Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

Sedighi, Tabassom; Varga, Liz

doi:10.3390/ijerph18073451

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…This technique is becoming popular for aiding decision-making in several domains due to the evolution of computational capacity, which makes possible the calculation of complex BN [ 44 ]. Applications of BN methods are found in a growing number of disciplines and policies [ 14 , 41 , 45 , 46 ].…”

Section: Evaluation Method: Probabilistic Graphical Modelsmentioning

confidence: 99%

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Sedighi

Varga²,

Hosseinian-Far

et al. 2021

IJERPH

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The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

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Section: Evaluation Method: Probabilistic Graphical Modelsmentioning

confidence: 99%

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Sedighi

Varga²,

Hosseinian-Far

et al. 2021

IJERPH

Self Cite

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show abstract

“…ly, failure to follow proper health and anti-tuberculosis prevention measures. These measures include maintaining good sanitation on holdings, disinfecting facilities and farm territories, and using disinfected manure (Bouchez-Zacria et al, 2018; Sedighi and Varga, 2021).…”

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confidence: 99%

Baktericidna svojstva nekih organskih kiselina u odnosu na mikobakterije

Paliy,

Pavlichenko,

Berezovskyi

et al. 2023

Vet. stn. (Online)

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Despite current successes in fighting animal tuberculosis, sporadic cases of infection with both the disease’s causative agents and atypical mycobacteria still occur in animals. Since animal mycobacteriosis cannot be treated with medication, priority must be given to their prevention and elimination as non-specific measures, including disinfection. The unique structure of the mycobacteria cell de- termines their high resistance to antimicrobial agents, therefore the constant search for effective disinfectants is an urgent task of veterinary science. This study aimed to determine the bactericidal properties of several inorganic acids against mycobacteria. Experiments were carried out following modern methodological aspects using the atypical mycobacteria M. fortuitum, pathogens of tuberculosis M. bovis and M. avium. Orthophosphoric (phosphoric) acid (H3PO4) and nitric acid (HNO3) were used in the experiments. Using the suspension method, orthophosphoric acid was found to have a bactericidal effect against atypical mycobacteria- ria M. fortuitum and the pathogen of tuberculoussis M. avium when tested at a concentration of 1.5% (24 hours) and 2.0% (1–24 hours), against the causative agent of tuberculosis M. bovis at a concentration of 1.5% (5–24 hours) and 2.0% (1–24 hours). Nitric acid showed a bactericidal effect when tested in solution against mycobacteriaria M. fortuitum and M. avium at a concentration of 2.0% (5–24 hours), and against M. bovis at a concentration of 2.0% after 1 hour exposure. A different level of mycobacterial growth intensity in test tubes was observed after the action of inorganic acids in sub-bactericidal and bacteriostatic concentrations, which was directly dependent on the concentration and exposure of the acid, and on the type of test culture of microorganisms. It has been shown that inorganic acids can disinfect surfaces contaminated with the tuberculosis-causing agent. This refers to various surfaces like wood, tile, fabric, glass, and metal. It has been found that orthophosphoric acid (1.5% for 24 hours) and nitric acid (2.0% for 5 hours) can be used to disinfect in cases of tuberculosis infection. These findings have been confirmed through bioassays on laboratory animals. Future research will focus on discovering and developing new medications with strong bactericidal properties against mycobacteria.

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Machine Learning Techniques for the Prediction of Bovine Tuberculosis Among the Cattle

Roy

Ravindra

Marada

et al. 2023

Lecture Notes in Networks and Systems

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Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

Cited by 6 publications

References 38 publications

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Baktericidna svojstva nekih organskih kiselina u odnosu na mikobakterije

Machine Learning Techniques for the Prediction of Bovine Tuberculosis Among the Cattle

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