ABSTRACT
Objectives: We sought to analyse the antibiotic susceptibility profiles and molecular epidemiology of MDR clinical Pseudomonas aeruginosa isolates from South India using non-MDR isolates as a reference. Methods: We established a comprehensive clinical strain library consisting of 58 isolates collected from patients across the South Indian state of Kerala from March 2017 to July 2019. The strains were subject to antibiotic susceptibility testing, modified carbapenem inactivation method assay for carbapenemase production, PCR sequencing, comparative sequence analysis and quantitative PCR of MDR determinants associated with antibiotic efflux pump systems, fluoroquinolone resistance and carbapenem resistance. We performed in silico modelling of MDR-specific SNPs. Results: Of our collection of South Indian P. aeruginosa clinical isolates, 74.1% were MDR and 55.8% were resistant to the entire panel of antibiotics tested. All MDR isolates were resistant to levofloxacin and 93% were resistant to meropenem. We identified seven distinct, MDR-specific mutations in nalD, three of which are novel. mexA was significantly overexpressed in strains that were resistant to the entire test antibiotic panel while gyrA and gyrB were overexpressed in MDR isolates. Mutations in fluoroquinolone determinants were significantly associated with MDR phenotype and a novel GyrA Y100C substitution was observed. Carbapenem resistance in MDR isolates was associated with loss-of-function mutations in oprD and high prevalence of NDM (blaNDM-1) within our sample. Conclusions: This study provides insight into MDR mechanisms adopted by P. aeruginosa clinical isolates, which may guide the potential development of therapeutic regimens to improve clinical outcomes.
ABSTRACT
BACKGROUND: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models. METHODS: We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. RESULTS: The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant. CONCLUSION: The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.
ABSTRACT
BACKGROUND: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government. METHODS: Exponential Growth method to estimate basic reproduction rate R0, and Time dependent method to calculate the effective reproduction number (dynamic) were used. "R0" package in R software was used to estimate these statistics. RESULTS: The basic reproduction number (R0) for India was estimated at 1.379 (95% CI: 1.375, 1.384). This was 1.450 (1.441, 1.460) for Maharashtra, 1.444 (1.430, 1.460) for Gujarat, 1.297 (1.284, 1.310) for Delhi and 1.405 (1.389, 1.421) for Tamil Nadu. In India, the R at the first week from March 2-8, 2020 was 3.2. It remained around 2 units for three weeks, from March 9-29, 2020. After March 2020, it started declining and reached around 1.3 in the following week suggesting a stabilisation of the transmissibility rate. CONCLUSION: The study estimated a baseline R0 of 1.379 for India. It also showed that the R was getting stabilised from first week of April (with an average R of 1.29), despite the increase in March. This suggested that in due course there will be a reversal of epidemic. However, these analyses should be revised periodically.
ABSTRACT
BACKGROUND: Global research is running towards to find a vaccine to stop the threat of the COVID-19. The Bacillus Calmette-Guérin (BCG) vaccine that prevents severe forms of tuberculosis is getting more attention in this scenario. The objective of our study was to determine the association between BCG vaccine coverage and incidence of COVID-19 at a national-level across the Globe. METHODS: The data of 160 countries were included in the study. Meta-regression was done to estimate the difference in the incidence of COVID-19 cases between countries with BCG vaccination coverage. BCG coverage was categorized as ≤70%, >70% and no vaccination. The analyses were carried out by adjusting for factors such as population density, income group, latitude, and percentage of the total population under age groups 15-64 and above 65 years of each country. RESULTS: The countries that had ≤70% coverage of BCG vaccine reported 6.5 (95% CI: -8.4 to -4.5) less COVID-19 infections per 10,000 population as compared to countries that reported no coverage. Those that had >70% coverage reported 10.1 (95% CI: -11.4 to -8.7) less infections per 10,000 population compared to those with no BCG countries. CONCLUSION: Our analysis suggests that BCG is associated with reduced COVID-19 infections if the BCG vaccine coverage is over 70%. The region-wise analyses also suggested similar findings, except the Middle East and North African region.
