Predicting antimicrobial resistance of bacterial pathogens using time series analysis.

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Application of Bayesian Regression for the Identification of a Catchment Area for Cancer Cases in Dogs and Cats.

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

Molecular Evolution of Porcine Reproductive and Respiratory Syndrome Virus Field Strains from Two Swine Production Systems in the Midwestern United States from 2001 to 2020.

Porcine reproductive and respiratory syndrome virus (PRRSV) poses an extensive economic threat to the United States swine industry. The high degree of PRRSV genetic and antigenic variability challenges existing vaccination programs. We evaluated the ORF5 sequence of 1,931 PRRSV-2 strains detected from >300 farms managed by two pork production systems in the midwestern United States from 2001 to 2020 to assess the genetic diversity and molecular characteristics of heterologous PRRSV-2 strains. Phylogenetic analysis was performed on ORF5 sequences and classified using the global PRRSV classification system. N-glycosylation and the global and local selection pressure in the putative GP5 encoded by ORF5 were estimated. The PRRSV-2 sequences were classified into lineage 5 (L5; n = 438[22.7%]) or lineage 1 (L1; n = 1,493[77.3%]). The L1 strains belonged to one of three subclades: L1A (n = 1,225[63.4%]), L1B (n = 69[3.6%]), and L1C/D (n = 199[10.3%]). 10 N-glycosylation sites were predicted, and positions N44 and N51 were detected in most GP5 sequences (n = 1,801[93.3%]). Clade-specific N-glycosylation sites were observed: 57th in L1A, 33rd in L1B, 30th and 34th in L1C/D, and 30th and 33rd in L5. We identified nine and 19 sites in GP5 under significant positive selection in L5 and L1, respectively. The 13th, 151st, and 200th positive selection sites were exclusive to L5. Heterogeneity of N-glycosylation and positive selection sites may contribute to varying the evolutionary processes of PRRSV-2 strains circulating in these swine production systems. L1A and L5 strains denoted excellence in adaptation to the current swine population by their extensive positive selection sites with higher site-specific selection pressure. IMPORTANCE Porcine reproductive and respiratory syndrome virus (PRRSV) is known for its high genetic and antigenic variability. In this study, we evaluated the ORF5 sequences of PRRSV-2 strains circulating in two swine production systems in the midwestern United States from 2001 to 2020. All the field strains were classified into four major groups based on genetic relatedness, where one group is closely related to the Ingelvac PRRS MLV strain. Here, we systematically compared differences in the ORF5 polymorphisms, N-glycosylation sites, and local and global evolutionary dynamics between different groups. Sites 44 and 51 were common for N-glycosylation in most amino acid sequences (n = 1,801, 93.3%). We identified that the L5 sequences had more positive selection pressure compared to the L1 strains. Our findings will provide valuable insights into the evolutionary mechanisms of PRRSV-2 and these molecular changes may lead to suboptimal effectiveness of Ingelvac PRRS MLV vaccine.

Climate change and zoonoses: A review of the current status, knowledge gaps, and future trends.

Emerging infectious diseases (EIDs), especially those with zoonotic potential, are a growing threat to global health, economy, and safety. The influence of global warming and geoclimatic variations on zoonotic disease epidemiology is evident by alterations in the host, vector, and pathogen dynamics and their interactions. The objective of this article is to review the current literature on the observed impacts of climate change on zoonoses and discuss future trends. We evaluated several climate models to assess the projections of various zoonoses driven by the predicted climate variations. Many climate projections revealed potential geographical expansion and the severity of vector-borne, waterborne, foodborne, rodent-borne, and airborne zoonoses. However, there are still some knowledge gaps, and further research needs to be conducted to fully understand the magnitude and consequences of some of these changes. Certainly, by understanding the impact of climate change on zoonosis emergence and distribution, we could better plan for climate mitigation and climate adaptation strategies.

Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene.

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

A pilot case-control study using a one health approach to evaluate behavioral, environmental, and occupational risk factors for chronic kidney disease of unknown etiology in Sri Lanka.

BACKGROUND: Chronic kidney disease of unknown etiology (CKDu) was first recognized in Sri Lanka in the early 1990s, and since then it has reached epidemic levels in the North Central Province of the country. The prevalence of CKDu is reportedly highest among communities that engage in chena and paddy farming, which is most often practiced in the dry zone including the North Central and East Central Provinces of Sri Lanka. Previous studies have suggested varied hypotheses for the etiology of CKDu; however, there is not yet a consensus on the primary risk factors, possibly due to disparate study designs, sample populations, and methodologies. METHODS: The goal of this pilot case-control study was to evaluate the relationships between key demographic, cultural, and occupational variables as risk factors for CKDu, with a primary interest in pesticide exposure both occupationally and through its potential use as an ingredient in brewed kasippu alcohol. An extensive one health focused survey was developed with in cooperation with the Centre for Research, Education, and Training on Kidney Diseases of Sri Lanka. RESULTS: A total of 56 CKDu cases and 54 control individuals were surveyed using a proctored, self-reported questionnaire. Occupational pesticide exposure and alcohol consumption were not found to be significant risk factors for CKDu. However, a statistically significant association with CKDu was observed with chewing betel (adjusted odds ratio [aOR]: 6.11, 95% confidence interval [CI]: 1.93, 19.35), age (aOR: 1.07, 95% CI: 1.02, 1.13), owning a pet dog (aOR: 3.74, 95% CI: 1.38, 10.11), water treatment (aOR: 3.68, 95% CI: 1.09, 12.43) and pests in the house (aOR: 5.81, 95% CI: 1.56, 21.60). CONCLUSIONS: The findings of this study suggest future research should focus on practices associated with chewing betel, potential animal interactions including pests in the home and pets, and risk factors associated with water. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42522-020-00034-3.