring resistance to critically important antimicrobials as defined by the World Health Organization is concerning, and actions to curb their spread are urgently needed. Legislation on limiting antimicrobial sales and initiatives to better inform farmers and veterinarians on appropriate antimicrobial usage and farm biosecurity could help reduce antimicrobial use on farms.

Multilayer network analysis of FMD transmission and containment among beef cattle farms

As a highly contagious livestock viral disease, foot-and-mouth disease poses a great threat to the beef-cattle industry. Direct animal movement is always considered as a major route for between-farm transmission of FMD virus. Sharing contaminated equipment and vehicles have also attracted increasing interests as an indirect but considerable route for FMD virus transmission. With the rapid development of communication technologies, information-sharing techniques have been used to control epidemics. In this paper, we built farm-level time-series three-layer networks to simulate the between-farm FMD virus transmission in southwest Kansas by cattle movements (direct-contact layer) and truck visits (indirect-contact layer) and evaluate the impact of information-sharing techniques (information-sharing layer) on mitigating the epidemic. Here, the information-sharing network is defined as the structure that enables the quarantine of farms that are connected with infected farms. When a farm is infected, its infection status is shared with the neighboring farms in the information-sharing network, which in turn become quarantined. The results show that truck visits can enlarge the epidemic size and prolong the epidemic duration of the FMD outbreak by cattle movements, and that the information-sharing technique is able to mitigate the epidemic. The mitigation effect of the information-sharing network varies with the information-sharing network topology and different participation levels. In general, an increased participation leads to a decreased epidemic size and an increased quarantine size. We compared the mitigation performance of three different information-sharing networks (random network, contact-based network, and distance-based network) and found the outbreak on the network with contact-based information-sharing layer has the smallest epidemic size under almost any participation level and smallest quarantine size with high participation. Furthermore, we explored the potential economic loss from the infection and the quarantine. By varying the ratio of the average loss of quarantine to the loss of infection, we found high participation results in reduced economic losses under the realistic assumption that culling costs are much greater than quarantine costs.

Assessment Impacts of Ozone on Salmonella Typhimurium and Escherichia coli O157:H7 in Liquid Dairy Waste

Liquid dairy manure, which is produced in enormous quantities in flush dairy manure management systems, is commonly used as an alternative to chemical fertilizers. It provides nutrient benefits to crops and soils. While dairy waste is a well-accepted and widely used fertilizer, the presence of indicator organisms and human pathogens in manure may lead to pathogen contamination in crops and soils. This study is focused on the examination of ozone gas-based sterilization. In the past, ozone (O3) has been used for sanitizing various foods and solid surfaces, but the potential of O3 for eliminating human pathogens in liquid dairy waste is not studied yet. Pathogens such as Salmonella Typhimurium and Escherichia coli O157:H7 are reported to be present in liquid dairy manure, and this research evaluated the effects of various levels of ozone on the survival of these two pathogens. We designed a continuous type O3 treatment system that has four major components: (1) ozone generator using oxygen; (2) ozone concentration control by mixing with pure air; (3) continuous monitoring of ozone concentrations; and (4) ozone experiment chambers. Various levels of ozone (43.26, 87.40, and 132.46 mg·L−1) were produced in the ozone system, and subsequently, ozone was diffused through liquid manure. Liquid manure was exposed to ozone for multiple durations (30, 60, and 120 min). To determine the effectiveness of O3 in eliminating pathogens, time-series samples were collected and analyzed for determining the levels of S. typhimurium and E. coli O157:H7. Preliminary results showed that ozone concentrations of 132.46 mg/L, and exposure time of 120 min resulted in the reduced levels of E. coli and Salmonella. Low levels of ozone and limited exposure time were found to be less effective in pathogen removal potentially due to high solid contents. Additional studies carrying out experiments to evaluate the impacts of solids in combination with ozone concentrations will provide further insights into developing full-scale ozone-based treatment systems.

Pathogen transport amplifies or dilutes disease transmission depending on the host dose-response relationship

Pathogen transport by biotic or abiotic processes (e.g. mechanical vectors, wind, rain) can increase disease transmission by creating more opportunities for host exposure. But transport without replication has an inherent trade-off, that creating new venues for exposure decreases the average pathogen abundance at each venue. The host dose-response relationship is therefore required to correctly assess infection risk. We model and analyse two examples-biotic mechanical vectors in plant-pollinator networks, and abiotic-facilitated long-distance pathogen dispersal-to illustrate how oversimplifying the dose-response relationship can lead to incorrect epidemiological predictions. When the minimum infective dose is high, mechanical vectors amplify disease transmission less than suggested by simple compartment models, and may even dilute transmission. When long-distance dispersal leads to infrequent large exposures, models that assume a linear force of infection can substantially under-predict the speed of epidemic spread. Our work highlights an important general interplay between dose-response relationships and pathogen transport.

Preventive effect of on-farm biosecurity practices against highly pathogenic avian influenza (HPAI) H5N6 infection on commercial layer farms in the Republic of Korea during the 2016-17 epidemic: A case-control study

Highly pathogenic avian influenza virus (HPAIv) H5N6 has destructive consequences on the global poultry production system. Recently, a growing number of layer farms have been heavily damaged from the HPAIv epidemic due to the increased virulence of the virus and the intensification of the production system. Therefore, stakeholders should implement effective preventive practices at the farm level that are aligned with contingency measures at the national level to minimize poultry losses. However, numerous biosecurity protocols for layer farm workers to follow have been developed, impeding efficient prevention and control. Furthermore, the effectiveness of biosecurity practices varies with the geographical condition and inter-farm contact structures. Hence, the objective of our study was to examine the preventive effect of five biosecurity actions commonly practiced at layer farms in the Republic of Korea against HPAIv H5N6: (i) fence installation around a farm, ii) rodent control inside a farm; iii) disinfection booth for visitors for disinfection protocols, iv) an anterior room in the sheds before entering the bird area and v) boots changes when moving between sheds in the same farm. We conducted a case-control study on 114 layer case farms and 129 layer control farms during the 2016-17 HPAI epidemic. The odds ratios for five on-farm biosecurity practices implemented in those study groups were estimated as a preventive effect on the HPAI infection with covariates, including seven geographical conditions and three network metrics using Bayesian hierarchical logistic regression and geographical location weighted logistic regression. The results showed that the use of a disinfection booth for personnel reduced the odds of HPAIv H5N6 infection (adjusted odds ratio [AOR] = 0.002, 95 % credible interval [CrI] = 0.00007 - 0.025) with relatively small spatial variation (minimum AOR - maximum AOR: 0.084-0.263). Changing boots between sheds on the same farm reduced the odds of HPAIv H5N6 infection (AOR = 0.160, 95 % CrI = 0.024-0.852) with relatively wide spatial variation (minimum AOR - maximum AOR = 0.270-0.688). Therefore, enhanced personnel biosecurity protocols at the farm of entry for layer farms is recommended to effectively prevent and respond to HPAIv H5N6 infection under different local condition. Our study provides an important message for layer farmers to effectively implement on-farm biosecurity actions against HPAIv H5N6 infection at their farms by setting priorities based on their spatial condition and network position.

On the optimal control of SIR model with Erlang-distributed infectious period: isolation strategies

Mathematical models are formal and simplified representations of the knowledge related to a phenomenon. In classical epidemic models, a major simplification consists in assuming that the infectious period is exponentially distributed, then implying that the chance of recovery is independent on the time since infection. Here, we first attempt to investigate the consequences of relaxing this assumption on the performances of time-variant disease control strategies by using optimal control theory. In the framework of a basic susceptible–infected–removed (SIR) model, an Erlang distribution of the infectious period is considered and optimal isolation strategies are searched for. The objective functional to be minimized takes into account the cost of the isolation efforts per time unit and the sanitary costs due to the incidence of the epidemic outbreak. Applying the Pontryagin’s minimum principle, we prove that the optimal control problem admits only bang–bang solutions with at most two switches. In particular, the optimal strategy could be postponing the starting intervention time with respect to the beginning of the outbreak. Finally, by means of numerical simulations, we show how the shape of the optimal solutions is affected by the different distributions of the infectious period, by the relative weight of the two cost components, and by the initial conditions.

Simulating human behavioral changes in livestock production systems during an epidemic: The case of the US beef cattle industry

Human behavioral change around biosecurity in response to increased awareness of disease risks is a critical factor in modeling animal disease dynamics. Here, biosecurity is referred to as implementing control measures to decrease the chance of animal disease spreading. However, social dynamics are largely ignored in traditional livestock disease models. Not accounting for these dynamics may lead to substantial bias in the predicted epidemic trajectory. In this research, an agent-based model is developed by integrating the human decision-making process into epidemiological processes. We simulate human behavioral change on biosecurity practices following an increase in the regional disease incidence. We apply the model to beef cattle production systems in southwest Kansas, United States, to examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, increasing the percentage of risk-averse producers in the total population using a targeted strategy can more effectively reduce the number of infected producer locations and cattle losses compared to a random strategy. In addition, the reduction in epidemic size caused by the shifting of producers' risk attitudes towards risk-aversion is heavily dependent on the initial biosecurity level. A comprehensive investigation of the initial biosecurity status is recommended to inform risk communication strategy design.

Quantitative risk assessment of introduction of BVDV and BoHV-1 through indirect contacts based on implemented biosecurity measures in dairy farms of Spain

A stochastic quantitative risk assessment model was developed to estimate the annual probability of introduction of bovine viral diarrhea virus (BVDV) and bovine herpesvirus 1 (BoHV-1) on 127 dairy farms through indirect contacts. Vehicles transporting calves, cattle to slaughterhouse, dead animals, and mixture of feed, as well as visits by veterinarians and hoof trimmers, farm workers and contacts with neighbors were considered in the model. Data from biosecurity questionnaires of each farm, scientific literature and expert opinion from field veterinarians, animal vehicle drivers, hoof trimmers and personnel from rendering transport companies were used to estimate values for input parameters. Results showed that the annual probability of introducing BVDV or BoHV-1 through indirect contacts was very heterogeneous. The overall distribution of median values for each farm ranged from 0.5 to 14.6% and from 1.0 to 24.9% for BVDV and BoHV-1, respectively. The model identified that providing protective clothing and boots to visits, not allowing the animal vehicle driver to come into contact with animals present on the farm and ensuring that calf vehicles arrived empty, were the measures with the highest impact on the probability of infection for most farms. This model could be a useful tool to show the impact of the measures to farmers and veterinarians, thus increasing their awareness on biosecurity. In addition, it could support decision making on which measures should be prioritized in dairy cattle herds to reduce the probability of introduction of diseases.

Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.

The Epidemiological Framework for Biological Invasions (EFBI): an interdisciplinary foundation for the assessment of biosecurity threats

Emerging microparasite (e.g. viruses, bacteria, protozoa and fungi) epidemics and the introduction of non-native pests and weeds are major biosecurity threats worldwide. The likelihood of these threats is often estimated from probabilities of their entry, establishment, spread and ease of prevention. If ecosystems are considered equivalent to hosts, then compartment disease models should provide a useful framework for understanding the processes that underpin non-native species invasions. To enable greater cross-fertilisation between these two disciplines, the Epidemiological Framework for Biological Invasions (EFBI) is developed that classifies ecosystems in relation to their invasion status: Susceptible, Exposed, Infectious and Resistant. These states are linked by transitions relating to transmission, latency and recovery. This viewpoint differs markedly from the species-centric approaches often applied to non-native species. It allows generalisations from epidemiology, such as the force of infection, the basic reproductive ratio R0, super-spreaders, herd immunity, cordon sanitaire and ring vaccination, to be discussed in the novel context of non-native species and helps identify important gaps in the study of biological invasions. The EFBI approach highlights several limitations inherent in current approaches to the study of biological invasions including: (i) the variance in non-native abundance across ecosystems is rarely reported; (ii) field data rarely (if ever) distinguish source from sink ecosystems; (iii) estimates of the susceptibility of ecosystems to invasion seldom account for differences in exposure to non-native species; and (iv) assessments of ecosystem susceptibility often confuse the processes that underpin patterns of spread within -and between- ecosystems. Using the invasion of lakes as a model, the EFBI approach is shown to present a new biosecurity perspective that takes account of ecosystem status and complements demographic models to deliver clearer insights into the dynamics of biological invasions at the landscape scale. It will help to identify whether management of the susceptibility of ecosystems, of the number of vectors, or of the diversity of pathways (for movement between ecosystems) is the best way of limiting or reversing the population growth of a non-native species. The framework can be adapted to incorporate increasing levels of complexity and realism and to provide insights into how to monitor, map and manage biological invasions more effectively.

Risk factors for the incursion, spread and persistence of the foot and mouth disease virus in Eastern Rwanda

BACKGROUND

Identification of risk factors is crucial in Foot-and-mouth disease (FMD) control especially in endemic countries. In Rwanda, almost all outbreaks of Foot-and-Mouth Disease Virus (FMDV) have started in Eastern Rwanda. Identifying the risk factors in this area will support government control efforts. This study was carried out to identify and map different risk factors for the incursion, spread and persistence of FMDV in Eastern Rwanda. Questionnaires were administered during farm visits to establish risk factors for FMD outbreaks. Descriptive statistical measures were determined and odds ratios were calculated to determine the effects of risk factors on the occurrence of FMD. Quantum Geographic Information System (QGIS) was used to produce thematic maps on the proportion of putative risk factors for FMD per village.

RESULTS

Based on farmers' perceptions, 85.31% (with p < 0.01) experienced more outbreaks during the major dry season, a finding consistent with other reports in other parts of the world. Univariate analysis revealed that mixed farming (OR = 1.501, p = 0.163, CI = 95%), and natural breeding method (OR = 1.626; p = 0.21, CI = 95%) were associated with the occurrence of FMD indicating that the two risk factors could be responsible for FMD outbreaks in the farms. The occurrence of FMD in the farms was found to be significantly associated with lack of vaccination of calves younger than 12 months in herds (OR = 0.707; p = 0.046, CI = 95%).

CONCLUSIONS

This is the first study to describe risk factors for persistence of FMDV in livestock systems in Rwanda. However, further studies are required to understand the role of transboundary animal movements and genotypic profiles of circulating FMDV in farming systems in Rwanda.

Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm

Objectives

This study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).

Methods

Our risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.

Results

After 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the “outbreak” (average ± SD, 0.20 ± 0.31) than “non-outbreak” (0.18 ± 0.30) farms (p < 0.001).

Conclusion

The risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.

Seroprevalences of multi-pathogen and description of farm movement in pigs in two provinces in Vietnam

Background

In Vietnam, lack of animal health information is considered a major challenge for pig production. The main objective of this study was to assess the seroprevalences of five pathogens [porcine circovirus type 2 (PCV2), porcine reproductive and respiratory syndrome virus (PRRSV), mycoplasma hyopneumoniae (M. hyo), Japanese encephalitis virus (JEV) and leptospirosis] and to better characterize the farm movements through a survey.

Results

A total of 600 samples were collected from 120 farms from Bac Giang and Nghe An. Among unvaccinated herds, the highest seroprevalence was found for JE with 73.81% (95% CI: 68.39–78.74) in Bac Giang and 53.51% (95% CI 47.68–59.27) in Nghe An. Seroprevalences for PCV2 and M.hyo were 49.43% (95% CI: 45.06–53.80) and 46.06% (95% CI: 41.48–50.69) among unvaccinated animals. Accumulative co-infections for JE (86.25%) showed the highest level followed by M. hyo (66.25%) and PCV2 (62.50%). Three co-infections with JE had the highest positive rate (28.75%) followed by four co-infections (25.0%). Medium farms had relatively higher herd prevalences for all pathogens, except from leptospirosis. Overall, farmers exported/imported their pigs at the most 1–2 times every 6 months. Some respondents (5% for exportation and 20% for importation) had moved pigs more than 6 times over the last 6 months.

Conclusions

Our study provided another pool of evidence that showed that PCV2, PRRS and H. hyo are endemic in pigs in Vietnam. Given the economic impacts of these pathogens elsewhere, the findings confirm the need for studies to evaluate the association between antibody response and clinical relevance as well as to assess the economic impact of co-infections at farm level. We also found that high seroprevalences of JE and leptospirosis were detected in pigs. From a pubic health point of view, it is crucial to raise public awareness especially for high risk occupations (mainly pig farm workers).

Complex Networks: a Mini-review

Network analysis is a powerful tool that provides us a fruitful framework to describe phenomena related to social, technological, and many other real-world complex systems. In this paper, we present a brief review about complex networks including fundamental quantities, examples of network models, and the essential role of network topology in the investigation of dynamical processes as epidemics, rumor spreading, and synchronization. A quite of advances have been provided in this field, and many other authors also review the main contributions in this area over the years. However, we show an overview from a different perspective. Our aim is to provide basic information to a broad audience and more detailed references for those who would like to learn deeper the topic.

When resolution does matter: Modelling indirect contacts in dairy farms at different levels of detail

Animal exchanges are considered the major pathway for between-farm transmission of many livestock infectious diseases. Yet, vehicles and operators visiting several farms during routine activities can also contribute to disease spread. Indeed, if contaminated, they can act as mechanical vectors of fomites, generating indirect contacts between visited farms. While data on animal exchanges is often available in national databases, information about the daily itineraries of trucks and operators is rare because difficult to obtain. Thus, some unavoidable approximations have been frequently introduced in the description of indirect contacts in epidemic models. Here, we showed that the level of detail in such description can significantly affect the predictions on disease dynamics. Our analyses focused on the potential spread of a disease in a dairy farm system subject of a comprehensive data collection campaign on calf transportations. We developed two temporal multilayer networks to model between-farm contacts generated by either animal exchanges (direct contacts) and connections operated by trucks moving calves (indirect contacts). The complete model used the full knowledge of the daily trucks' itineraries, while the partial informed one used only a subset of such available information. To account for various conditions of pathogen survival ability and effectiveness of cleaning operations, we performed a sensitivity analysis on trucks' contamination period. An accurate description of indirect contacts was crucial both to correctly predict the final size of epidemics and to identify the seed farms responsible for generating the most severe outbreaks. The importance of detailed information emerged even more clearly in the case of short contamination periods. Our conclusions could be extended to between-farm contacts generated by other vehicles and operators. Overcoming these information gaps would be decisive for a deeper understanding of epidemic spread in livestock and to develop effective control plans.

Biosecurity and herd health management practices on Canadian dairy farms

The Canadian dairy industry has created national standards to support the adoption of biosecurity practices and to reduce disease risks across the country. There is, however, very little information on the adoption of these practices. The present study aimed to describe the adoption of biosecurity practices on Canadian dairy farms shortly after the creation of the national standards and to identify demographic factors associated with practice adoption. In 2015, 2 questionnaires (phase 1 and 2) were administered to Canadian dairy farmers during an extensive cross-sectional study. Associations between adoption of biosecurity practices as well as associations between adoption of these practices and demographic variables were tested using multiple correspondence analysis. A total of 1,157 questionnaires were completed in phase 1, and a subsample of 368 respondents was selected using stratified random sampling to complete phase 2 during visits to the farms. There was a lack of investigation into general disease syndromes such as a high prevalence of abortion or an unexplained death (38 and 22% of respondents, respectively). Biosecurity measures within herds and between herds to minimize the spread of infection were not widely adopted (e.g., 27% of the respondents never housed sick or lame animals in their calving pen, 41% had closed herds, and 25 and 48% of the open herds had no strategy for introducing new additions and reintroducing returning animals, respectively). Cleanliness of the cows before calving was always ensured by 29% of the respondents, and 27% of the respondents reported always sanitizing the calving pen after each calving. Less than 15% of the respondents had measures in place to limit or control visitors coming on their farm. Moreover, less than half of the respondents reported requiring family members, employees, and visitors to wear farm-designated or clean boots and coveralls. From the multiple correspondence analysis, 2 dimensions were retained and were summarized as "animal movement, calving area, and visitor biosecurity" and "employee biosecurity." Geographical region, type of housing, and milk production were associated with the "employee biosecurity" dimension. The present study demonstrates that many important biosecurity practices are not implemented on Canadian dairy farms; therefore, efforts to promote the adoption of these practices will be necessary to improve biosecurity in dairy herds.

Optimal control of epidemic size and duration with limited resources

The total number of infections (epidemic size) and the time needed for the infection to go extinct (epidemic duration) represent two of the main indicators for the severity of infectious disease epidemics in human and livestock. However, few attempts have been made to address the problem of minimizing at the same time the epidemic size and duration from a theoretical point of view by using optimal control theory. Here, we investigate the multi-objective optimal control problem aiming to minimize, through either vaccination or isolation, a suitable combination of epidemic size and duration when both maximum control effort and total amount of resources available during the entire epidemic period are limited. Application of Pontryagin's Maximum Principle to a Susceptible-Infected-Removed epidemic model, shows that, when the resources are not sufficient to maintain the maximum control effort for the entire duration of the epidemic, the optimal vaccination control admits only bang-bang solutions with one or two switches, while the optimal isolation control admits only bang-bang solutions with one switch. We also find that, especially when the maximum control effort is low, there may exist a trade-off between the minimization of the two objectives. Consideration of this conflict among objectives can be crucial in successfully tackling real-world problems, where different stakeholders with potentially different objectives are involved. Finally, the particular case of the minimum time optimal control problem with limited resources is discussed.

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'.

Microbial species and biodiversity in settling dust within and between pig farms

The airborne fungal and bacterial species present in pig farm dust have not been well characterised even though these bioaerosols are known to cause inflammation and other airway maladies. In this study, the microbial species and composition in airborne dust within and between pig farms were investigated. Passively sedimenting dust from six pig farms were collected using electrostatic dust collectors. The bacterial and fungal species were identified using matrix-assisted laser desorption-ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and next generation sequencing (NGS). Dust samples taken within the same stable section revealed high resemblance and stability. Constrained statistical analysis of the microbial community compositions indicated that the types of stable did not appear to have a great effect on the bacterial and fungal β-diversity. In contrast to this, the farm from which samples were taken appeared to have the greatest effect on the bacterial β-diversity, but this trend was not observed for the fungal β-diversity. The most common bacteria and fungi according to NGS data were anaerobes typically associated with the pig intestinal tract and yeasts respectively. Bacterial sedimentation varied at a rate between 10³ and 10⁹ CFU/m²/day, with the most common species after aerobic incubation being Aerococcus viridans and Staphylococcus equorum, while Clostridium perfringens and Staphylococcus simulans were the most common species after anaerobic incubation. A total of 28 different species of bacteria and fungi were classifiable as pathogens. In conclusion, the biodiversity in pig farm dust shows a high diversity of bacterial species. However, samples from the same stable section resembled each other, but also different sections within the same farm also resembled each other, thus indicating a high degree of community stability in the dust source. In regards to fungal identification, the biodiversity was observed to be similar between samples from different stable sections and farms, indicating a higher degree of similarities in the mycobiomes found across pig farms studied.

Contact chains of cattle farms in Great Britain

Network analyses can assist in predicting the course of epidemics. Time-directed paths or 'contact chains' provide a measure of host-connectedness across specified timeframes, and so represent potential pathways for spread of infections with different epidemiological characteristics. We analysed networks and contact chains of cattle farms in Great Britain using Cattle Tracing System data from 2001 to 2015. We focused on the potential for between-farm transmission of bovine tuberculosis, a chronic infection with potential for hidden spread through the network. Networks were characterized by scale-free type properties, where individual farms were found to be influential 'hubs' in the network. We found a markedly bimodal distribution of farms with either small or very large ingoing and outgoing contact chains (ICCs and OCCs). As a result of their cattle purchases within 12-month periods, 47% of British farms were connected by ICCs to more than 1000 other farms and 16% were connected to more than 10 000 other farms. As a result of their cattle sales within 12-month periods, 66% of farms had OCCs that reached more than 1000 other farms and 15% reached more than 10 000 other farms. Over 19 000 farms had both ICCs and OCCs reaching more than 10 000 farms for two or more years. While farms with more contacts in their ICCs or OCCs might play an important role in disease spread, farms with extensive ICCs and OCCs might be particularly important by being at higher risk of both acquiring and disseminating infections.

A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale

The effectiveness of infectious disease control depends on the ability of health managers to act in a coordinated way. However, with regards to non-notifiable animal diseases, farmers individually decide whether or not to implement control measures, leading to positive and negative externalities for connected farms and possibly impairing disease control at a regional scale. Our objective was to facilitate the identification of optimal incentive schemes at a collective level, adaptive to the epidemiological situation, and minimizing the economic costs due to a disease and its control. We proposed a modelling framework based on Markov Decision Processes (MDP) to identify effective strategies to control PorcineReproductive andRespiratorySyndrome (PRRS), a worldwide endemicinfectiousdisease thatsignificantly impactspig farmproductivity. Using a stochastic discrete-time compartmental model representing PRRS virus spread and control within a group of pig herds, we defined the associated MDP. Using a decision-tree framework, we translated the optimal policy into a limited number of rules providing actions to be performed per 6-month time-step according to the observed system state. We evaluated the effect of varying costs and transition probabilities on optimal policy and epidemiological results. We finally identifiedan adaptive policy that gave the best net financial benefit. The proposed framework is a tool for decision support as it allows decision-makers to identify the optimal policy and to assess its robustness to variations in the values of parameters representing an impact of incentives on farmers' decisions.

The microbial killing capacity of aqueous and gaseous ozone on different surfaces contaminated with dairy cattle manure

A high reactivity and leaving no harmful residues make ozone an effective disinfectant for farm hygiene and biosecurity. Our objectives were therefore to (1) characterize the killing capacity of aqueous and gaseous ozone at different operational conditions on dairy cattle manure-based pathogens (MBP) contaminated different surfaces (plastic, metal, nylon, rubber, and wood); (2) determine the effect of microbial load on the killing capacity of aqueous ozone. In a crossover design, 14 strips of each material were randomly assigned into 3 groups, treatment (n = 6), positive-control (n = 6), and negative-control (n = 2). The strips were soaked in dairy cattle manure with an inoculum level of 10⁷–10⁸ for 60 minutes. The treatment strips were exposed to aqueous ozone of 2, 4, and 9 ppm and gaseous ozone of 1and 9 ppm for 2, 4, and 8 minutes exposure. 3M™ Petrifilm™ rapid aerobic count plate and plate reader were used for bacterial culture. On smooth surfaces, plastic and metal, aqueous ozone at 4 ppm reduced MBP to a safe level (≥5-log₁₀) within 2 minutes (6.1 and 5.1-log₁₀, respectively). However, gaseous ozone at 9 ppm for 4 minutes inactivated 3.3-log₁₀ of MBP. Aqueous ozone of 9 ppm is sufficient to reduce MBP to a safe level, 6.0 and 5.4- log_10, on nylon and rubber surfaces within 2 and 8 minutes, respectively. On complex surfaces, wood, both aqueous and gaseous ozone at up to 9 ppm were unable to reduce MBP to a safe level (3.6 and 0.8-log₁₀, respectively). The bacterial load was a strong predictor for reduction in MBP (P<0.0001, R² = 0.72). We conclude that aqueous ozone of 4 and 9 ppm for 2 minutes may provide an efficient method to reduce MBP to a safe level on smooth and moderately rough surfaces, respectively. However, ozone alone may not an adequate means of controlling MBP on complex surfaces.

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

BACKGROUND

The Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.

SURVEY METHODOLOGY

In this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms "dynamics," "mathematical model," "modeling," and "vector-borne" together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were "compartmental," "spatial," "metapopulation," "network," "individual-based," "agent-based" AND "Zika." All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.

RESULTS

We found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.

DISCUSSION

Mathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.