Stochastic Simulation Model Research Articles

On the Stochastic Modeling of COVID-19 under the Environmental White Noise

The COVID-19 pandemic has caused emotional loss to people around the world and provides an unusual test for public welfare, educational framework, food frameworks, and the world of work. The economic and social turmoil caused by this epidemic is increasing, and many people are at risk of falling into oppressive poverty. In this article, we describe the pandemic of infectious illness with the help of stochastic mathematical modeling. Based on the environmental white noise and by building appropriate Lyapunov functions and by applying Ito’s formula, a few subjective properties are gotten. We provide a new mathematical model for the COVID-19 spread. The novel stochastic model is used to analyze the existence and prevalence of the disease, as well as its extinction. A numerical approach is developed for computing approximate solutions of the model. We show numerical simulations of deterministic and stochastic models of COVID-19 by utilizing the MATLAB software. In this direction, three graphs are included in the paper for the numerical interpretation of the stochastic model with the help of existing parametric and initial values for the model.

SIMULATION MODELING OF INVESTMENT PROJECTS IN THE SERVICE SECTOR

Scientific article is devoted to researching the possibility of developing a simulation model of an investment project for a travel agency. In recent years, the tourism industry in the world is experiencing rapid development. The development of the tourism industry requires new investments. The investor must assess the degree of risks, the likelihood of profit or loss. It is noted that the most important advantage of simulation modeling is that it makes it possible to study economic systems at the design stage. Due to this, simulation models can be used as a universal tool in making appropriate decisions under conditions of uncertainty and taking into account those factors that are difficult to predict and take into account, which is why simulation is so often used in the development of investment projects. When assessing the risks of investment projects, the collection of information requires significant financial expenses, is quite time-consuming, and sometimes impossible. The subject of the study is a set of the theoretical, methodological and organizational problems related to the management decisions regarding the investment of the projects in the field of tourism. The methodology is based on the stochastic simulation modeling of economic processes. The aim of the study is to develop a simulation model for an investment project in the tourism sector, which will allow the investor to appreciate the degree of the risk and the likelihood of return on the investment. The article reveals the features of computer simulation in the MathCAD system. It is proved that it is convenient to develop simulation models of investment projects in the MathCAD system. It has a powerful mathematical support, remains one of the systems in which the description of the solution of mathematical problems is given with the use of conventional mathematical formulas and symbols and does not require special training in programming. The simulation model of the investment project of the travel agency was developed in the MathCAD system. Statistical processing of the results of the experiments with the model has been carried out. The necessity of the detailed study of critical intervals of the histogram at the transition from loss-making to profitable NPV values has been substantiated. Based on the results of simulation experiments with the model, the errors of the model were estimated, the law of NPV random variables distribution was established. The developed simulation model allows the investor to estimate the risk factor, the probability of profit or loss from the investment, to assess the possible uncertainty of the results of their own decision to invest in the project. Conclusion: the simulation model of the investment project built and investigated in MathCAD system allows investor to estimate the risk factor, probability of profit or loss for choosing the optimal pricing policy and optimization of economic strategy of tourist agency.

Stochastic Simulation of Typhoon in Northwest Pacific Basin Based on Machine Learning.

Typhoons have caused serious economic losses and casualties in coastal areas all over the world. The big size of the tropical cyclone sample by stochastic simulation can effectively evaluate the typhoon hazard risk, and the typhoon full-track model is the most popular model for typhoon stochastic simulation. Based on the advantages of machine learning in dealing with nonlinear problems, this study uses a backpropagation neural network (BPNN) to replace the regression model in the empirical track model, reestablishes the neural network model for track and intensity prediction in typhoon stochastic simulation, and constructs full‐track typhoon events of 1000 years for Northwest Pacific basin. The validation results indicate that the BPNN can improve the accuracy of typhoon track and intensity prediction.

Economics of timed artificial insemination with unsorted or sexed semen in a high-producing, pasture-based dairy production system

This study used a stochastic simulation model to estimate the potential economic benefit of using timed artificial insemination (TAI) in combination with conventional unsorted (TCONV) and sexed (TSEX) semen in heifers only (TCONV-H, TSEX-H) and in both heifers and lactating cows (TCONV-HC, TSEX-HC) in a high-producing, pasture-based production system. The scenarios were compared with a conventional reproductive policy (CONV) in which heifers and cows were inseminated with conventional unsorted semen after estrus detection. Sensitivity analysis was also used to estimate the effect of hormone costs from TAI use on the profitability of each program relative to CONV. The mean annual (± standard deviation) profit advantage (ΔPROF) over CONV for TCONV-H, TCONV-HC, TSEX-H, and TSEX-HC scenarios were €3.90/cow ± 4.65, €34.11/cow ± 25.69, €13.96/cow ± 6.83, and €41.52/cow ± 42.86, respectively. Combined application of both technologies was shown to return a greater annual ΔPROF on average compared with that achievable from TAI alone. However, the risk of not returning a positive annual ΔPROF varied across the scenarios with higher risk in TCONV-H and TSEX-HC. Specifically, TCONV-H and TSEX-HC had a 24 and 18% chance, respectively, of not returning a positive annual ΔPROF. Sensitivity analysis showed that when hormone costs increased by €10/cow TCONV-H and TSEX-HC had a 38 and 23% chance, respectively, of not returning a positive annual ΔPROF. The range in ΔPROF for TCONV policies was most sensitive to the TAI pregnancy rate and TSEX policies were most sensitive to the relative fertility achieved with sexed compared with unsorted semen. This study has shown TAI and sexed semen are complementary technologies that can increase genetic gain and profitability in a pasture-based, dairy production system.

The Disconnect Between Short- and Long-Term Population Projections for Plant Reintroductions

The reintroduction of rare species in natural preserves is a commonly used restoration strategy to prevent species extinction. An essential first step in planning successful reintroductions is identifying which life stages (e.g., seeds or large adults) should be used to establish these new populations. Following this initial establishment phase, it is necessary to determine the level of survival, growth, and recruitment needed to maintain population persistence over time and identify management actions that will achieve these goals. In this 5-year study, we projected the short- and long-term population growth rates of a critically endangered long-lived shrub, Delissea waianaeensis. Using this model system, we show that reintroductions established with mature individuals have the lowest probability of quasi-population extinction (10 individuals) and the highest increase in population abundance. However, our results also demonstrate that short-term increases in population abundances are overly optimistic of long-term outcomes. Using long-term stochastic model simulations, we identified the level of natural seedling regeneration needed to maintain a positive population growth rate over time. These findings are relevant for planning future reintroduction efforts for long-lived species and illustrate the need to forecast short- and long-term population responses when evaluating restoration success.

Modelling the epidemiology of malaria and spread of HRP2-negative Plasmodium falciparum following the replacement of HRP2-detecting rapid diagnostic tests.

Malaria rapid diagnostic tests (RDTs) are dominated by products which use histidine-rich protein 2 (HRP2) to detect Plasmodium falciparum. The emergence of parasites lacking the pfhrp2 gene can lead to high rates of false-negative results amongst these RDTs. One solution to restore the ability to correctly diagnose falciparum malaria is to switch to an RDT which is not solely reliant on HRP2. This study used an agent-based stochastic simulation model to investigate the impact on prevalence and transmission caused by switching the type of RDT used once false-negative rates reached pre-defined thresholds within the treatment-seeking symptomatic population. The results show that low transmission settings were the first to reach the false-negative switch threshold, and that lower thresholds were typically associated with better long-term outcomes. Changing the diagnostic RDT away from a HRP2-only RDT is predicted to restore the ability to correctly diagnose symptomatic malaria infections, but often did not lead to the extinction of HRP2-negative parasites from the population which continued to circulate in low density infections, or return to the parasite prevalence and transmission levels seen prior to the introduction of the HRP2-negative parasite. In contrast, failure to move away from HRP2-only RDTs leads to near fixation of these parasites in the population, and the inability to correctly diagnose symptomatic cases. Overall, these results suggest pfhrp2-deleted parasites are likely to become a significant component of P. falciparum parasite populations, and that long-term strategies are needed for diagnosis and surveillance which do not rely solely on HRP2.

GESTÃO DA EXTRAÇÃO FLORESTAL COM O INDICADOR EFICIÊNCIA GLOBAL DE MÁQUINAS FLORESTAIS (EGMF)

ABSTRACT Performance indicators are tools capable of exposing measurable characteristics and generating relevant information on forest operations, thus being considered pillars for managers to make agile and assertive decisions. Forest extraction with a forwarder must be improved, understanding the factors that affect the costs of this machine, such as productivity (PR), fuel consumption (FC), operational efficiency (OE), and quality of operation. Thus, the objective of this study was to evaluate the implementation of the Overall Efficiency of Forest Machines (OEFM) indicator in the management of forest extraction data using forwarders. Data were collected during forest harvesting from five operating fleets, in commercial eucalypt plantations, in full-tree and coppice regimes, in the states of Bahia and Espírito Santo. The indicator was expressed as a percentage calculated by OEFM = ( ( 4 ∗ PR ) + ( 3 ∗ FC ) + ( 3 ∗ OE ) ) / 10. The performance of the machines was evaluated by a stochastic model of dynamic simulation of systems in eight scenarios, proposing improvement for the average individual volume harvested, fuel consumption, and mechanical or operational stops. Analyzes were performed using PowerSim Studio 9 software. The OEFM of two fleets was higher than the established target of 95.17%, with 95.72% and 97.44%. The OEFM indicator proved to be useful in the management of forest extraction with adequate and easy-to-understand information from a large amount and variety of data. The stochastic simulation model was efficient to study the impact on the global efficiency and the flow of wood extraction by the forwarder.

A stochastic process based modular tool-box for simulating COVID-19 infection spread

BackgroundThe novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account. MethodsThe program was written in R language. A stochastic point process simulation model for simulating epidemics, a maximum-likelihood estimation model, an exponential growth rate model for calculating the basic reproduction number (R0), and functions for generating graphical representations of the simulations were utilized.Geographical area definition, population size, the number of initial infected individuals, period of simulation, parameters accounting for the radius of spread like masks usage, mobility level, intrinsic viral virulence, average infectious period, fraction of population vaccinated, time of vaccination, the efficacy of the vaccine, presence or absence of quarantine centers, time of establishment of quarantine centers, the efficacy of case detection and average time to quarantine from the detection of the infection were considered. ResultsWhen the defined parameters were input, the model performed successfully producing the epidemic curve, R0 and an animation of infection spread. It was found that when parameters of known epidemics such as COVID-19 in California, Texas and, Florida were input, the epidemic curve generated was comparable to the epidemic curve in reality. ConclusionThis model can be utilized by many countries to visualize the effects of various mitigation strategies applied in their stage of disease and for policy makers to make informed decisions. It is applicable to many infectious diseases and hence can be used for research and educational purposes.

Validation of a multi-physics simulation platform for engine emissions modelling

This paper introduces a comprehensive and systematic Design of Experiments based methodology deployed in conjunction with a multi-physics engine air-path and combustion co-simulation, leading to the development of a global transient simulation capability for engine out NOx emissions. The proposed multi-physics engine simulation framework couples a real-time one-dimensional air flow model with a Probability Density Function based Stochastic Reactor Model that accounts for detailed in-cylinder combustion chemistry to predict combustion emissions. The integration challenge stemming from the different computation complexities and time scales required to ensure adequate fidelity levels across multi-physics simulations was addressed through a comprehensive Design of Experiments methodology to develop a reduction of the slower Stochastic Reactor Model simulation to enable a transient simulation focussed on NOx emissions. The Design of Experiments methodology, based on Optimal Latin Hypercube design experiments, was deployed on the multi-physics engine co-simulation platform and systematically validated against both steady state and transient light-duty Diesel engine test data. The surrogate selection process included the evaluation of a range of metamodels, with Kriging metamodels selected based on both the statistical performance criteria and consideration of physical phenomena trends. The transient validation was carried out on a simulated New European Drive Cycle against the experimental data available, showing good capability to capture transient NOx emission behaviour in terms of trends and values. The significance of the results is that it proves the transient and drive cycle capability of the multi-physics simulation platform, suggesting a promising potential applicability for early powertrain development work focussed on drive cycle emissions.

A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.

Increased B Cell Selection Stringency In Germinal Centers Can Explain Improved COVID-19 Vaccine Efficacies With Low Dose Prime or Delayed Boost.

The efficacy of COVID-19 vaccines appears to depend in complex ways on the vaccine dosage and the interval between the prime and boost doses. Unexpectedly, lower dose prime and longer prime-boost intervals have yielded higher efficacies in clinical trials. To elucidate the origins of these effects, we developed a stochastic simulation model of the germinal center (GC) reaction and predicted the antibody responses elicited by different vaccination protocols. The simulations predicted that a lower dose prime could increase the selection stringency in GCs due to reduced antigen availability, resulting in the selection of GC B cells with higher affinities for the target antigen. The boost could relax this selection stringency and allow the expansion of the higher affinity GC B cells selected, improving the overall response. With a longer dosing interval, the decay in the antigen with time following the prime could further increase the selection stringency, amplifying this effect. The effect remained in our simulations even when new GCs following the boost had to be seeded by memory B cells formed following the prime. These predictions offer a plausible explanation of the observed paradoxical effects of dosage and dosing interval on vaccine efficacy. Tuning the selection stringency in the GCs using prime-boost dosages and dosing intervals as handles may help improve vaccine efficacies.

Assessing microplastic exposure of large marine filter-feeders

Large filter-feeding animals are potential sentinels for understanding the extent of microplastic pollution, as their mode of foraging and prey mean they are continuously sampling the environment. However, there is considerable uncertainty about the total and mode of exposure (environmental vs trophic). Here, we explore microplastic exposure and ingestion by baleen whales feeding year-round in coastal Auckland waters, New Zealand. Plastic and DNA were extracted concurrently from whale scat, with 32 ± 24 (mean ± SD, n = 21) microplastics per 6 g scat sample detected. Using a novel stochastic simulation modeling incorporating new and previously published DNA diet information, we extrapolate this to total microplastic exposure levels of 24,028 (95% CI: 2119, 69,270) microplastics per mouthful of prey, or 3,408,002 microplastics (95% CI: 295,810, 10,031,370) per day, substantially higher than previous estimates for large filter-feeding animals. Critically, we find that the total exposure is four orders of magnitude more than expected from microplastic measurements of local coastal surface waters. This suggests that trophic transfer, rather than environmental exposure, is the predominant mode of exposure of large filter feeders for microplastic pollution. Measuring plastic concentration from the environment alone significantly underestimates exposure levels, an important consideration for future risk assessment studies.

Heuristics for the sustainable harvest of wildlife in stochastic social-ecological systems.

Sustainable wildlife harvest is challenging due to the complexity of uncertain social-ecological systems, and diverse stakeholder perspectives of sustainability. In these systems, semi-complex stochastic simulation models can provide heuristics that bridge the gap between highly simplified theoretical models and highly context-specific case-studies. Such heuristics allow for more nuanced recommendations in low-knowledge contexts, and an improved understanding of model sensitivity and transferability to novel contexts. We develop semi-complex Management Strategy Evaluation (MSE) models capturing dynamics and variability in ecological processes, monitoring, decision-making, and harvest implementation, under a diverse range of contexts. Results reveal the fundamental challenges of achieving sustainability in wildlife harvest. Environmental contexts were important in determining optimal harvest parameters, but overall, evaluation contexts more strongly influenced perceived outcomes, optimal harvest parameters and optimal harvest strategies. Importantly, simple composite metrics popular in the theoretical literature (e.g. focusing on maximizing yield and population persistence only) often diverged from more holistic composite metrics that include a wider range of population and harvest objectives, and better reflect the trade-offs in real world applied contexts. While adaptive harvest strategies were most frequently preferred, particularly for more complex environmental contexts (e.g. high uncertainty or variability), our simulations map out cases where these heuristics may not hold. Despite not always being the optimal solution, overall adaptive harvest strategies resulted in the least value forgone, and are likely to give the best outcomes under future climatic variability and uncertainty. This demonstrates the potential value of heuristics for guiding applied management.

Exploiting Noise, Non-Linearity, and Feedback for Differential Control of Multiple Synthetic Cells with a Single Optogenetic Input.

Synthetic biology seeks to develop modular biocircuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, a deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial conditions. We explore how controllability depends on cells' regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.

What Are the Human Resources Required to Control a Foot-and-Mouth Disease Outbreak in Austria?

Contingency planning allows veterinary authorities to prepare a rapid response in the event of a disease outbreak. A recently published foot-and-mouth disease (FMD) simulation study indicated concerns whether capacity was sufficient to control a potential FMD epidemic in Austria. The objectives of the study presented here were to estimate the human resources required to implement FMD control measures and to identify areas of the operational activities that could potentially delay successful control of the disease. The stochastic spatial simulation model EuFMDiS (The European Foot-and-Mouth Disease Spread Model) was used to simulate a potential FMD outbreak and its economic impact, including different control scenarios based on variations of culling, vaccination, and pre-emptive depopulation. In this context, the utilization of human resources was assessed based on the associated EuFMDiS output regarding the performance of operational activities. The assessments show that the number of personnel needed in an outbreak with a stamping-out policy would reach the peak at the end of the second week of control with a median of 540 (257–926) individuals, out of which 31% would be veterinarians. Approximately 58% of these human resources would be attributable to surveillance, followed by staff for cleaning and disinfection activities. Our analysis demonstrates that, of the operational activities, surveillance personnel were the largest factor influencing the magnitude of the outbreak. The aim of the assessment presented here is to assist veterinary authorities in the contingency planning of required human resources to respond effectively to an outbreak of animal diseases such as FMD.

Use of scenario tree modelling to plan freedom from infection surveillance: Mycoplasma bovis in New Zealand

Since mid-2018, the New Zealand (NZ) Ministry for Primary Industries (MPI) has been operating an eradication program for an incursion of Mycoplasma bovis. Although NZ is still delimiting the outbreak, consideration is being given to how freedom from M. bovis will be demonstrated. Rapid demonstration of freedom will minimise the length of the program, significantly reducing its financial burden. This collaborative research was undertaken to help inform planning of surveillance to demonstrate freedom after M. bovis is believed eradicated.Scenario tree modelling (STM) involves assimilating multiple surveillance system components to determine whether disease is absent. STM has infrequently been used to plan appropriate surveillance but this was the approach used here. A stochastic simulation model was implemented in R. The model represented the NZ commercial dairy and non-dairy cattle industries and the current surveillance components that are also planned to be used to gather evidence of absence of M. bovis once it is eradicated. Different surveillance intensities and risk based versus random surveillance were simulated and compared for probability of freedom, financial cost of sampling and testing and the time to demonstrate freedom.The results indicate that the current surveillance components will enable demonstration of freedom. Surveillance components included bulk tank milk testing, herd testing and testing at meat processing plants, predominantly using an imperfect ELISA. Several combinations of surveillance components appeared most efficient achieving >95 % confidence of freedom over 2–4 years, whilst sampling 4–7 % of the non-dairy herds and less than 25 % of dairy herds annually. The results indicate that surveillance intensity can be lower than is currently occurring to support the delimiting phase, thereby saving significant resources in the post eradication phase (proof of freedom phases).Further consideration is required to enable the assumption of 100 % herd specificity made in the model to be achieved. The ELISA used is very specific, but will yield some false positives that must be resolved to their true status. This may occur for example through modified diagnostic test interpretation (e.g. cut point optimisation at individual and herd level) or resolution of putative false positive herds with epidemiological investigation.In conclusion this research demonstrates the utility of STM for planning surveillance programs, and in this instance has highlighted efficient and effective surveillance components for demonstrating freedom from M. bovis in NZ. It also highlights the need to achieve 100 % specificity for M. bovis in herds tested during the proof of freedom phases.

Modelling and Simulation of the Effect of Targeted Decolonisation on Incidence of Extended-Spectrum Beta-Lactamase-Producing Enterobacterales Bloodstream Infections in Haematological Patients.

IntroductionHaematological patients are at higher risk of bloodstream infections (BSI) after chemotherapy. The aim of this study was to develop a simulation model assessing the impact of selective digestive decontamination (SDD) of haematological patients colonised with extended-spectrum beta-lactamase-producing Enterobacterales (ESBL-E) on the incidence of ESBL-E BSI after chemotherapy.MethodsA patient population was created by a stochastic simulation model mimicking the patients’ states of colonisation with ESBL-E during hospitalisation. A systematic literature search was performed to inform the model. All ESBL-E carriers were randomised (1:1) to either the intervention (targeted SDD) or the control group (placebo). ESBL-E BSI incidence was the outcome of the model. Sensitivity analyses were performed by prevalence of ESBL-E carriage at hospital admission (low: < 10%, medium: 10–25%, high: > 25%), duration of neutropenia after receiving chemotherapy, administration of antibiotic prophylaxis with quinolones, and time interval between SDD and chemotherapy.ResultsThe model estimated that the administration of targeted SDD before chemotherapy reduces the incidence of ESBL-E BSI in the hospitalised haematological population up to 27%. The greatest benefit was estimated in high-prevalence settings, regardless of the duration of neutropenia, the time interval before chemotherapy, and the administration of antibiotic prophylaxis with quinolones (p < 0.05). In medium-prevalence settings, SDD was effective in patients receiving quinolone prophylaxis, with either 1-day time interval before chemotherapy and a neutropenia duration > 6 days (p < 0.05) or 7-day time interval before chemotherapy and a neutropenia duration > 9 days (p < 0.05). No benefit was observed in low-prevalence settings.ConclusionsOur model suggests that targeted SDD could decrease the rate of ESBL-E BSI in haematological carriers before chemotherapy in the setting of high ESBL-E prevalence at hospital admission. These estimates require confirmation by well-designed multicentre RCTs, including the assessment of the impact on resistance/disruption patterns of gut microbiome.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40121-021-00550-3.

Economic losses due to clinical lameness in Kosovo dairy cattle

The health of dairy cows is an important factor affecting the profitability of dairy farms worldwide, and lameness is regarded as one of the most costly dairy cattle diseases. The aim of this study was to estimate the economic cost of cow lameness among Kosovo dairy farms. Data collected from 56 dairy farms were analysed with a farm-level stochastic (Monte Carlo) simulation model to estimate the cost of lameness. Lameness-associated sources of economic loss examined within the model included: reduced milk production, treatment cost, discarded milk, reduced cow body weight, and premature culling. Results showed that prevalence of lameness among cows on Kosovo dairy farms ranged from 17% to 39%. The average annual cost of lameness was estimated at €338.57 per farm (or €46.25 per cow). Reduced milk production was the largest financial contribution to the cost of lameness (45% of total economic loss) followed by premature culling (31% of total economic loss). Discarded milk, reduced cow body weight, and cost of treatment each contributed approximately 8% to the total economic loss. These findings indicate that dairy farmers need to be more cognizant of the financial losses associated with lameness and should be encouraged to implement management strategies to reduce lameness as a means of enhancing farm profitability.