Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .

One World, One Health: Zoonotic Diseases, Parasitic Diseases, and Infectious Diseases

When we take into account how the boundaries between human, animal, and environmental health are inextricably linked and increasingly intertwined, it comes as no surprise that the One Health approach has assumed an unprecedented level of importance over the past decade [...].

Helicobacter pullorum and Helicobacter canadensis: Etiology, pathogenicity, epidemiology, identification, and antibiotic resistance implicating food and public health

Nowadays, it is well-established that the consumption of poultry meat, especially chicken meat products has been drastically increasing. Even though more attentions are being paid to the major foodborne pathogens, it seems that scientists in the area of food safety and public health would prefer tackling the minor food borne zoonotic emerging or reemerging pathogens, namely Helicobacter species. Recently, understanding the novel aspects of zoonotic Enterohepatic Helicobacter species, including pathogenesis, isolation, identification, and genomic features is regarded as a serious challenge. In this regard, considerable attention is given to emerging elusive zoonotic Enterohepatic Helicobacter species, comprising Helicobacter pullorum and Helicobacter canadensis. In conclusion, the current review paper would attempt to elaborately summarize and somewhat compare the etiology, pathogenesis, cultivation process, identification, genotyping, and antimicrobial resistance profile of both H. pullorum and H. Canadensis. Further, H. pullorum has been introduced as the most significant food borne pathogen in chicken meat products.

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

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.