ABSTRACT
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in 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.
ABSTRACT
It has been established that smell and taste loss are frequent symptoms during COVID-19 onset. Most evidence stems from medical exams or self-reports. The latter is particularly confounded by the common confusion of smell and taste. Here, we tested whether practical smelling and tasting with household items can be used to assess smell and taste loss. We conducted an online survey and asked participants to use common household items to perform a smell and taste test. We also acquired generic information on demographics, health issues including COVID-19 diagnosis, and current symptoms. We developed several machine learning models to predict COVID-19 diagnosis. We found that the random forest classifier consistently performed better than other models like support vector machines or logistic regression. The smell and taste perception of self-administered household items were statistically different for COVID-19 positive and negative participants. The most frequently selected items that also discriminated between COVID-19 positive and negative participants were clove, coriander seeds, and coffee for smell and salt, lemon juice, and chillies for taste. Our study shows that the results of smelling and tasting household items can be used to predict COVID-19 illness and highlight the potential of a simple home-test to help identify the infection and prevent the spread.
ABSTRACT
The risk for community acquired pneumonia (CAP) is partially driven by genetics. To identify the CAP-associated genetic risk loci, we performed a meta-analysis of clinically diagnosed CAP (3,310 individuals) with 2,655 healthy controls. The findings revealed CYP1A1 variants (rs2606345, rs4646903, rs1048943) associated with pneumonia. We observed rs2606345 [G vs T; OR=1.49(1.29-1.69); p=0.0001; I2= 15.5%], and rs1048943 [T vs G; OR= 1.31(0.90-1.71); p=0.002; I2=19.3%] as risk markers and rs4646903 [T vs C; OR= 0.79(0.62-0.96); p=0.03; I2=0%] as a protective marker for susceptibility to CAP, when compared with healthy controls. Our meta-analysis showed the presence of CYP1A1 SNP alleles contributing significant risk toward pneumonia susceptibility. Interestingly, we observed a striking difference of allele frequency for rs2606345 (CYP1A1) among Europeans, Africans and Asians which may provide a possible link for observed variations in death due to coronavirus disease 2019 (COVID-19), a viral pneumonia. We report, for the first time, a significant positive correlation for the risk allele (T or A) of rs2606345, with a higher COVID-19 mortality rate worldwide and within a genetically heterogeneous nation like India. Mechanistically, the risk allele A (rs2606345) is associated with lower expression of CYP1A1 and presumably leads to reduced capacity for xenobiotic detoxification. We note that ambient air pollution, a powerful inducer of CYP1A1 gene expression, is globally associated with lower, not higher mortality, as would normally be predicted. In conclusion, we find that CYP1A1 alleles are associated with CAP mortality, presumably via altered xenobiotic metabolism. We speculate that gene-environment interactions governing CYP1A1 expression may influence COVID-19 mortality.
ABSTRACT
Delhi, the national capital of India, has experienced multiple SARS-CoV-2 outbreaks in 2020 and reached a population seropositivity of over 50% by 2021. During April 2021, the city became overwhelmed by COVID-19 cases and fatalities, as a new variant B.1.617.2 (Delta) replaced B.1.1.7 (Alpha). A Bayesian model explains the growth advantage of Delta through a combination of increased transmissibility and partial reduction of immunity elicited by prior infection (median estimates; x1.5-fold, 20% reduction). Seropositivity of an employee and family cohort increased from 42% to 86% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi. One-Sentence SummaryDelhi experienced an overwhelming surge of COVID-19 cases and fatalities peaking in May 2021 as the highly transmissible and immune evasive Delta variant replaced the Alpha variant.
ABSTRACT
Immunization is expected to confer protection against infection and severe disease for vaccinees, while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies, we show that during a severe SARS-CoV2 Delta-variant outbreak in Delhi, 25.3% (95% CI 16.9 - 35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare-workers (HCW) were infected within a period of less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, IQR 374) or not (342 U/ml, IQR 497), as was induction of neutralization activity to wildtype. Most infections were unrecognized. The Delta-variant thus causes frequent unrecognized breakthrough infections in adequately immunized subjects, reducing any herd-effect of immunity, and requiring reinstatement of preventive measures such as masking.
ABSTRACT
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% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. 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{middle dot}23, 1{middle dot}92-2{middle dot}59, 6{middle dot}5E-26), use of public transport (1{middle dot}79, 1{middle dot}43-2{middle dot}24, 2{middle dot}8E-06), not smoking (1{middle dot}52, 1{middle dot}16-1{middle dot}99, 0{middle dot}02), non-vegetarian diet (1{middle dot}67, 1{middle dot}41-1{middle dot}99, 3{middle dot}0E-08), and B blood group (1{middle dot}36,1{middle dot}15-1{middle dot}61, 0{middle dot}001). Impact StatementWidespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. FundingCouncil of Scientific and Industrial Research, India (CSIR)
ABSTRACT
The coronavirus disease of 2019 (COVID-19) pandemic exposed a limitation of artificial intelligence (AI) based medical image interpretation systems. Early in the pandemic, when need was greatest, the absence of sufficient training data prevented effective deep learning (DL) solutions. Even now, there is a need for Chest-X-ray (CxR) screening tools in low and middle income countries (LMIC), when RT-PCR is delayed, to exclude COVID-19 pneumonia (Cov-Pneum) requiring transfer to higher care. In absence of local LMIC data and poor portability of CxR DL algorithms, a new approach is needed. Axiomatically, it is faster to repurpose existing data than to generate new datasets. Here, we describe CovBaseAI, an explainable tool which uses an ensemble of three DL models and an expert decision system (EDS) for Cov-Pneum diagnosis, trained entirely on datasets from the pre-COVID-19 period. Portability, performance, and explainability of CovBaseAI was primarily validated on two independent datasets. First, 1401 randomly selected CxR from an Indian quarantine-center to assess effectiveness in excluding radiologic Cov-Pneum that may require higher care. Second, a curated dataset with 434 RT-PCR positive cases of varying levels of severity and 471 historical scans containing normal studies and non-COVID pathologies, to assess performance in advanced medical settings. CovBaseAI had accuracy of 87% with negative predictive value of 98% in the quarantine-center data for Cov-Pneum. However, sensitivity varied from 0.66 to 0.90 depending on whether RT-PCR or radiologist opinion was set as ground truth. This tool with explainability feature has better performance than publicly available algorithms trained on COVID-19 data but needs further improvement.