Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis.

The most recent IPCC report presented further scientific evidence for global climate change in the twenty-first century. Important secondary effects of climate change include those on water resource availability, agricultural yields, urban healthy living, biodiversity, ecosystems, food security, and public health. The aim of this explorative study was to determine the range of expected airborne pathogen concentrations during a single outbreak or release in a future climate compared to a historical climatic period (1981-2010). We used five climate scenarios for the periods 2016-2045 and 2036-2065 defined by the Royal Netherlands Meteorological Institute and two conversion tools to create hourly future meteorological data sets. We modelled season-averaged airborne pathogen concentrations by means of an atmospheric dispersion model and compared these data to historical (1981-2010) modelled concentrations. Our results showed that modelled concentrations were modified several percentage points on average as a result of climate change. On average, concentrations were reduced in four out of five scenarios. Wind speed and global radiation were of critical importance, which determine horizontal and vertical dilution. Modelled concentrations decreased on average, but large positive and negative hourly averaged effects were calculated (from -67 to +639 %). This explorative study shows that further research should include pathogen inactivation and more detailed probability functions on precipitation, snow, and large-scale circulation.

Modeling of Salmonella Contamination in the Pig Slaughterhouse.

In this article we present a model for Salmonella contamination of pig carcasses in the slaughterhouse. This model forms part of a larger QMRA (quantitative microbial risk assessment) on Salmonella in slaughter and breeder pigs, which uses a generic model framework that can be parameterized for European member states, to describe the entire chain from farm-to-consumption and the resultant human illness. We focus on model construction, giving mathematical formulae to describe Salmonella concentrations on individual pigs and slaughter equipment at different stages of the slaughter process. Variability among individual pigs and over slaughterhouses is incorporated using statistical distributions, and simulated by Monte Carlo iteration. We present the results over the various slaughter stages and show that such a framework is especially suitable to investigate the effect of various interventions. In this article we present the results of the slaughterhouse module for two case study member states. The model outcome represents an increase in average prevalence of Salmonella contamination and Salmonella numbers at dehairing and a decrease of Salmonella numbers at scalding. These results show good agreement when compared to several other QMRAs and microbiological studies.

A QMRA Model for Salmonella in Pork Products During Preparation and Consumption.

As part of a quantitative microbiological risk assessment (QMRA) food chain model, this article describes a model for the consumer phase for Salmonella-contaminated pork products. Three pork products were chosen as a proxy for the entire pork product spectrum: pork cuts, minced meat patties, and fermented sausages. For pork cuts cross-contamination is considered the most important process and therefore it is modeled in detail. For minced meat, both cross-contamination and undercooking are the relevant processes. For those commodities bacterial growth during transport and storage is also modeled. Fermented sausages are eaten raw and the production may be defective. Variability between consumers' behavior and the impact of variability between production processes at the farm and abattoir are taken into account. Results indicate that Salmonella levels on products may increase significantly during transport and storage. Heating is very efficient at lowering concentrations, yet cross-contamination plays an important role in products that remain contaminated. For fermented sausage it is found that drying is important for Salmonella reduction. Sensitivity analysis revealed that cross- contamination factors "knife cleaning" and "preparation of a salad" are important parameters for pork cuts. For minced meat cleaning of the board, salad consumption, refrigerator temperature, and storage time were significant.

Human Q fever incidence is associated to spatiotemporal environmental conditions.

Airborne pathogenic transmission from sources to humans is characterised by atmospheric dispersion and influence of environmental conditions on deposition and reaerosolisation. We applied a One Health approach using human, veterinary and environmental data regarding the 2009 epidemic in The Netherlands, and investigated whether observed human Q fever incidence rates were correlated to environmental risk factors. We identified 158 putative sources (dairy goat and sheep farms) and included 2339 human cases. We performed a high-resolution (1 × 1 km) zero-inflated regression analysis to predict incidence rates by Coxiella burnetii concentration (using an atmospheric dispersion model and meteorological data), and environmental factors - including vegetation density, soil moisture, soil erosion sensitivity, and land use data - at a yearly and monthly time-resolution. With respect to the annual data, airborne concentration was the most important predictor variable (positively correlated to incidence rate), followed by vegetation density (negatively). The other variables were also important, but to a less extent. High erosion sensitive soils and the land-use fractions "city" and "forest" were positively correlated. Soil moisture and land-use "open nature" were negatively associated. The geographical prediction map identified the largest Q fever outbreak areas. The hazard map identified highest hazards in a livestock dense area. We conclude that environmental conditions are correlated to human Q fever incidence rate. Similar research with data from other outbreaks would be needed to more firmly establish our findings. This could lead to better estimations of the public health risk of a C. burnetii outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.

Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock - A review to inform risk assessment studies.

In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion. To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameterisations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment). The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation. A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose-response models to estimate infection probabilities in the population at risk.

Impact of acquired immunity and dose-dependent probability of illness on quantitative microbial risk assessment.

Dose-response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure-infection step; the infection-illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within-host dynamics of illness. We find that at low (average) doses, the infection-illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose-response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.

The protective effects of temporary immunity under imposed infection pressure.

The aim of this paper is to show in explicit detail that, due to the effects of waning and boosting of immunity, an increasing force of infection does not necessarily lead to an increase in the incidence of disease. Under certain conditions, a decrease of the force of infection may in fact lead to an increase of the incidence of disease. Thus we confirm and reinforce the conclusions from Águas et al. (2006), concerning pertussis. We do so, however, in the context of Campylobacter infections in humans deriving from animal reservoirs. For such an externally 'driven' epidemic we can ignore the transmission feedback cycle and treat the force of infection as a parameter. As this parameter is, to a certain extent, under public health control, our findings constitute an important warning: reducing exposure may not necessarily lead to a reduction in the occurrence of clinical illness. In a second part of the paper we relate the model parameters to the available data concerning campylobacteriosis.

Seroprevalence and risk factors for Toxoplasma gondii infection in domestic cats in The Netherlands.

Cats, as definitive hosts, play an important role in the transmission of Toxoplasma gondii. To determine the seroprevalence and risk factors for T. gondii infection in Dutch domestic cats, serum samples of 450 cats were tested for T. gondii antibodies by indirect ELISA. Binary mixture analysis was used to estimate the seroprevalence, the optimal cut-off value and the probability of being positive for each cat. The seroprevalence was estimated at 18.2% (95% CI: 16.6-20.0%) and showed a decrease with age in very young cats, an increase up to about 4 years old and ranged between 20 and 30% thereafter. Hunting (OR 4.1), presence of a dog in the household (OR 2.1), former stray cat (OR 3.3) and feeding of raw meat (OR 2.7) were identified as risk factors by multivariable logistic regression analysis. Prevalence differences were estimated by linear regression on the probabilities of being positive and used to calculate the population attributable fractions for each risk factor. Hunting contributed most to the T. gondii seroprevalence in the sampled population (35%).

A practical framework for the construction of a biotracing model: application to Salmonella in the pork slaughter chain.

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables "backward reasoning" when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.