Multiple waves of infection were observed in many states in India during the coronavirus disease 2019 (COVID19) pandemic. Fine-scale evolution of major SARS-CoV-2 lineages and sublineages during four wave-window categories: Pre-Wave 1, Wave 1, Pre-Wave 2, and Wave 2 in four major states of India: Delhi (North), Maharashtra (West), Karnataka (South), and West Bengal (East) was studied using large-scale virus genome sequencing data.

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CONTEXT:The bacterial load in different air samples from environment of most hospitals remained undetermined. Any direct correlation between such bacterial load and the nosocomial infection are also lacking. Only higher bacterial load in air of a particular hospital environment may indicate higher risk of airborne cross infections. AIMS: The study is to determine the bacterial presence per unit volume of air, and the factors influencing the bioload. SETTINGS AND DESIGN: The air samples were collected from different locations of our tertiary care hospital, during Jul 2011 to June 2012 with information like room space per patient, number of daily average visitors, system of air circulation and house-keeping quality. METHODS: A specific volume of air was impacted on a plastic strips containing nutrient agar by air sampler La200, Hi-Media. Following incubation for 24 hour bacterial colonies were counted and organisms were identified up to genus level. RESULTS: Mostly Gram positive cocci followed by Gram positive and a few Gram negative bacilli were detected. The highest bacterial load was found in general outdoor premises (2456 CFU/cm), followed by some extremely crowded general wards (573 CFU/cm). The lowest count of such was found in nursery area (94 CFU/cm), where special emphasis was given on cleanliness, room ventilation and visitor's restriction. Similarly variations in bacterial loads were also noted in different times in a day and in different seasons in a year. The bioload in all tested samples were within permissible limits. CONCLUSIONS: By appropriate measures the aerobic bacterial load in hospital environment can be restricted within optimal level.