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Calculation of steady states and stability.

The dilution effect, where an increase in biodiversity results in a reduction in the prevalence of an infectious disease, has been the subject of speculation and controversy. Conversely, an amplification effect occurs when increased biodiversity is related to an increase in prevalence. We explore the conditions under which these effects arise, using multi species compartmental models that integrate ecological and epidemiological interactions. We introduce three potential metrics for quantifying dilution and amplification, one based on infection prevalence in a focal host species, one based on the size of the infected subpopulation of that species and one based on the basic reproduction number. We introduce our approach in the simplest epidemiological setting with two species, and show that the existence and strength of a dilution effect is influenced strongly by the choices made to describe the system and the metric used to gauge the effect.We show that our method can be generalized to any number of species and to more complicated ecological and epidemiological dynamics. Our method allows a rigorous analysis of ecological systems where dilution effects have been postulated, and contributes to future progress in understanding the phenomenon of dilution in the context of infectious disease dynamics and infection risk.

Interspecific interactions in ecological communities are the main mechanisms that determine structure, functioning, and stability of ecosystems (May, 1972, 1973; Neutel et al., 2002; Alessina and Tang, 2012; Mougi and Kondoh, 2012, 2014). These interactions can be qualitatively positive, negative, or neutral, and pairs of these interactions between two species may be of opposite sign (e.g., trophic, parasitic) or of equivalent sign (e.g., mutualistic, competitive). Most of the research on ecological interactions has focused on feeding relations (Odum, 1971; Pimm, 1982; Levin et al., 2009; McCann, 2011; Moore and de Ruiter, 2012), but in recent studies of ecological communities this was extended to parasitic (Huxham et al., 1995; Thompson et al., 2004; Lafferty et al., 2006; Kuris et al., 2008) and non-parasitic non-trophic relations (Thebault and Fountaine, 2010; Fontaine et al., 2011; Kéfi et al., 2012; Mougi and Kondoh, 2012; Sauve et al., 2014). In this chapter, we focus on parasitic relations and notably on the question of how trophic interactions and infectious agents mutually influence each other. Here we will refer to the combined classes of infectious species as parasites (see next section for details). The impact of parasites in an ecological community can be quantified through their direct influence on the food-web structure, as well as more indirectly through the way they influence physiological traits of host species and trophic relations of the host and non-host species (Kéfi et al., 2012; Selakovic et al., 2014). In this chapter we first briefly discuss the diversity of parasitic interactions, their relationships with host and non-host species, as well as their effects on a simple consumer–resource relationship consisting of one host and one non-host species. The largest part of the chapter is devoted to exploring a basic model, to show how intricately ecological and epidemiological effects are interwoven, even in the simplest possible ecosystem consisting of two species. Even though this model is basic in the sense that it is low dimensional and not meant to realistically represent any particular system, the analysis does hint at broader ecological insight, for example into possible differences between terrestrial and aquatic ecosystems based on parasitic interaction. The simple analysis highlights the need to study the link between ecology and infectious disease epidemiology in more realistic models.

The question of how biodiversity influences the emergence of infectious diseases is the subject of ongoing research. A set of nonlinear differential equations is been used to explore the interactions between ecology and epidemiology. The model allows for frequency-dependent transmission of infection within host species, and density-dependent transmission between species, via the environment or a vector. Three examples are discussed. It is shown that removing a pathogen may increase a consumer population, decreasing its resource. It is then shown that the presence of a pathogen could enable a predator and a prey species to coexist. Finally the dilution effect, by which increasing biodiversity reduces the transmission of an infectious disease, is investigated.

Accurate estimation of population size is key to understanding the ecology of disease vectors, as well as the epidemiology of the pathogens they carry and to plan effective control activities. Population size can be estimated through mark-release-recapture (MRR) experiments that are based on the assumption that the ratio of recaptured individuals to the total captures approximates the ratio of marked individuals released to the total population. However, methods to obtain population size estimates usually consider pooled data and are often based on the total number of marked and unmarked captures. We here present a logistic regression model, based on the principle of the well-known Fisher-Ford method, specific for MRR experiments where the information available is the number of marked mosquitoes released, the number of marked and unmarked mosquitoes caught in each trap and on each day, and the geographic coordinates of the traps. The model estimates population size, taking into consideration the distance between release points and traps, the time between release and recapture, and the loss of marked mosquitoes to death or dispersal. The performance and accuracy of the logistic regression model has been assessed using simulated data from known population sizes. We then applied the model to data from MRR experiments with Aedes albopictus Skuse performed on the campus of "Sapienza" University in Rome (Italy). © 2013 Entomological Society of America.

We address the interaction of ecological processes, such as consumer-resource relationships and competition, and the epidemiology of infectious diseases spreading in ecosystems. Modelling such interactions seems essential to understand the dynamics of infectious agents in communities consisting of interacting host and non-host species. We show how the usual epidemiological next-generation matrix approach to characterize invasion into multi-host communities can be extended to calculate R0 , and how this relates to the ecological community matrix. We then present two simple examples to illustrate this approach. The first of these is a model of the rinderpest, wildebeest, grass interaction, where our inferred dynamics qualitatively matches the observed phenomena that occurred after the eradication of rinderpest from the Serengeti ecosystem in the 1980s. The second example is a prey-predator system, where both species are hosts of the same pathogen. It is shown that regions for the parameter values exist where the two host species are only able to coexist when the pathogen is present to mediate the ecological interaction.

A common approach to analyse stability of biological communities is to calculate the interaction strength matrix. Problematic in this approach is defining intraspecific interaction strengths, represented by diagonal elements in the matrix, due to a lack of empirical data for these strengths. Theoretical studies have shown that an overall increase in these strengths enhances stability. However, the way in which the pattern in intraspecific interaction strengths, i.e. the variation in these strengths between species, influences stability has received little attention. We constructed interaction strength matrices for 11 real soil food webs in which four patterns for intraspecific interaction strengths were chosen, based on the ecological literature. These patterns included strengths that were (1) similar for all species, (2) trophic level dependent, (3) biomass dependent, or (4) death rate dependent. These four patterns were analysed for their influence on (1) ranking food webs by their stability and (2) the response in stability to variation of single interspecific interaction strengths. The first analysis showed that ranking the 11 food webs by their stability was not strongly influenced by the choice of diagonal pattern. In contrast, the second analysis showed that the response of food web stability to variation in single interspecific interaction strengths was sensitive to the choice of diagonal pattern. Notably, stability could increase using one pattern and decrease using another. This result asks for deliberate approaches to choose diagonal element values in order to make predictions on how particular species, interactions, or other food web parameters affect food web stability.

The basic reproduction number ℛ 0 is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation of ℛ 0 where finitely many different categories of individuals are recognized. We clear up confusion that has been around in the literature concerning the construction of this matrix, specifically for the most frequently used so-called compartmental models. We present a detailed easy recipe for the construction of the NGM from basic ingredients derived directly from the specifications of the model. We show that two related matrices exist which we define to be the NGM with large domain and the NGM with small domain . The three matrices together reflect the range of possibilities encountered in the literature for the characterization of ℛ 0 . We show how they are connected and how their construction follows from the basic model ingredients, and establish that they have the same non-zero eigenvalues, the largest of which is the basic reproduction number ℛ 0 . Although we present formal recipes based on linear algebra, we encourage the construction of the NGM by way of direct epidemiological reasoning, using the clear interpretation of the elements of the NGM and of the model ingredients. We present a selection of examples as a practical guide to our methods. In the appendix we present an elementary but complete proof that ℛ 0 defined as the dominant eigenvalue of the NGM for compartmental systems and the Malthusian parameter r , the real-time exponential growth rate in the early phase of an outbreak, are connected by the properties that ℛ 0 > 1 if and only if r > 0, and ℛ 0 = 1 if and only if r = 0.

The seminal paper by Daniel Bernoulli published in 1766 is put into a new perspective. After a short account of smallpox inoculation and of Bernoulli’s life, the motivation for that paper and its impact are described. It determines the age-specific equilibrium prevalence of immune individuals in an endemic potentially lethal infectious disease. The gain in life expectancy after elimination of this cause of death can be explicitly expressed in terms of the case fatality and the endemic prevalence of susceptibles. D’Alembert developed in 1761 an alternative method for dealing with competing risks of death, which is also applicable to non-infectious diseases. Bernoulli’s formula for the endemic prevalence of susceptibles has so far escaped attention. It involves the lifetime risk of the infection, the force of infection and the life expectancy at birth. A new formula for the basic reproduction number is derived which involves the average force of infection, the average case fatality and the life expectancy at the time of infection. One can use this estimate to assess the gain in life expectancy if only a fraction of the population is immunized.

Palaearctic Culicoides midges (Diptera: Ceratopogonidae) represent a vital link in the northward advance of certain arboviral pathogens of livestock such as that caused by bluetongue virus. The effects of relevant ecological factors on weekly Culicoides vector abundances during the bluetongue virus serotype 8 epidemics in the Netherlands in 2007 and 2008 were quantified within a hurdle modelling framework. The relative role of meteorological parameters showed a broadly consistent association across species, with larger catches linked to temperature-related variables and lower wind speed. Moreover, vector abundance was found to be influenced by edaphic factors, likely related to species-specific breeding habitat preferences that differed markedly amongst some species. This is the first study on Culicoides vector species in the Netherlands identified during an entomological surveillance programme, in which an attempt is made to pinpoint the factors that influence midge abundance levels. In addition to providing key inputs into risk-mitigating tools for midge-borne pathogens and disease transmission models, the adoption of methods that explicitly address certain features of abundance datasets (frequent zero-count observations and over-dispersion) helped enhance the robustness of the ecological analysis.

Background Increases in human population size, dengue vector-density and human mobility cause rapid spread of dengue virus in Indonesia. We investigated the changes in dengue haemorrhagic fever (DHF) incidence in Indonesia over a 45-year period and determined age-specific trends in annual DHF incidence. Methods Using an on-going nationwide dengue surveillance program starting in 1968, we evaluated all DHF cases and related deaths longitudinally up to 2013. Population demographics were used to calculate annual incidence and case fatality ratios (CFRs). Age-specific data on DHF available from 1993 onwards were used to assess trends in DHF age-distribution. Time-dependency of DHF incidence and CFRs was assessed using the Cochrane-Armitage trend test. Results The annual DHF incidence increased from 0.05/100,000 in 1968 to ~ 35-40/100,000 in 2013, with superimposed epidemics demonstrating a similar increasing trend with the highest epidemic occurring in 2010 (85.70/100,000; p

We address the interaction of ecological processes, such as consumer-resource relationships and competition, and the epidemiology of infectious diseases spreading in ecosystems. Modelling such interactions seems essential to understand the dynamics of infectious agents in communities consisting of interacting host and non-host species. We show how the usual epidemiological next-generation matrix approach to characterize invasion into multi-host communities can be extended to calculate R0 , and how this relates to the ecological community matrix. We then present two simple examples to illustrate this approach. The first of these is a model of the rinderpest, wildebeest, grass interaction, where our inferred dynamics qualitatively matches the observed phenomena that occurred after the eradication of rinderpest from the Serengeti ecosystem in the 1980s. The second example is a prey-predator system, where both species are hosts of the same pathogen. It is shown that regions for the parameter values exist where the two host species are only able to coexist when the pathogen is present to mediate the ecological interaction.

A new quantity called the target reproduction number is defined to measure control strategies for infectious diseases with multiple host types such as waterborne, vector-borne and zoonotic diseases. The target reproduction number includes as a special case and extends the type reproduction number to allow disease control targeting contacts between types. Relationships among the basic, type and target reproduction numbers are established. Examples of infectious disease models from the literature are given to illustrate the use of the target reproduction number.

The control of chronic bacterial diseases with high prevalence in areas of endemicity would strongly benefit from availability of postexposure vaccines. The development of these vaccines against mycobacterial infections, such as (para)tuberculosis, is hampered by lack of experience in natural hosts. Paratuberculosis in cattle is both a mycobacterial disease of worldwide importance and a natural host model for mycobacterial infections in general. The present study showed beneficial effects of therapeutic heat shock protein 70 (Hsp70) vaccination in cattle with naturally acquired chronic infection with Mycobacterium avium subsp. paratuberculosis. Vaccination-induced protection was associated with antibody responses, rather than with induction of specific T helper 1 cells. Targeted therapeutic postexposure vaccination complementary to selective use of antibiotics could be an effective approach for control of chronic mycobacterial infections.

Speculation on how the bacterium Yersinia pestis re-emerges after years of absence in the Prebalkhash region in Kazakhstan has been ongoing for half a century, but the mechanism is still unclear. One of the theories is that plague persists in its reservoir host (the great gerbil) in so-called hotspots, i.e. small regions in which the conditions remain favourable for plague to persist during times where the conditions in the Prebalkhash region as a whole have become unfavourable for plague persistence. In this paper we use a metapopulation model that describes the dynamics of the great gerbil. With this model we study the minimum size of an individual hotspot and the combined size of multiple hotspots in the Prebalkhash region that would be required for Y. pestis to persist through an inter-epizootic period. We show that the combined area of hotspots required for plague persistence is so large that it would be unlikely to have been missed by existing plague surveillance. This suggests that persistence of plague in that region cannot solely be explained by the existence of hotspots, and therefore other hypotheses, such as survival in multiple host species, and persistence in fleas or in the soil should be considered as well.

Plague (caused by the bacterium Yersinia pestis ) is a zoonotic reemerging infectious disease with reservoirs in rodent populations worldwide. Using one-half of a century of unique data (1949–1995) from Kazakhstan on plague dynamics, including data on the main rodent host reservoir (great gerbil), main vector (flea), human cases, and external (climate) conditions, we analyze the full ecoepidemiological (bubonic) plague system. We show that two epidemiological threshold quantities play key roles: one threshold relating to the dynamics in the host reservoir, and the second threshold relating to the spillover of the plague bacteria into the human population.

INTRODUCTION: The presence of Livestock Associated MRSA (LA-MRSA) in humans is associated with intensity of animal contact. It is unknown whether the presence of LA-MRSA is a result of carriage or retention of MRSA-contaminated dust. We conducted a longitudinal study among 155 veal farmers in which repeated nasal and throat swabs were taken for MRSA detection. Periods with and without animal exposure were covered. METHODS: Randomly, 51 veal calf farms were visited from June-December 2008. Participants were asked to fill in questionnaires (n = 155) to identify potential risk factors for MRSA colonisation. Nasal and throat swabs were repeatedly taken from each participant for approximately 2 months. Swabs were analysed for MRSA and MSSA by selective bacteriological culturing. Spa-types of the isolates were identified and a ST398 specific PCR was performed. Data were analyzed using generalized estimation equations (GEE) to allow for correlated observations within individuals. RESULTS: Mean MRSA prevalence was 38% in farmers and 16% in family members. Presence of MRSA in farmers was strongly related to duration of animal contact and was strongly reduced in periods with absence of animal contact (-58%). Family members, especially children, were more often carriers when the farmer was a carrier (OR = 2, P

Wild waterfowl populations form a natural reservoir of Avian Influenza (AI) virus, and fears exist that these birds may contribute to an AI pandemic by spreading the virus along their migratory flyways. Observational studies suggest that individuals infected with AI virus may delay departure from migratory staging sites. Here, we explore the epidemiological dynamics of avian influenza virus in a migrating mallard (Anas platyrhynchos) population with a specific view to understanding the role of infection-induced migration delays on the spread of virus strains of differing transmissibility. We develop a host-pathogen model that combines the transmission dynamics of influenza with the migration, reproduction and mortality of the host bird species. Our modeling predicts that delayed migration of individuals influences both the timing and size of outbreaks of AI virus. We find that (1) delayed migration leads to a lower total number of cases of infection each year than in the absence of migration delay, (2) when the transmission rate of a strain is high, the outbreak starts at the staging sites at which birds arrive in the early part of the fall migration, (3) when the transmission rate is low, infection predominantly occurs later in the season, which is further delayed when there is a migration delay. As such, the rise of more virulent AI strains in waterfowl could lead to a higher prevalence of infection later in the year, which could change the exposure risk for farmed poultry. A sensitivity analysis shows the importance of generation time and loss of immunity for the effect of migration delays. Thus, we demonstrate, in contrast to many current transmission risk models solely using empirical information on bird movements to assess the potential for transmission, that a consideration of infection-induced delays is critical to understanding the dynamics of AI infection along the entire flyway.

In view of the remarkable decrease of the relative heart weight (HW) and the relative blood volume in growing pigs, we investigated whether HW, cardiac output (CO), and stroke volume (SV) of modern growing pigs are proportional to BW, as predicted by allometric scaling laws: HW (or CO or SV) = a∙BWb, in which a and b are constants, and constant b is a multiple of 0.25 (quarter-power scaling law). Specifically, we tested the hypothesis that both HW and CO scale with BW to the power of 0.75 (HW or CO = a∙BW0.75) and SV scales with BW to the power of 1.00 (SV = a∙BW1.0). For this purpose, 2 groups of pigs (group 1, consisting of 157 pigs of 50 ± 1 kg; group 2, consisting of 45 pigs of 268 ± 18 kg) were surgically instrumented with a flow probe or a thermodilution dilution catheter, under open-chest anesthetized conditions to measure CO and SV, after which HW was determined. The 95% confidence intervals of power-coefficient b for HW were 0.74 to 0.80, encompassing the predicted value of 0.75, suggesting that HW increased proportionally with BW, as predicted by the allometric scaling laws. In contrast, the 95% confidence intervals of power-coefficient b for CO and SV as measured with flow probes were 0.40 to 0.56 and 0.39 to 0.61, respectively, and values obtained with the thermodilution technique were 0.34 to 0.53 and 0.40 to 0.62, respectively. Thus, the 95% confidence limits failed to encompass the predicted values of b for CO and SV of 0.75 and 1.0, respectively. In conclusion, although adult breeding sows display normal heart growth, cardiac performance appears to be disproportionately low for BW. This raises concern regarding the health status of adult breeding sows.

BACKGROUND: Zoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The disease's causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus. METHODS: Sandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km). Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R(0) map for leishmaniasis was constructed within a Geographical Information System. R(0) is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission. CONCLUSIONS: To our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R(0) model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

One of the important questions in understanding infectious diseases and their prevention and control is how infectious agents can invade and become endemic in a host population. A ubiquitous feature of natural populations is that they are spatially fragmented, resulting in relatively homogeneous local populations inhabiting patches connected by the migration of hosts. Such fragmented population structures are studied extensively with metapopulation models. Being able to define and calculate an indicator for the success of invasion and persistence of an infectious agent is essential for obtaining general qualitative insights into infection dynamics, for the comparison of prevention and control scenarios, and for quantitative insights into specific systems. For homogeneous populations, the basic reproduction ratio R(0) plays this role. For metapopulations, defining such an 'invasion indicator' is not straightforward. Some indicators have been defined for specific situations, e.g., the household reproduction number R*. However, these existing indicators often fail to account for host demography and especially host migration. Here we show how to calculate a more broadly applicable indicator R(m) for the invasion and persistence of infectious agents in a host metapopulation of equally connected patches, for a wide range of possible epidemiological models. A strong feature of our method is that it explicitly accounts for host demography and host migration. Using a simple compartmental system as an example, we illustrate how R(m) can be calculated and expressed in terms of the key determinants of epidemiological dynamics.

Our objective was to use data mining to develop and validate a detection model for clinical mastitis (CM) using sensor data collected at nine Dutch dairy herds milking automatically. Sensor data was available for almost 3.5 million quarter milkings (QM) from 1,109 cows; 348 QM with CM were observed by the participating farmers. Data was divided into a training and a test set, stratified at the cow level. For model building, QM with CM (n = 243) from the training set were taken together with 24,987 QM with a somatic cell count less than 200,000 cells/ml on a milk production test day from cows that never exceeded this threshold during all test days within parity and that were never visually checked by the farmers for CM. The model used decision tree (DT) induction as base classifier, with and without bagging and boosting techniques. Both bagging and boosting techniques work by building models using the base classifier on various samples of the training data. For validation two test sets were created. The first included 105 QM with CM and 13,313 QM without CM, using the same selection as for the training. This test set (Test_GreyOut) excluded the large pool of QM that have a less clear mastitis status. The second test set included the same 105 QM with CM but this dataset included QM with a less clear mastitis status (Test_GreyIn): for negative examples, those QM that were not scored as having CM by the farmers were labeled as negative for CM. From this large sample (n = 1,146,544), a random sample of 50,000 QM was selected. Sensitivity levels were computed at fixed SP levels, and the transformed partial area under the curve (pAUC) was calculated for specificity values of 97% or more to evaluate performance. To visualize performance of the detection models for specificity values of 97% or more, receiver operating characteristic (ROC) curves were constructed. When using the Test_GreyOut set, the transformed pAUC increased from 0.713 when using the base classifier alone, to 0.787 when combined with boosting, to 0.800 when combined with bagging. At a specificity of 99%, sensitivity was 43.5% for the base classifier, 60.0% when combined with boosting and 61% when combined with bagging. When testing on the TestInGrey set, pAUC values were lower, but still increased when bagging and boosting techniques were used: values increased from 0.643 when using the base classifier alone, to 0.677 when combined with boosting, to 0.702 when combined with bagging. At a specificity of 99%, sensitivity was 24.7% for the base classifier, 30.5% when combined with boosting and 35.2% when combined with bagging. These results were obtained using very narrow time-windows. It is therefore concluded that models developed by DT induction are promising for future implementation

BACKGROUND: To support the development of early warning and surveillance systems of emerging zoonoses, we present a general method to prioritize pathogens using a quantitative, stochastic multi-criteria model, parameterized for the Netherlands. METHODOLOGY/PRINCIPAL FINDINGS: A risk score was based on seven criteria, reflecting assessments of the epidemiology and impact of these pathogens on society. Criteria were weighed, based on the preferences of a panel of judges with a background in infectious disease control. CONCLUSIONS/SIGNIFICANCE: Pathogens with the highest risk for the Netherlands included pathogens in the livestock reservoir with a high actual human disease burden (e.g. Campylobacter spp., Toxoplasma gondii, Coxiella burnetii) or a low current but higher historic burden (e.g. Mycobacterium bovis), rare zoonotic pathogens in domestic animals with severe disease manifestations in humans (e.g. BSE prion, Capnocytophaga canimorsus) as well as arthropod-borne and wildlife associated pathogens which may pose a severe risk in future (e.g. Japanese encephalitis virus and West-Nile virus). These agents are key targets for development of early warning and surveillance.

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n = 24,717) or diseased (n = 243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24 h before the CM observation, to a time window from 24 h before to 24 h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well.

Heterogeneity in exposure to Eimeria spp. of chickens in a flock will result in differences between individual birds in oocyst output and acquired immunity, which subsequently affects transmission of the parasite in the population. The aim of this study was to quantify effects of previous infection of broilers with Eimeria acervulina on immune responses, oocyst output and transmission. A transmission experiment was carried out with pair-wise housed broilers, that differed in infection history. This "infection history" was achieved by establishment of a primary infection by inoculation of birds with 50,000 sporulated E. acervulina oocysts at day 6 of age ("primed"); the other birds did not receive a primary infection ("naive"). The actual transmission experiment started at day 24 of age: one bird (I) was inoculated with 50,000 sporulated oocysts and was housed together with a non-inoculated contact bird (C). Oocyst excretion and parameters describing transmission, i.e. the number of infected C birds and time passed before start of excretion of C birds, were determined from day 28 to day 50 for six pairs of four different combinations of I and C birds (I-C): naive-naive, naive-primed, primed-naive and primed-primed. Immune parameters, CD4(+), CD8(+), αβTCR(+) and γδTCR(+) T cells and macrophages in duodenum, were determined in an additional 25 non-primed, non-inoculated control birds, and in the naive-naive and naive-primed groups, each group consisting of 25 pairs. Although the numbers of CD4(+) T cells and γδTCR(+) T cells increased after primary infection, none of the immunological cell types provided an indication of differences in infectivity, susceptibility or transmission between birds. Oocyst output was significantly reduced in primed I and C birds. Transmission was reduced most in the primed-primed group, but nonetheless transmission occurred in all groups. This study also showed that acquired immunity significantly reduced oocyst output after inoculation and contact-infection, but not sufficiently to prevent transmission to contact-exposed birds.

Geographical maps indicating the value of the basic reproduction number, R 0 , can be used to identify areas of higher risk for an outbreak after an introduction. We develop a methodology to create R 0 maps for vector-borne diseases, using bluetongue virus as a case study. This method provides a tool for gauging the extent of environmental effects on disease emergence. The method involves integrating vector-abundance data with statistical approaches to predict abundance from satellite imagery and with the biologically mechanistic modelling that underlies R 0 . We illustrate the method with three applications for bluetongue virus in the Netherlands: 1) a simple R 0 map for the situation in September 2006, 2) species-specific R 0 maps based on satellite-data derived predictions, and 3) monthly R 0 maps throughout the year. These applications ought to be considered as a proof-of-principle and illustrations of the methods described, rather than as ready-to-use risk maps. Altogether, this is a first step towards an integrative method to predict risk of establishment of diseases based on mathematical modelling combined with a geographic information system that may comprise climatic variables, landscape features, land use, and other relevant factors determining the risk of establishment for bluetongue as well as of other emerging vector-borne diseases.