A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

A detailed investigation of N95 respirator sterilization with dry heat, hydrogen peroxide, and ionizing radiation

In the present ongoing pandemic, the N95 respirator is an essential protective barrier to suppress the spread of the SARS-Cov-2 virus and protect the frontline worker from exposure. The N95 respirators are meant for single usage;however, they can be used after sterilization, considering the economy and shortfall in availability. At this juncture, the performance of the respirator after various types of sterilization and usage condition is required to be analyzed in detail. With this motto, this work has proceeded. The respirator?s filtration efficiency (FE), breathing resistance, and quality factor are evaluated for two face velocities (5.8 ± 0.2 and 26.4 ± 0.9 cm/s). Sterilization techniques used here are dry air oven heating at 70?80 ± 3°C for 30 and 60 min, gamma irradiation for cumulative dose 15 and 25 kGy, and soaking in liquid hydrogen peroxide for 30 min. The filtration performance and electrostatic surface charge density are used to determine the facemask?s efficacy after sterilization. The respirator?s physical, chemical, and morphological degradation were investigated using materials area density, microscopic analyses, FTIR, Raman spectroscopy, EPR, and TGA analyses. The highest reduction in filtration efficiency is 29.36 ± 0.49?36.08 ± 1.78% after irradiation due to a reduction in the charge density (71?133%) of the respirator layers. However, the FE does not reduce significantly (0.39 ± 0.52 to ?2.46 ± 0.60) for dry air heat and H2O2 sterilization despite a change in charge density (0.4?53%), but there is no direct correlation with FE. Electrostatic charge measurement of the filtration layer is a crucial indicator of FE degradation. Hence, dry air heat and H2O2 soaking are found to be the most suitable sterilization methods. No significant degradation was observed on the physical, chemical, and morphological properties of respirators layers after sterilization.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).