Modelling the wind-borne spread of highly pathogenic avian influenza virus between farms

A quantitative understanding of the spread of contaminated farm dust between locations is a prerequisite for obtaining much-needed insight into one of the possible mechanisms of disease spread between farms. Here, we develop a model to calculate the quantity of contaminated farm-dust particles deposited at various locations downwind of a source farm and apply the model to assess the possible contribution of the wind-borne route to the transmission of Highly Pathogenic Avian Influenza virus (HPAI) during the 2003 epidemic in the Netherlands. The model is obtained from a Gaussian Plume Model by incorporating the dust deposition process, pathogen decay, and a model for the infection process on exposed farms. Using poultry- and avian influenza-specific parameter values we calculate the distance-dependent probability of between-farm transmission by this route. A comparison between the transmission risk pattern predicted by the model and the pattern observed during the 2003 epidemic reveals that the wind-borne route alone is insufficient to explain the observations although it could contribute substantially to the spread over short distance ranges, for example, explaining 24% of the transmission over distances up to 25 km.     

Sheep Movement Networks and the Transmission of Infectious Diseases

Background and Methodology

Various approaches have been used to investigate how properties of farm contact networks impact on the transmission of infectious diseases. The potential for transmission of an infection through a contact network can be evaluated in terms of the basic reproduction number, R ₀. The magnitude of R ₀ is related to the mean contact rate of a host, in this case a farm, and is further influenced by heterogeneities in contact rates of individual hosts. The latter can be evaluated as the second order moments of the contact matrix (variances in contact rates, and co-variance between contacts to and from individual hosts). Here we calculate these quantities for the farms in a country-wide livestock network: >15,000 Scottish sheep farms in each of 4 years from July 2003 to June 2007. The analysis is relevant to endemic and chronic infections with prolonged periods of infectivity of affected animals, and uses different weightings of contacts to address disease scenarios of low, intermediate and high animal-level prevalence.

Principal Findings and Conclusions

Analysis of networks of Scottish farms via sheep movements from July 2003 to June 2007 suggests that heterogeneities in movement patterns (variances and covariances of rates of movement on and off the farms) make a substantial contribution to the potential for the transmission of infectious diseases, quantified as R ₀, within the farm population. A small percentage of farms (<20%) contribute the bulk of the transmission potential (>80%) and these farms could be efficiently targeted by interventions aimed at reducing spread of diseases via animal movement.     

Evolutionary analysis of inter-farm transmission dynamics in a highly pathogenic avian influenza epidemic

Phylogenetic studies have largely contributed to better understand the emergence, spread and evolution of highly pathogenic avian influenza during epidemics, but sampling of genetic data has never been detailed enough to allow mapping of the spatiotemporal spread of avian influenza viruses during a single epidemic. Here, we present genetic data of H7N7 viruses produced from 72% of the poultry farms infected during the 2003 epidemic in the Netherlands. We use phylogenetic analyses to unravel the pathways of virus transmission between farms and between infected areas. In addition, we investigated the evolutionary processes shaping viral genetic diversity, and assess how they could have affected our phylogenetic analyses. Our results show that the H7N7 virus was characterized by a high level of genetic diversity driven mainly by a high neutral substitution rate, purifying selection and limited positive selection. We also identified potential reassortment in the three genes that we have tested, but they had only a limited effect on the resolution of the inter-farm transmission network. Clonal sequencing analyses performed on six farm samples showed that at least one farm sample presented very complex virus diversity and was probably at the origin of chronological anomalies in the transmission network. However, most virus sequences could be grouped within clearly defined and chronologically sound clusters of infection and some likely transmission events between farms located 0.8-13 Km apart were identified. In addition, three farms were found as most likely source of virus introduction in distantly located new areas. These long distance transmission events were likely facilitated by human-mediated transport, underlining the need for strict enforcement of biosafety measures during outbreaks. This study shows that in-depth genetic analysis of virus outbreaks at multiple scales can provide critical information on virus transmission dynamics and can be used to increase our capacity to efficiently control epidemics.     

Contribution of Company Affiliation and Social Contacts to Risk Estimates of Between-Farm Transmission of Avian Influenza

Background

Models of between-farm transmission of pathogens have identified service vehicles and social groups as risk factors mediating the spread of infection. Because of high levels of economic organization in much of the poultry industry, we examined the importance of company affiliation, as distinct from social contacts, in a model of the potential spread of avian influenza among broiler poultry farms in a poultry-dense region in the United States. The contribution of company affiliation to risk of between-farm disease transmission has not been previously studied.

Methodology/Principal Findings

We obtained data on the nature and frequency of business and social contacts through a national survey of broiler poultry growers in the United States. Daily rates of contact were estimated using Monte Carlo analysis. Stochastic modeling techniques were used to estimate the exposure risk posed by a single infectious farm to other farms in the region and relative risk of exposure for farms under different scenarios. The mean daily rate of vehicular contact was 0.82 vehicles/day. The magnitude of exposure risk ranged from <1% to 25% under varying parameters. Risk of between-farm transmission was largely driven by company affiliation, with farms in the same company group as the index farm facing as much as a 5-fold increase in risk compared to farms contracted with different companies. Employment of part-time workers contributed to significant increases in risk in most scenarios, notably for farms who hired day-laborers. Social visits were significantly less important in determining risk.

Conclusions/Significance

Biosecurity interventions should be based on information on industry structure and company affiliation, and include part-time workers as potentially unrecognized sources of viral transmission. Modeling efforts to understand pathogen transmission in the context of industrial food animal production should consider company affiliation in addition to geospatial factors and pathogen characteristics. Restriction of social contacts among farmers may be less useful in reducing between-farm transmission.     

The initialization and sensitivity of multigroup models for the transmission of HIV

We study two multigroup mathematical models of the spread of HIV. In the differential infectivity model, the infected population is divided into groups according to their infectiousness, and HIV is primarily spread by a small, highly infectious, group of superspreaders. In the staged-progression model, every infected individual goes through a series of infection stages and the virus is primarily spread by individuals in an initial highly infectious stage or in the late stages of the disease. We demonstrate the importance of choosing appropriate initial conditions, and define a new approach to distributing the initial population among the subgroups so as to minimize the artificial transients in the solutions due to unbalanced initial conditions. We demonstrate that the rate of removal in and out of a population is an important, yet often neglected, effect. We also illustrate the importance of distinguishing between the number of partners a person has and the number of contacts per partner. By assuming that people with many partners have fewer contacts per partner than people with few partners, we found that the epidemic is less sensitive to the partner acquisition rate than one might expect. However, because the probability of transmission of HIV per contact is low, the epidemic is very sensitive to the number of contacts per partner. Modeling this distinction is particularly important when estimating the impact of programs which encourage people to have fewer sexual partners.     

Expert Opinion on the Perceived Effectiveness and Importance of On-Farm Biosecurity Measures for Cattle and Swine Farms in Switzerland

Biosecurity is crucial for safeguarding livestock from infectious diseases. Despite the plethora of biosecurity recommendations, published scientific evidence on the effectiveness of individual biosecurity measures is limited. The objective of this study was to assess the perception of Swiss experts about the effectiveness and importance of individual on-farm biosecurity measures for cattle and swine farms (31 and 30 measures, respectively). Using a modified Delphi method, 16 Swiss livestock disease specialists (8 for each species) were interviewed. The experts were asked to rank biosecurity measures that were written on cards, by allocating a score from 0 (lowest) to 5 (highest). Experts ranked biosecurity measures based on their importance related to Swiss legislation, feasibility, as well as the effort required for implementation and the benefit of each biosecurity measure. The experts also ranked biosecurity measures based on their effectiveness in preventing an infectious agent from entering and spreading on a farm, solely based on transmission characteristics of specific pathogens. The pathogens considered by cattle experts were those causing Bluetongue (BT), Bovine Viral Diarrhea (BVD), Foot and Mouth Disease (FMD) and Infectious Bovine Rhinotracheitis (IBR). Swine experts expressed their opinion on the pathogens causing African Swine Fever (ASF), Enzootic Pneumonia (EP), Porcine Reproductive and Respiratory Syndrome (PRRS), as well as FMD. For cattle farms, biosecurity measures that improve disease awareness of farmers were ranked as both most important and most effective. For swine farms, the most important and effective measures identified were those related to animal movements. Among all single measures evaluated, education of farmers was perceived by the experts to be the most important and effective for protecting both Swiss cattle and swine farms from disease. The findings of this study provide an important basis for recommendation to farmers and policy makers.     

Informing Disease Models with Temporal and Spatial Contact Structure among GPS-Collared Individuals in Wild Populations

Contacts between hosts are essential for transmission of many infectious agents. Understanding how contacts, and thus transmission rates, occur in space and time is critical to effectively responding to disease outbreaks in free-ranging animal populations. Contacts between animals in the wild are often difficult to observe or measure directly. Instead, one must infer contacts from metrics such as proximity in space and time. Our objective was to examine how contacts between white-tailed deer (Odocoileus virginianus) vary in space and among seasons. We used GPS movement data from 71 deer in central New York State to quantify potential direct contacts between deer and indirect overlap in space use across time and space. Daily probabilities of direct contact decreased from winter (0.05–0.14), to low levels post-parturition through summer (0.00–0.02), and increased during the rut to winter levels. The cumulative distribution for the spatial structure of direct and indirect contact probabilities around a hypothetical point of occurrence increased rapidly with distance for deer pairs separated by 1,000 m – 7,000 m. Ninety-five percent of the probabilities of direct contact occurred among deer pairs within 8,500 m of one another, and 99% within 10,900 m. Probabilities of indirect contact accumulated across greater spatial extents: 95% at 11,900 m and 99% at 49,000 m. Contacts were spatially consistent across seasons, indicating that although contact rates differ seasonally, they occur proportionally across similar landscape extents. Distributions of contact probabilities across space can inform management decisions for assessing risk and allocating resources in response.     

The Influence of Between-Farm Distance and Farm Size on the Spread of Classical Swine Fever during the 1997–1998 Epidemic in The Netherlands

As the size of livestock farms in The Netherlands is on the increase for economic reasons, an important question is how disease introduction risks and risks of onward transmission scale with farm size (i.e. with the number of animals on the farm). Here we use the epidemic data of the 1997–1998 epidemic of Classical Swine Fever (CSF) Virus in The Netherlands to address this question for CSF risks. This dataset is one of the most powerful ones statistically as in this epidemic a total of 428 pig farms where infected, with the majority of farm sizes ranging between 27 and 1750 pigs, including piglets. We have extended the earlier models for the transmission risk as a function of between-farm distance, by adding two factors. These factors describe the effect of farm size on the susceptibility of a ‘receiving’ farm and on the infectivity of a ‘sending’ farm (or ‘source’ farm), respectively. Using the best-fitting model, we show that the size of a farm has a significant influence on both farm-level susceptibility and infectivity for CSF. Although larger farms are both more susceptible to CSF and, when infected, more infectious to other farms than smaller farms, the increase is less than linear. The higher the farm size, the smaller the effect of increments of farm size on the susceptibility and infectivity of a farm. Because of changes in the Dutch pig farming characteristics, a straightforward extrapolation of the observed farm size dependencies from 1997/1998 to present times would not be justified. However, based on our results one may expect that also for the current pig farming characteristics in The Netherlands, farm susceptibility and infectivity depend non-linearly on farm size, with some saturation effect for relatively large farm sizes.     

Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes.     

A study of fox (<Emphasis Type="Italic">Vulpes vulpes</Emphasis>) visits to farm buildings in Southwest England and the implications for disease management

Farm buildings may offer foraging opportunities for wild mammals, which may result in economic losses and the potential for disease transmission to livestock. Effective management to reduce such risks requires knowledge of the behaviour of wildlife visiting buildings. Remote surveillance was used to monitor red fox (Vulpes vulpes) activity in buildings on cattle farms in Southwest England over a period of 2 years. Frequent visits were observed throughout the year, during which foxes excreted and scent-marked on stored feed. This behaviour carries potential risks of pathogen transmission to cattle. Fox visits were positively correlated with maximum temperature in the preceding 24 h, and were most frequent to buildings where cattle were absent. The frequency of fox visits varied widely amongst farms, potentially reflecting variation in local farm management practices. On some farms, visits were frequent and involved behaviour with the potential to result in contamination of the environment with infectious pathogens. Risks of onward transmission to domestic animals could be managed by imposing tighter biosecurity measures.     

Risk maps for the spread of highly pathogenic avian influenza in poultry

Devastating epidemics of highly contagious animal diseases such as avian influenza, classical swine fever, and foot-and-mouth disease underline the need for improved understanding of the factors promoting the spread of these pathogens. Here the authors present a spatial analysis of the between-farm transmission of a highly pathogenic H7N7 avian influenza virus that caused a large epidemic in The Netherlands in 2003. The authors developed a method to estimate key parameters determining the spread of highly transmissible animal diseases between farms based on outbreak data. The method allows for the identification of high-risk areas for propagating spread in an epidemiologically underpinned manner. A central concept is the transmission kernel, which determines the probability of pathogen transmission from infected to uninfected farms as a function of interfarm distance. The authors show how an estimate of the transmission kernel naturally provides estimates of the critical farm density and local reproduction numbers, which allows one to evaluate the effectiveness of control strategies. For avian influenza, the analyses show that there are two poultry-dense areas in The Netherlands where epidemic spread is possible, and in which local control measures are unlikely to be able to halt an unfolding epidemic. In these regions an epidemic can only be brought to an end by the depletion of susceptible farms by infection or massive culling. The analyses provide an estimate of the spatial range over which highly pathogenic avian influenza viruses spread between farms, and emphasize that control measures aimed at controlling such outbreaks need to take into account the local density of farms.     

The biosecurity status and its associations with production and management characteristics in farrow-to-finish pig herds

Disease prevention through biosecurity measures is believed to be an important factor for improvement of the overall health status in animal production. This study aimed at assessing the levels of implementation of biosecurity measures in pig production in four European Union (EU) countries and to describe possible associations between the biosecurity level and farm and production characteristics. A cross-sectional study was conducted in 232 farrow-to-finish pig herds in Belgium, France, Germany and Sweden between December 2012 and December 2013. The biosecurity status in each of these herds was described and quantified by using the risk-based scoring tool Biocheck.UGent^TM (www.biocheck.ugent.be). Production and management characteristics, obtained from the herd management system and by interviewing the farmer, were analysed for their association with the biosecurity level. A causal path was designed to study statistical associations. The results showed that there was substantial room for improvement in the biosecurity status on many pig farms. Significant differences (P<0.01) both in internal and external biosecurity levels were observed between countries. The external biosecurity status, combining all measures taken to prevent disease introduction into the herd, was highest in Germany and lowest in France. The internal biosecurity status, combining all measures taken to prevent within herd disease transmission, was highest in Sweden and lowest, with a large variation, in Belgium. External biosecurity scores were in general higher compared to internal biosecurity scores. The number of pathogens vaccinated against was significantly associated with internal biosecurity status, suggesting an overall more preventive approach towards the risk of disease transmission. A higher external biosecurity was associated with more weaned piglets per sow per year. Furthermore also the weaning age and the mortality till weaning were highly associated with the number of weaned piglets per sow per year. The negative association observed between the biosecurity level and the estimated frequency of treatment against certain clinical signs of disease as a proxy for disease incidence is consistent with the hypothesis that a higher biosecurity level results in healthier animals. These findings promote an improved biosecurity status at pig farms and are of relevance in the discussion on alternative ways to keep animals healthy with a reduced necessity of antimicrobials; Prevention is better than cure!     

Additive‐Multiplicative Regression Models for Spatio‐Temporal Epidemics

An extension of the stochastic susceptible–infectious–recovered (SIR) model is proposed in order to accommodate a regression context for modelling infectious disease data. The proposal is based on a multivariate counting process specified by conditional intensities, which contain an additive epidemic component and a multiplicative endemic component. This allows the analysis of endemic infectious diseases by quantifying risk factors for infection by external sources in addition to infective contacts. Inference can be performed by considering the full likelihood of the stochastic process with additional parameter restrictions to ensure non‐negative conditional intensities. Simulation from the model can be performed by Ogata's modified thinning algorithm. As an illustrative example, we analyse data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of the classical swine fever virus in Germany during 1993–2004.     

Transmission parameters of the 2001 foot and mouth epidemic in Great Britain

Despite intensive ongoing research, key aspects of the spatial-temporal evolution of the 2001 foot and mouth disease (FMD) epidemic in Great Britain (GB) remain unexplained. Here we develop a Markov Chain Monte Carlo (MCMC) method for estimating epidemiological parameters of the 2001 outbreak for a range of simple transmission models. We make the simplifying assumption that infectious farms were completely observed in 2001, equivalent to assuming that farms that were proactively culled but not diagnosed with FMD were not infectious, even if some were infected. We estimate how transmission parameters varied through time, highlighting the impact of the control measures on the progression of the epidemic. We demonstrate statistically significant evidence for assortative contact patterns between animals of the same species. Predictive risk maps of the transmission potential in different geographic areas of GB are presented for the fitted models.     

Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing

The expected number of new infections per day per infectious person during an epidemic has been found to exhibit power-law scaling with respect to the susceptible fraction of the population. This is in contrast to the linear scaling assumed in traditional epidemiologic modeling. Based on simulated epidemic dynamics in synthetic populations representing Los Angeles, Chicago, and Portland, we find city-dependent scaling exponents in the range of 1.7-2.06. This scaling arises from variations in the strength, duration, and number of contacts per person. Implementation of power-law scaling of the new infection rate is quite simple for SIR, SEIR, and histogram-based epidemic models. Treatment of the effects of the social contact structure through this power-law formulation leads to significantly lower predictions of final epidemic size than the traditional linear formulation.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

Network frailty and the geometry of herd immunity

The spread of infectious disease through communities depends fundamentally on the underlying patterns of contacts between individuals. Generally, the more contacts one individual has, the more vulnerable they are to infection during an epidemic. Thus, outbreaks disproportionately impact the most highly connected demographics. Epidemics can then lead, through immunization or removal of individuals, to sparser networks that are more resistant to future transmission of a given disease. Using several classes of contact networks-Poisson, scale-free and small-world-we characterize the structural evolution of a network due to an epidemic in terms of frailty (the degree to which highly connected individuals are more vulnerable to infection) and interference (the extent to which the epidemic cuts off connectivity among the susceptible population that remains following an epidemic). The evolution of the susceptible network over the course of an epidemic differs among the classes of networks; frailty, relative to interference, accounts for an increasing component of network evolution on networks with greater variance in contacts. The result is that immunization due to prior epidemics can provide greater community protection than random vaccination on networks with heterogeneous contact patterns, while the reverse is true for highly structured populations.     

Predicting epidemics on directed contact networks

Contact network epidemiology is an approach to modeling the spread of infectious diseases that explicitly considers patterns of person-to-person contacts within a community. Contacts can be asymmetric, with a person more likely to infect one of their contacts than to become infected by that contact. This is true for some sexually transmitted diseases that are more easily caught by women than men during heterosexual encounters; and for severe infectious diseases that cause an average person to seek medical attention and thereby potentially infect health care workers (HCWs) who would not, in turn, have an opportunity to infect that average person. Here we use methods from percolation theory to develop a mathematical framework for predicting disease transmission through semi-directed contact networks in which some contacts are undirected-the probability of transmission is symmetric between individuals-and others are directed-transmission is possible only in one direction. We find that the probability of an epidemic and the expected fraction of a population infected during an epidemic can be different in semi-directed networks, in contrast to the routine assumption that these two quantities are equal. We furthermore demonstrate that these methods more accurately predict the vulnerability of HCWs and the efficacy of various hospital-based containment strategies during outbreaks of severe respiratory diseases.     

Biosecurity on cattle farms: a study in north-west England

Background

Few studies have considered in detail the range of biosecurity practices undertaken on cattle farms, particularly within the UK. In this study, 56 cattle farmers in a 100 km² area of north-west England were questioned regarding their on-farm biosecurity practices, including those relating to animal movements, equipment sharing and companies and contractors visiting the farms.

Methodology/Principal Findings

There was great variation between farms in terms of the type of, and extent to which, biosecurity was carried out. For example, the majority of farmers did not isolate stock bought onto the farm, but a small proportion always isolated stock. Many farmers administered treatments post-movement, primarily vaccinations and anthelmintics, but very few farms reported carrying out any health checks after moving animals on. In addition, there appeared to be much variation in the amount of biosecurity carried out by the different companies and contractors visiting the farms. Deadstock collectors and contracted animal waste spreaders, although likely to have a high potential for contact with infectious agents, were reported to infrequently disinfect themselves and their vehicles.

Conclusions/Significance

These findings suggest that although certain biosecurity practices are undertaken, many are carried out infrequently or not at all. This may be due to many factors, including cost (in time and money), lack of proven efficacies of practices and lack of relevant education of veterinary surgeons, producers and other herd health specialists. Further research exploring the reasons for the lack of uptake is imperative if preventive medicine is to be utilised fully by the farming industry.