SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey 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 sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, 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).

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The Oxford-Astra Zeneca COVID 19 vaccine (AZD1222 or ChAdOx1) is an important part of the global vaccine roll-out against SARS-CoV-2, and a locally manufactured version (Covishield by Serum Institute, Pune, India) is the most commonly used vaccine in India. The vaccination program started in January 2021 and here we report effectiveness of the first dose of Covishield in generating antibody response and its kinetics. We further report differences in the quantitative antibody response amongst individuals who had pre-existing antibodies to SARS CoV2 and those who did not. In a group of 135 healthcare workers administered Covishield, we measured antibodies to SARS-CoV-2 directed against the spike protein (S-antigen) using Elecsys Anti-SARS-CoV-2 S quantitative antibody detection kit (Roche Diagnostics) at days 0, 7, 14, and 28. In 44 subjects (32.5%) who had already developed antibodies to SARS-CoV-2 at day 0 (before immunization), it was observed that antibody response was significantly higher at each time point, with the maximum increase seen between days 0 and 7. In contrast the sero-negative group (n=91) started developing antibody response only after 14 days or later. Three sero-negative individuals did not develop any antibody response even at day 28 of vaccination. It is noted that median antibody response at 28 days in seronegative subjects was similar to that of seropositive subjects at baseline (day 0) and was on a rising trajectory. Our data suggests that ChAdOx1 is highly immunogenic, particularly so where previous SARS CoV2 antibody-response is established. Given the high background seropositivity in India, this may be useful in determining optimal timing of the second dose during mass immunization within the constraints of vaccine supply and administration.

Immunization is expected to confer protection against infection and severe disease for vaccines while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies of an observational cohort of healthcare workers, we show that during a Severe Acute Respiratory Syndrome -Coronavirus 2 Delta-variant outbreak in Delhi, 25.3% (95% Confidence Interval 16.9-35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare workers were infected within less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, Inter Quartile Range 374) and without (342 U/ml, Inter Quartile Range 497), as was the induction of neutralization activity to wildtype. This was not vaccine failure since vaccine effectiveness estimate based on infection rates in an unvaccinated cohort were about 70% and most infections were asymptomatic. We find that while ChAdOx1-nCoV19 vaccination remains effective in preventing severe infections, it is unlikely to be completely able to block transmission and provide herd immunity.