scholarly journals Dynamics of Salmonella transmission on a British pig grower-finisher farm: a stochastic model

2007 ◽  
Vol 136 (3) ◽  
pp. 320-333 ◽  
Author(s):  
A. A. HILL ◽  
E. L. SNARY ◽  
M. E. ARNOLD ◽  
L. ALBAN ◽  
A. J. C. COOK
Keyword(s):  
Control Strategies ◽  
Meat Consumption ◽  
Monitoring Programme ◽  
Transmission Model ◽  
Effective Control ◽  
Model Parameters ◽  
Salmonella Infections ◽  
Food Standards ◽  
Modelling Studies ◽  
Slaughter Age

SUMMARYPrevious modelling studies have estimated that between 1% and 10% of human salmonella infections are attributable to pig meat consumption. In response to this food safety threat the British pig industry have initiated a salmonella monitoring programme. It is anticipated that this programme will contribute to achieving a UK Food Standards Agency target for reducing salmonella levels in pigs at slaughter by 50% within 5 years. In order to better inform the monitoring programme, we have developed a stochastic transmission model for salmonella in a specialist grower-finisher pig herd, where data from a Danish longitudinal study have been used to estimate some of the key model parameters. The model estimates that about 17% of slaughter-age pigs will be infected with salmonella, and that of these infected pigs about 4% will be excreting the organism. In addition, the model shows that the most effective control strategies will be those that reduce between-pen transmission.

Fuzzy Semi-Active Control of Multi-Degree-of-Freedom Structure Using Magnetorheological Elastomers

2017 ◽  
Author(s):  
Nguyen Xuan Bao ◽  
Toshihiko Komatsuzaki ◽  
Yoshio Iwata ◽  
Haruhiko Asanuma
Keyword(s):  
Active Control ◽  
Control Strategy ◽  
Control Strategies ◽  
Excitation Frequency ◽  
Harmonic Excitation ◽  
Effective Control ◽  
Magnetic Flux Density ◽  
Model Parameters ◽  
Structural Vibrations ◽  
Standard Linear Solid

Magnetorheological elastomer (MRE), used in semi-active control, has recently emerged as a smart material that could potentially improve traditional systems in controlling structural vibrations. This study considers two main issues concerning the application of an MRE. The first issue is the modelling and identification of the viscoelastic property, and the second is the formulation of an effective control strategy based on the fuzzy logic system. Firstly, a nonlinear dynamic MRE model was developed to simulate the dynamic behavior of MRE. In this model, the viscoelastic force of the material as an output was calculated from displacement, frequency, and magnetic flux density as inputs. The MRE model consisted of three components including the viscoelasticity of host elastomer, magnetic field-induced property, and interfacial slippage that were modeled by analogy with a standard linear solid model (Zener model), a stiffness variable spring, and a smooth Coulomb friction, respectively. The model parameters were identified by manipulating two sets of data that were measured by changing applied electric current and harmonic excitation frequency. A good agreement was obtained between numerical and experimental results. The proposed model offers a beneficial solution to numerically investigate vibration control strategies. Secondly, a fuzzy semi-active controller was designed for seismic protection of building with an MRE-based isolator. The control strategy was designed to determine the command applied current. The proposed strategy is fully adequate to the nonlinearity of the isolator and works independently with the building structure. The efficiency of the proposed fuzzy semi-active controller was investigated numerically by MATLAB simulations, whose performance was compared with that of passive systems and a system with traditional semi-active controller. Numerical results show that the developed fuzzy semi-active controller not only mitigates the responses of both the base floor and the superstructure, but also has an ability to control structural vibrations adaptively to the different intensity ground motions.

Research on Control Strategies and Optimal Signal Timing for Traffic Congestion Based on Prediction

2010 ◽  
Vol 40-41 ◽  
pp. 858-865
Author(s):  
Shu Bin Li ◽  
Yong Lin ◽  
Hua Ling Ren ◽  
Jian Cheng Long
Keyword(s):  
Traffic Congestion ◽  
Control Strategies ◽  
Urban Traffic ◽  
Transmission Model ◽  
Effective Control ◽  
Traffic Network ◽  
Cell Transmission Model ◽  
Traffic Jams ◽  
Urban Traffic Network ◽  
The Difference

Designing effective control strategies to traffic jams is an important measure to solve the problem of traffic jams in urban traffic network. Most of the green ratio models clear off crowded traffic flow synchronously at each approach of signal intersection in oversaturated traffic, but ignore the difference of traffic flows in different traffic state, and lead to the queue becoming longer and longer at oversaturated signal intersection. In this paper, the cell transmission model is applied to the propagation of traffic jams, the formation and dissipation of traffic jams in urban traffic network. A new method for predicting the traffic states is proposed, and the future traffic states can be achieved, according to the spatial structure of traffic jam propagation. We use an idea of traffic priority as an management measure to design the optimal green ratio in advance, and the improved green ratio model can realize the goal of preventing traffic congestion and clearing off traffic blockage quickly. Simulation results show that the proposed strategies with appropriate application can effectively control jam dissipation and prevent traffic congestion formation.

scholarly journals Modeling the COVID-19 Epidemic With Multi-Population and Control Strategies in the United States

2022 ◽  
Vol 9 ◽  
Author(s):  
Deshun Sun ◽  
Xiaojun Long ◽  
Jingxiang Liu
Keyword(s):  
United States ◽  
Mathematical Model ◽  
Control Strategies ◽  
The United States ◽  
Basic Reproductive Number ◽  
Effective Control ◽  
Model Parameters ◽  
Cumulative Number ◽  
And Control ◽  
Sensitive Parameters

As of January 19, 2021, the cumulative number of people infected with coronavirus disease-2019 (COVID-19) in the United States has reached 24,433,486, and the number is still rising. The outbreak of the COVID-19 epidemic has not only affected the development of the global economy but also seriously threatened the lives and health of human beings around the world. According to the transmission characteristics of COVID-19 in the population, this study established a theoretical differential equation mathematical model, estimated model parameters through epidemiological data, obtained accurate mathematical models, and adopted global sensitivity analysis methods to screen sensitive parameters that significantly affect the development of the epidemic. Based on the established precise mathematical model, we calculate the basic reproductive number of the epidemic, evaluate the transmission capacity of the COVID-19 epidemic, and predict the development trend of the epidemic. By analyzing the sensitivity of parameters and finding sensitive parameters, we can provide effective control strategies for epidemic prevention and control. After appropriate modifications, the model can also be used for mathematical modeling of epidemics in other countries or other infectious diseases.

scholarly journals Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
Author(s):  
Aidan C. Daly ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
Chris Holmes
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Biological Models ◽  
Exact Inference ◽  
Modelling Studies ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Marine Organisms for the Sustainable Management of Plant Parasitic Nematodes

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 369
Author(s):  
Pasqua Veronico ◽  
Maria Teresa Melillo
Keyword(s):  
Crop Production ◽  
Control Strategies ◽  
Marine Organisms ◽  
New Drugs ◽  
Effective Control ◽  
Parasitic Nematodes ◽  
Plant Parasitic Nematodes ◽  
Wide Range ◽  
Plant Growth Stimulants ◽  
Plant Parasitic

Plant parasitic nematodes are annually responsible for the loss of 10%–25% of worldwide crop production, most of which is attributable to root-knot nematodes (RKNs) that infest a wide range of agricultural crops throughout the world. Current nematode control tools are not enough to ensure the effective management of these parasites, mainly due to the severe restrictions imposed on the use of chemical pesticides. Therefore, it is important to discover new potential nematicidal sources that are suitable for the development of additional safe and effective control strategies. In the last few decades, there has been an explosion of information about the use of seaweeds as plant growth stimulants and potential nematicides. Novel bioactive compounds have been isolated from marine cyanobacteria and sponges in an effort to find their application outside marine ecosystems and in the discovery of new drugs. Their potential as antihelmintics could also be exploited to find applicability against plant parasitic nematodes. The present review focuses on the activity of marine organisms on RKNs and their potential application as safe nematicidal agents.

scholarly journals Label-Free Comparative Proteomics of Differentially Expressed Mycobacterium tuberculosis Protein in Rifampicin-Related Drug-Resistant Strains

Pathogens ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 607
Author(s):  
Nadeem Ullah ◽  
Ling Hao ◽  
Jo-Lewis Banga Ndzouboukou ◽  
Shiyun Chen ◽  
Yaqi Wu ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Control Strategies ◽  
Multidrug Resistant ◽  
Differentially Expressed ◽  
Effective Control ◽  
Drug Resistant ◽  
Differentially Expressed Proteins ◽  
Label Free ◽  
Candidate Target

Rifampicin (RIF) is one of the most important first-line anti-tuberculosis (TB) drugs, and more than 90% of RIF-resistant (RR) Mycobacterium tuberculosis clinical isolates belong to multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. In order to identify specific candidate target proteins as diagnostic markers or drug targets, differential protein expression between drug-sensitive (DS) and drug-resistant (DR) strains remains to be investigated. In the present study, a label-free, quantitative proteomics technique was performed to compare the proteome of DS, RR, MDR, and XDR clinical strains. We found iniC, Rv2141c, folB, and Rv2561 were up-regulated in both RR and MDR strains, while fadE9, espB, espL, esxK, and Rv3175 were down-regulated in the three DR strains when compared to the DS strain. In addition, lprF, mce2R, mce2B, and Rv2627c were specifically expressed in the three DR strains, and 41 proteins were not detected in the DS strain. Functional category showed that these differentially expressed proteins were mainly involved in the cell wall and cell processes. When compared to the RR strain, Rv2272, smtB, lpqB, icd1, and folK were up-regulated, while esxK, PPE19, Rv1534, rpmI, ureA, tpx, mpt64, frr, Rv3678c, esxB, esxA, and espL were down-regulated in both MDR and XDR strains. Additionally, nrp, PPE3, mntH, Rv1188, Rv1473, nadB, PPE36, and sseA were specifically expressed in both MDR and XDR strains, whereas 292 proteins were not identified when compared to the RR strain. When compared between MDR and XDR strains, 52 proteins were up-regulated, while 45 proteins were down-regulated in the XDR strain. 316 proteins were especially expressed in the XDR strain, while 92 proteins were especially detected in the MDR strain. Protein interaction networks further revealed the mechanism of their involvement in virulence and drug resistance. Therefore, these differentially expressed proteins are of great significance for exploring effective control strategies of DR-TB.

scholarly journals High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

scholarly journals The use of light spectrum blocking films to reduce populations of Drosophila suzukii Matsumura in fruit crops

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michelle T. Fountain ◽  
Amir Badiee ◽  
Sebastian Hemer ◽  
Alvaro Delgado ◽  
Michael Mangan ◽  
...  
Keyword(s):  
Control Strategies ◽  
Field Trials ◽  
Biological Data ◽  
Insect Vision ◽  
Invasive Pest ◽  
Effective Control ◽  
Fruit Crops ◽  
Light Spectrum ◽  
Drosophila Suzukii ◽  
The Impact

Abstract Spotted wing drosophila, Drosophila suzukii, is a serious invasive pest impacting the production of multiple fruit crops, including soft and stone fruits such as strawberries, raspberries and cherries. Effective control is challenging and reliant on integrated pest management which includes the use of an ever decreasing number of approved insecticides. New means to reduce the impact of this pest that can be integrated into control strategies are urgently required. In many production regions, including the UK, soft fruit are typically grown inside tunnels clad with polyethylene based materials. These can be modified to filter specific wavebands of light. We investigated whether targeted spectral modifications to cladding materials that disrupt insect vision could reduce the incidence of D. suzukii. We present a novel approach that starts from a neuroscientific investigation of insect sensory systems and ends with infield testing of new cladding materials inspired by the biological data. We show D. suzukii are predominantly sensitive to wavelengths below 405 nm (ultraviolet) and above 565 nm (orange & red) and that targeted blocking of lower wavebands (up to 430 nm) using light restricting materials reduces pest populations up to 73% in field trials.

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