Abstract
Introduction Bacterial diseases exhibit seasonal trends, necessitating their monitoring for outbreak prediction, treatment optimization, and infection control. This study explores seasonal trends, temperature correlations, and antimicrobial resistance profiles of key pathogens in an Indian tertiary care setting. Methodology This cross-sectional study analyzed bacterial isolates from 1,562 patient samples, including Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Escherichia coli, and Enterococcus faecalis. Monthly infection rates and seasonal patterns were visualized using heatmaps and time-series graphs. Pearson's correlation assessed the relationship between these infection rates and temperature. Antibiotic susceptibility was evaluated using VITEK2, with resistance patterns visualized in R. Results Infections peaked in April (n = 163, 10.43%) and March (n = 161, 10.30%), with S . aureus as the most common pathogen (n = 271, 25.64%), followed by K . pneumoniae (n = 201, 19.02%) and P . aeruginosa (n = 178, 16.84%). Seasonal trends showed S. aureus infections peaked in summer (n = 45, 16.6%), while P. aeruginosa (n = 27, 15.2%) and E. faecalis (n = 24, 25.5%) peaked in winter. Temperature correlated positively with S. aureus infection (r = 0.814, P = 0.001) and negatively with P. aeruginosa (r = -0.845, P = 0.001), and E . faecalis (r = -0.618, P = 0.032), with no correlation observed for K. pneumoniae, A. baumannii, and E. coli. Multi-drug resistance (MDR), extensively drug resistance (XDR), and pandrug resistance were more prevalent in Gram-negative than in Gram-positive bacteria. Conclusions This study reveals temperature-driven seasonal patterns in bacterial infections, aiding outbreak prediction and prevention. The findings emphasize the threat of multidrug resistance, particularly in Gram-negative bacteria, reinforcing the need for enhanced infection control and targeted antibiotic stewardship.