Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Pillai, Nisha; Ramkumar, Mahalingam; Nanduri, Bindu

doi:10.3390/microorganisms10101911

Cited by 16 publications

(7 citation statements)

References 81 publications

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“…sea surface temperature, sea level, river flow, river height) and compare a broad range of analytical methods. In particular, machine learning and artificial intelligence could be used to automate more robust prediction models [ 40 ]. Moreover, this research did not examine interactions between climate variables (e.g.…”

Section: Discussionmentioning

confidence: 99%

Predictive modelling of Ross River virus using climate data in the Darling Downs

Meadows

McMichael²,

Campbell³

2023

Epidemiol. Infect.

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Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 -June 2014 were used for model training. Model predictions were tested using data for July 2014 -June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.

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Section: Discussionmentioning

confidence: 99%

Predictive modelling of Ross River virus using climate data in the Darling Downs

Meadows

McMichael²,

Campbell³

2023

Epidemiol. Infect.

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“…In this way, feature extraction helps in identifying and extracting relevant attributes or features from the data that are likely to be informative for disease detection. Feature selection helps in reducing the dimensionality of the data, making the AI model more efficient and accurate (Pillai et al, 2022).…”

Section: Artificial Intelligence and Its Application In The Predictio...mentioning

confidence: 99%

Artificial Intelligence and its Application in the Prediction and Diagnosis of Animal Diseases: A Review

AlZubi

2023

IJAR

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The role of artificial intelligence (AI) in veterinary science is becoming increasingly important as the technology advances. AI applications have the potential to revolutionize the prediction and diagnosis of animal diseases, thereby improving animal health by enhancing disease management. Given the complexity and subjectivity associated with evaluating medical images, as well as the difficulties inherent in handling animals, automating the analysis process using deep learning and artificial intelligence techniques would be highly beneficial. The main focus of the research study is to examine the applications of artificial intelligence in the prediction and diagnosis of Animal Diseases by the comprehensive process of literature review. It was found that many researchers have been using these techniques for diagnosis from the images, creating software that can help doctors, veterinarians and radiologists in their day-to-day work. The study includes machine learning and deep learning algorithms like PLS-DA, ANN and CNN models for the prediction and characterization of lesions. The results were analyzed and compared to obtain a high-accuracy model.

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“…Bacterial agents are predominant among the food-borne pathogens from animal sources; for example, Salmonella spp., Staphylococcus aureus , Escherichia coli (E. coli) , and Listeria monocytogenes [ 3 , 4 ]. Food animals serve as reservoirs of zoonotic pathogens, hence crucial in transmitting infection from food sources [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021

Kabantiyok,

Gyang,

Agada

et al. 2023

Veterinary Sciences

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Farm animals harbour bacterial pathogens, which are often viewed as important indicators of animal health and determinants of food safety. To better understand the prevalence and inform treatment, we audited laboratory data at the Bacteriology Laboratory of the NVRI from 2018–2021. Antibiotics were classified into seven basic classes: quinolones, tetracyclines, beta-lactams, aminoglycosides, macrolides, nitrofuran, and cephalosporins. Trends were analysed using a generalised linear model with a log link function for the Poisson distribution, comparing proportions between years with an offset to account for the variability in the total number of organisms per year. Avian (73.18%) samples were higher than any other sample. The major isolates identified were Escherichia. coli, Salmonella spp., Klebsiella spp., Staphylococcus spp., Proteus spp., and Pseudomonas spp. We found that antimicrobial resistance to baseline antibiotics increased over the years. Of particular concern was the increasing resistance of Klebsiella spp. to cephalosporins, an important second-generation antibiotic. This finding underscores the importance of farm animals as reservoirs of pathogens harbouring antimicrobial resistance. Effective biosecurity, surveillance, and frugal use of antibiotics in farms are needed because the health of humans and animals is intricately connected.

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Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Cited by 16 publications

References 81 publications

Predictive modelling of Ross River virus using climate data in the Darling Downs

Predictive modelling of Ross River virus using climate data in the Darling Downs

Artificial Intelligence and its Application in the Prediction and Diagnosis of Animal Diseases: A Review

Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021

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