Metapopulation dynamics of Escherichia coli O157 in cattle: an exploratory model

Frequency and risk-factors analysis of Escherichia coli O157:H7 in Bali-cattle

Cattle are known as the main reservoir of zoonotic agents verocytotoxin-producing Escherichia coli. These bacteria are usually isolated from calves with diarrhea and/or mucus and blood. Tolerance of these agents to the environmental conditions will strengthen of their transmission among livestock. A total of 238 cattle fecal samples from four sub-districts in Badung, Bali were used in this study. Epidemiological data observed include cattle age, sex, cattle rearing system, the source of drinking water, weather, altitude, and type of cage floor, the cleanliness of cage floor, the slope of cage floor, and the level of cattle cleanliness. The study was initiated by culturing of samples onto eosin methylene blue agar, then Gram stained, and tested for indole, methyl-red, voges proskauer, and citrate, Potential E.coli isolates were then cultured onto sorbitol MacConkey agar, and further tested using O157 latex agglutination test and H7 antisera. Molecular identification was performed by analysis of the 16S rRNA gene, and epidemiological data was analyzed using STATA 12.0 software. The results showed, the prevalence of E. coli O157:H7 in cattle at Badung regency was 6.30% (15/238) covering four sub districts i.e. Petang, Abiansemal, Mengwi, and Kuta which their prevalence was 8.62%(5/58), 10%(6/60), 3.33%(2/60), and 3.33(2/60)%, respectively. The analysis of 16S rRNA gene confirmed of isolates as an E. coli O157:H7 strain with 99% similarities. Furthermore, the risk factors analysis showed that the slope of the cage floor has a highly significant effect (P<0.05) to the distribution of infection. Consequently, implementing this factor must be concerned in order to decrease of infection.

Mathematical Modeling Tools to Study Preharvest Food Safety

This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

Implications of within-farm transmission for network dynamics: consequences for the spread of avian influenza

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Cross-scale dynamics were investigated for avian influenza in British poultry.

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Transmission risk is dependent on the assumed within-flock transmission mode.

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Transmission risk may not scale with transmissibility or flock size.

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Transmission risk corresponds with between-farm impact for 28% of farms.

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These results have implications for targeted disease control at the farm-level.

The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000–35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures.

How to make predictions about future infectious disease risks

Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models.

Escherichia coli O157 infection on cattle farms: the formulation of the force of infection and its effect on control effectiveness

The kernel of modelling transmission dynamics of infectious diseases lies in constructing the force of infection (FOI). Traditionally, it was based on mass-action law. In this paper, we show, based on survey data of Escherichia coli O157 infection on Scottish cattle farms, that the actual form of FOI deviates greatly from mass-action law. Further, control effectiveness deviates qualitatively: the epidemic of mass-action FOI can be controlled with a control effort larger than the so-called herd immunity, while the epidemic inferred from the survey data of E. coli O157 infection was shown to be difficult to control. This indicates that, at least for E. coli O157 infection on cattle farms, it is risky to rely on models of transmission dynamics that were based on mass-action law to design the optimal intervention programme for infectious diseases. This suggests the importance of collecting epidemic data and model selection from data-driven models to infer the most likely model of transmission dynamics.

Spread of E. coli O157 infection among Scottish cattle farms: stochastic models and model selection

Identifying risk factors for the presence of Escherichia coli O157 infection on cattle farms is important for understanding the epidemiology of this zoonotic infection in its main reservoir and for informing the design of interventions to reduce the public health risk. Here, we use data from a large-scale field study carried out in Scotland to fit both "SIS"-type dynamical models and statistical risk factor models. By comparing the fit (assessed using maximum likelihood) of different dynamical models we are able to identify the most parsimonious model (using the AIC statistic) and compare it with the model suggested by risk factor analysis. Both approaches identify 2 key risk factors: the movement of cattle onto the farm and the number of cattle on the farm. There was no evidence for a role of other livestock species or seasonality, or of significant risk of local spread. However, the most parsimonious dynamical model does predict that farms can infect other farms through routes other than cattle movement, and that there is a nonlinear relationship between the force of infection and the number of infected farms. An important prediction from the most parsimonious model is that although only approximately 20% farms may harbour E. coli O157 infection at any given time approximately 80% may harbour infection at some point during the course of a year.

Escherichia coli O157 infection on Scottish cattle farms: dynamics and control

In this study, we parametrize a stochastic individual-based model of the transmission dynamics of Escherichia coli O157 infection among Scottish cattle farms and use the model to predict the impacts of both targeted and non-targeted interventions. We first generate distributions of model parameter estimates using Markov chain Monte Carlo methods. Despite considerable uncertainty in parameter values, each set of parameter values within the 95th percentile range implies a fairly similar impact of interventions. Interventions that reduce the transmission coefficient and/or increase the recovery rate of infected farms (e.g. via vaccination and biosecurity) are much more effective in reducing the level of infection than reducing cattle movement rates, which improves effectiveness only when the overall control effort is small. Targeted interventions based on farm-level risk factors are more efficient than non-targeted interventions. Herd size is a major determinant of risk of infection, and our simulations confirmed that targeting interventions at farms with the largest herds is almost as effective as targeting based on overall risk. However, because of the striking characteristic that the infection force depends weakly on the number of infected farms, no interventions that are less than 100 per cent effective can eradicate E. coli O157 infection from Scottish cattle farms, implying that eliminating the disease is impractical.

Model or meal? Farm animal populations as models for infectious diseases of humans

In recent decades, theory addressing the processes that underlie the dynamics of infectious diseases has progressed considerably. Unfortunately, the availability of empirical data to evaluate these theories has not grown at the same pace. Although laboratory animals have been widely used as models at the organism level, they have been less appropriate for addressing issues at the population level. However, farm animal populations can provide empirical models to study infectious diseases at the population level.

Super-shedding and the link between human infection and livestock carriage of Escherichia coli O157

Cattle that excrete more Escherichia coli O157 than others are known as super-shedders. Super-shedding has important consequences for the epidemiology of E. coli O157 in cattle--its main reservoir--and for the risk of human infection, particularly owing to environmental exposure. Ultimately, control measures targeted at super-shedders may prove to be highly effective. We currently have only a limited understanding of both the nature and the determinants of super-shedding. However, super-shedding has been observed to be associated with colonization at the terminal rectum and might also occur more often with certain pathogen phage types. More generally, epidemiological evidence suggests that super-shedding might be important in other bacterial and viral infections.