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Not AvailableThe basic reproduction number (R0) is a measure of an infectious pathogen's infectiousness or transmissibility. It is the number of instances caused directly by an infected individual over the course of its infectious period. R0 is a statistic that is used to determine a disease's potential to spread within a population. It has been regarded as one of the most basic and widely used metrics for studying infectious disease dynamics. In the present study, we focused on the basic reproduction number (R0), that describes an epidemic's transmission potential. Data was obtained from the Dept. of animal husbandry, Bengaluru, Karnataka. For risk assessment and to explore the influence of changes in precipitation levels, disease incidence data was categorized into two groups, average annual precipitation above and below normal (1151mm). R0 package was used for the purpose of calculating the basic reproduction number. The variation in R0 ranged from 1.06 to 1.78 with average annual precipitation above normal and 0.76 to 2.08 with average annual precipitation below normal. The objective was to evaluate the R0 of anthrax disease among livestock in Karnataka corresponding to changes in precipitation level and to describe the variation of R0, to assess an infectious disease's ability to infect the population, and to determine the fraction of the population that should be vaccinated in order to prevent epidemic growth.Not Availabl

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Not AvailableAnthrax is a one of the zoonotic diseases existing in India. Early detection of anthrax outbreaks is crucial for minimizing anthrax morbidity and death, as well as the risk of anthrax transmission in the population. Objective of the present research is to develop a disease prediction model by employing Machine-Learning techniques to assess the risk of anthrax analogous to the impact of changes in precipitation level that can benefit as an early warning system for detecting future anthrax outbreaks among livestock across Karnataka. By considering the disease incidence data during 2000 to 2019, livestock population data and the ecological parameters, the machine learning model was successful in identifying the next outbreak susceptible areas and the parameters that contribute significantly to the disease outbreak. Machine learning model was developed by R statistical software version 3.1.3 using different data mining regression and classification models viz., GLM, GAM, MARS, FDA, CT, SVM, NB, ADA, RF, GBM and ANN. Disease incidence data was collected from Department of animal husbandry, Bengaluru, Karnataka. Disease incidence data was divided in two groups based on average annual precipitation above and below normal (1151mm) for the risk assessment and study the impact of changes in precipitation level. Data with average annual-precipitation above normal was predicted with high risk in the north, northern east and the state's southern region. Whereas data with average annual-precipitation below normal was predicted with high risk in south, northern east and the state's central region. Cohen's Kappa, ROC curve, True Skill Statistics (TSS), and ACCURACY was used to assess the models performance. Further, this model can be intensified and validated using the anthrax outbreak data available at national level which will be useful for policymakers to formulate control strategiesNot Availabl

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Not AvailableAnthrax is a one of the zoonotic diseases existing in India. Early detection of anthrax outbreaks is crucial for minimizing anthrax morbidity and death, as well as the risk of anthrax transmission in the population. Objective of the present research is to develop a disease prediction model by employing Machine-Learning techniques to assess the risk of anthrax analogous to the impact of changes in precipitation level that can benefit as an early warning system for detecting future anthrax outbreaks among livestock across Karnataka.By considering the disease incidence data during 2000 to 2019, livestock population data and the ecological parameters, the machine learning model was successful in identifying the next outbreak susceptible areas and the parameters that contribute significantly to the disease outbreak. Machine learning model was developed by R statistical software version 3.1.3 using different data mining regression and classification models viz., GLM, GAM, MARS, FDA, CT, SVM, NB, ADA, RF, GBM and ANN. Disease incidence data was collected from Department of animal husbandry, Bengaluru, Karnataka. Disease incidence data was divided in two groups based on average annual precipitation above and below normal (1151mm) for the risk assessment and study the impact of changes in precipitation level. Data with average annual-precipitation above normal was predicted with high risk in the north, northern east and the state's southern region. Whereas data with average annual-precipitation below normal was predicted with high risk in south, northern east and the state's central region. Cohen's Kappa, ROC curve, True Skill Statistics (TSS), and ACCURACY was used to assess the models' performance. Further, this model can be intensified and validated using the anthrax outbreak data available at national level which will be useful for policymakers to formulate control strategies.Not Availabl

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Not AvailableAnthrax is an ancient and acuteillness that affects alarge quantity of animal species and is caused by a bacterium Bacillus anthracis, which is a rod-shaped, gram-positive and spore-forming bacterium. Virulent forms of B.anthracishas two large pathogenicity related plasmids pXO1 and pXO2. pXO1 has the different anthrax toxin genes cya, lef, and pagA where as pXO2 has the genes accountable for capsule synthesis and degradation, capA, capB, capC, and capD. B. anthracis express its pathogenic activity mostly over the capsule and the manufacture of a toxic compound involving three proteins known as edema factor (EF), lethal factor (LF) and protective antigen (PA). These two enormous plasmids of B.anthracisare crucial for full pathogenicity, exclusion of either of the plasmids extremely weakens the malignity of B. anthracis. In the current study we conducted the relative analysis of the codon usage and nucleotide bias of virulent genes subsist in pXO1 plasmid of B.anthracis. Codon usage bias not only plays a substantial role at the extent of gene expression, but also supports to improve the efficacy and accurateness of translation. Codon usage pattern analysis of B. anthracisgenome is essential for understanding the evolutionary characteristicsin the different species. To examine the codon usage arrangement of theB.anthracisgenome, Nucleotide sequences of the virulent genes viz cya, lef and pag were collected from National Center for Biotechnology Information (NCBI). The correlations between GC3s, whole GC content, Effective No. of Codons (ENC), Codon Adaptation Index (CAI), Codon Bias Index (CBI), Frequency of Optimal Codons (FOP), General average hydropathicity (Gravy) and Aromaticity (Aroma), of the selected genes were determined. The ENC-plot i.e., ENc values vs GC3s, Pr2 plot i.e., relationship between A3 / (A3 +T3) and G3 / (G3 +C3), Neutrality plot i.e., GC12 versus GC3s, and the RSCU of the genes, all shows codon usage bias existence in all the virulent genes subsists in pXO1 plasmid of B.anthracis genome. These results expresses the codon usage bias existing in the pXO1 plasmid’s virulent genes of B.anthracis genome could be utilized for further exploration on their evolutionary analysis as in design of primers, design of transgenes, determine of origin of species as well as prediction of gene expression level and gene functionNot Availabl

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Not AvailableBackground and Aim: Anthrax, caused by the soil-borne spore-forming bacteria called Bacillus anthracis, is a zoonotic disease that persists worldwide in livestock and wildlife and infects humans. It is a great hazard to livestock; henceforth, evaluating the global concerns about the disease occurrence in livestock is essential. This study was conducted to estimate the global prevalence of anthrax and predict high-risk regions, which could be an input to veterinarians to take necessary steps to control and avoid the disease. Materials and Methods: A literature review was performed using online databases, namely, PubMed, Google Scholar, Scopus, Biomed Central, and Science Direct, to extract relevant publications worldwide between 1992 and 2020. Initially, 174 articles were selected, and after scrutinizing, 24 articles reporting the prevalence of anthrax were found to be adequate for the final meta-analysis. The statistical study was accompanied by employing fixed effects and random effects models using R. Results: The pooled prevalence of anthrax globally was 28% (95% confidence interval, 26-30%) from 2452 samples through the fixed effects model. Continent-wise subgroup analysis through the random effects model revealed that the pooled prevalence of anthrax was highest in Africa (29%) and least in North America (21%). Conclusion: In these publications, anthrax causes economic loss to farmers and, thus, to the world. Hence, controlling anthrax infections in high-risk regions are essential by implementing appropriate control measures to decrease the effect of the disease, thereby reducing economic lossNot Availabl

An Estimate of Global Anthrax Prevalence in Livestock: A Meta-analysis

Background and Aim: Anthrax, caused by the soil-borne spore-forming bacteria called Bacillus anthracis, is a zoonotic disease that persists worldwide in livestock and wildlife and infects humans. It is a great hazard to livestock; henceforth, evaluating the global concerns about the disease occurrence in livestock is essential. This study was conducted to estimate the global prevalence of anthrax and predict high-risk regions, which could be an input to veterinarians to take necessary steps to control and avoid the disease. Materials and Methods: A literature review was performed using online databases, namely, PubMed, Google Scholar, Scopus, Biomed Central, and Science Direct, to extract relevant publications worldwide between 1992 and 2020. Initially, 174 articles were selected, and after scrutinizing, 24 articles reporting the prevalence of anthrax were found to be adequate for the final meta-analysis. The statistical study was accompanied by employing fixed effects and random effects models using R. Results: The pooled prevalence of anthrax globally was 28% (95% confidence interval, 26-30%) from 2452 samples through the fixed effects model. Continent-wise subgroup analysis through the random effects model revealed that the pooled prevalence of anthrax was highest in Africa (29%) and least in North America (21%). Conclusion: In these publications, anthrax causes economic loss to farmers and, thus, to the world. Hence, controlling anthrax infections in high-risk regions are essential by implementing appropriate control measures to decrease the effect of the disease, thereby reducing economic loss