Methicillin-resistant staphylococcus aureus nosocomial infection has a distinct epidemiological position and acts as a marker for overall hospital-acquired infection trends

An ongoing healthcare debate is whether controlling hospital-acquired infection (HAI) from methicillin-resistant Staphylococcus aureus (MRSA) will result in lowering the global HAI rate, or if MRSA will simply be replaced by another pathogen and there will be no change in overall disease burden. With surges in drug-resistant hospital-acquired pathogens during the COVID-19 pandemic, this remains an important issue. Using a dataset of more than 1 million patients in 51 acute care facilities across the USA, and with the aid of a threshold model that models the nonlinearity in outbreaks of diseases, we show that MRSA is additive to the total burden of HAI, with a distinct 'epidemiological position', and does not simply replace other microbes causing HAI. Critically, as MRSA is reduced it is not replaced by another pathogen(s) but rather lowers the overall HAI burden. The analysis also shows that control of MRSA is a benchmark for how well all non-S. aureus nosocomial infections in the same hospital are prevented. Our results are highly relevant to healthcare epidemiologists and policy makers when assessing the impact of MRSA on hospitalized patients. These findings further stress the major importance of MRSA as a unique cause of nosocomial infections, as well as its pivotal role as a biomarker in demonstrating the measured efficacy (or lack thereof) of an organization's Infection Control program

Modelling the dynamic relationship between spread of infection and observed crowd movement patterns at large scale events.

Understanding how contact patterns arise from crowd movement is crucial for assessing the spread of infection at mass gathering events. Here we study contact patterns from Wi-Fi mobility data of large sports and entertainment events in the Johan Cruijff ArenA stadium in Amsterdam. We show that crowd movement behaviour at mass gathering events is not homogeneous in time, but naturally consists of alternating periods of movement and rest. As a result, contact duration distributions are heavy-tailed, an observation which is not explained by models assuming that pedestrian contacts are analogous to collisions in the kinetic gas model. We investigate the effect of heavy-tailed contact duration patterns on the spread of infection using various random walk models. We show how different types of intermittent movement behaviour interact with a time-dependent infection probability. Our results point to the existence of a crossover point where increased contact duration presents a higher level of transmission risk than increasing the number of contacts. In addition, we show that different types of intermittent movement behaviour give rise to different mass-action kinetics, but also show that neither one of two mass-action mechanisms uniquely describes events

The role of inter-regional mobility in forecasting SARS-CoV-2 transmission

In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants

Defining Fatty Acid Changes Linked to Rumen Development, Weaning and Growth in Holstein-Friesian Heifers

After birth, as effectively monogastric animals, calves undergo substantial physiological changes to become ruminants by 3 months of age and reach sexual maturity at approximately 15 months of age. Herein, we assess longitudinal metabolomic changes in Holstein-Friesian (HF) heifers from birth until sexual maturity during this developmental process. Sera from 20 healthy, HF heifers were sampled biweekly from 2 weeks of age until 13 months of age and then monthly until 19 months of age. Sera were assessed using flow infusion electrospray high-resolution mass spectrometry (FIE-HRMS) on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer for high-throughput, sensitive, non-targeted metabolite fingerprinting. Partial least squares discriminant analysis (PLS-DA) and unsupervised hierarchical clustering analysis (HCA) of the derived metabolomes indicated changes detectable in heifers’ sera over time. Time series analyses identified 30 metabolites that could be related to rumen development and weaning at ~3 months of age. Further time series analysis identified 40 metabolites that could be correlated with growth. These findings highlight the role of acetic acid and 3-phenylpropionate (3-PP) in rumen development and growth, suggest that weaning induces elevated levels of fatty acyls in response to a post-weaning stress-induced innate immune response and demonstrate the utilization of fatty acyls in growth. The identified metabolites offer serum metabolites which could inform the nutrition and healthy development of heifers

Metabolomic Changes in Naturally MAP-Infected Holstein–Friesian Heifers Indicate Immunologically Related Biochemical Reprogramming

Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), causes weight loss, diarrhoea, and reduced milk yields in clinically infected cattle. Asymptomatic, subclinically infected cattle shed MAP bacteria but are frequently not detected by diagnostic tests. Herein, we compare the metabolite profiles of sera from subclinically infected Holstein-Friesian heifers and antibody binding to selected MAP antigens. The study used biobanked serum samples from 10 naturally MAP-infected and 10 control heifers, sampled monthly from ~1 to 19 months of age. Sera were assessed using flow infusion electrospray-high-resolution mass spectrometry (FIE-HRMS) on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer for high-throughput, sensitive, non-targeted metabolite fingerprinting. Partial least-squares discriminant analyses (PLS-DA) and hierarchical cluster analysis (HCA) of the data discriminated between naturally MAP-infected and control heifers. In total, 33 metabolites that differentially accumulated in naturally MAP-infected heifers compared to controls were identified. Five were significantly elevated within MAP-infected heifers throughout the study, i.e., leukotriene B4, bicyclo prostaglandin E2 (bicyclo PGE2), itaconic acid, 2-hydroxyglutaric acid and N6-acetyl-L-lysine. These findings highlight the potential of metabolomics in the identification of novel MAP diagnostic markers and particular biochemical pathways, which may provide insights into the bovine immune response to MAP

Glucocorticoids in relation to behavior, morphology, and physiology as proxy indicators for the assessment of animal welfare. A systematic mapping review

Translating theoretical concepts of animal welfare into quantitative assessment protocols is an ongoing challenge. Glucocorticoids (GCs) are frequently used as physiological measure in welfare assessment. The interpretation of levels of GCs and especially their relation to welfare, however, is not as straightforward, questioning the informative power of GCs. The aim of this systematic mapping review was therefore to provide an overview of the relevant literature to identify global patterns in studies using GCs as proxy for the assessment of welfare of vertebrate species. Following a systematic protocol and a-priory inclusion criteria, 509 studies with 517 experiments were selected for data extraction. The outcome of the experiments was categorized based on whether the intervention significantly affected levels of GCs, and whether these effects were accompanied by changes in behavior, morphology and physiology. Additional information, such as animal species, type of intervention, experimental set up and sample type used for GC determination was extracted, as well. Given the broad scope and large variation in included experiments, meta-analyses were not performed, but outcomes are presented to encourage further, in-depth analyses of the data set. The interventions did not consistently lead to changes in GCs with respect to the original authors hypothesis. Changes in GCs were not consistently paralleled by changes in additional assessment parameter on behavior, morphology and physiology. The minority of experiment quantified GCs in less invasive sample matrices compared to blood. Interventions showed a large variability, and species such as fish were underrepresented, especially in the assessment of behavior. The inconclusive effects on GCs and additional assessment parameter urges for further validation of techniques and welfare proxies. Several conceptual and technical challenges need to be met to create standardized and robust welfare assessment protocols and to determine the role of GCs herein

On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

The expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population is mathematically defined as the dominant eigenvalue of a positive linear operator. It is shown that in certain special cases one can easily compute or estimate this eigenvalue. Several examples involving various structuring variables like age, sexual disposition and activity are presented

Modelling the reproductive power function

This paper discusses methods of estimating the reproductive power and the accompanying survival function of communicable events, e.g. infectious disease transmission. The early stage of an outbreak can be described by the infectiousness of the outbreak process, but in later stages of the outbreak, this is complicated by factors such as changing contact patterns and the impact of control measures. It is important to take these factors into account in order to get a good, if approximate, model for an outbreak process. This paper proposes a non-homogeneous birth process and regression model for the reproductive power function, similar to models in discrete survival analysis. A baseline reproductive power function gives a description of the outbreak when covariates are at their baseline values. As an illustration these methods are applied to an avian influenza (H5N1) outbreak among poultry in Thailand

Ecological impact of changes in intrinsic growth rates of species at different trophic levels

Decreased and increased intrinsic growth rate and abundance of a single species can severely and negatively impact other species in the same food web. Here we compare the wider system effects of decreased and increased intrinsic growth rates of species occupying different trophic levels. Specifically, we derive the change in growth rate of a single (focal) species necessary to cause a 90% reduction in the abundance – a quasi-extinction – of another species in model communities. We find that even relatively small changes, negative as well as positive, in the growth rate of the focal species can cause quasi-extinctions of others. Furthermore, the magnitude of change needed to cause a quasi-extinction depends on the trophic level of the perturbed species. The potential ecosystem impact of such ‘negative' and ‘positive' changes is largely unknown. We argue that such a targeted decrease or increase could be induced by human interference, such as hunting or harvesting, but also by an outbreak or fade-out of an infectious disease. As ecosystems maintain many and diverse infectious agents, these results suggest that these agents may play an important role in the structure and balance of ecosystems

Modelling the spatial distribution of the nuisance mosquito species Anopheles plumbeus (Diptera Culicidae) in the Netherlands

BACKGROUND: Landscape modifications, urbanization or changes of use of rural-agricultural areas can create more favourable conditions for certain mosquito species and therefore indirectly cause nuisance problems for humans. This could potentially result in mosquito-borne disease outbreaks when the nuisance is caused by mosquito species that can transmit pathogens. Anopheles plumbeus is a nuisance mosquito species and a potential malaria vector. It is one of the most frequently observed species in the Netherlands. Information on the distribution of this species is essential for risk assessments. The purpose of the study was to investigate the potential spatial distribution of An. plumbeus in the Netherlands. METHODS: Random forest models were used to link the occurrence and the abundance of An. plumbeus with environmental features and to produce distribution maps in the Netherlands. Mosquito data were collected using a cross-sectional study design in the Netherlands, from April to October 2010-2013. The environmental data were obtained from satellite imagery and weather stations. Statistical measures (accuracy for the occurrence model and mean squared error for the abundance model) were used to evaluate the models performance. The models were externally validated. RESULTS: The maps show that forested areas (centre of the Netherlands) and the east of the country were predicted as suitable for An. plumbeus. In particular high suitability and high abundance was predicted in the south-eastern provinces Limburg and North Brabant. Elevation, precipitation, day and night temperature and vegetation indices were important predictors for calculating the probability of occurrence for An. plumbeus. The probability of occurrence, vegetation indices and precipitation were important for predicting its abundance. The AUC value was 0.73 and the error in the validation was 0.29; the mean squared error value was 0.12. CONCLUSIONS: The areas identified by the model as suitable and with high abundance of An. plumbeus, are consistent with the areas from which nuisance was reported. Our results can be helpful in the assessment of vector-borne disease risk