Invasive meningococcal disease (IMD) is an uncommon but serious and potentially fatal condition mainly affecting children and adolescents. Active surveillance between 2005 and 2016 at Tijuana General Hospital, Mexico, indicated that the incidence of IMD in Tijuana was higher than previously thought, at 2.69 per 100,000 population aged <16 years. The objective of this study was to estimate the economic burden associated with 51 IMD cases in children aged <16 years identified over the 11 years of active surveillance at Tijuana General Hospital, Mexico. Healthcare resource usage for the IMD cases was obtained from the hospital database and combined with unit costs from the hospital purchasing department or national databases to estimate total healthcare costs over a follow-up period of 3 months. Societal costs were represented by the value of lost wages for parents or guardians. All costs were expressed in US$. Over the 11-year study period there were 51 IMD cases, of which 13 (25%) were fatal. The total cost for all 51 cases over the 11-year study period was US$1,054,499 (average per case US$20,676), of which direct healthcare costs comprised US$1,029,948 (average per case US$20,195) and societal costs US$24,551 (average per case US$481). Extrapolated to the population of Tijuana region aged <16 years, the estimated annual economic burden of IMD was US$268,794. The major cost driver was the cost of hospitalization. These data illustrate the significant economic burden associated with IMD in Tijuana, and will be useful in assessing optimal vaccination programs against meningococcal disease in Mexico. GRAPHICAL ABSTRACT

Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings.

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction.