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.
Subject(s)
COVID-19 , Breakthrough PainABSTRACT
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.
Subject(s)
COVID-19 , Taste Disorders , ConfusionABSTRACT
In April 2021, after successfully enduring three waves of the SARS-CoV2 pandemic in 2020, and having reached population seropositivity of about 50%, Delhi, the national capital of India was overwhelmed by the fourth wave. Here, we trace viral, host, and social factors contributing to the scale and exponent of the fourth wave, when compared to preceding waves, in an epidemiological context. Genomic surveillance data from Delhi and surrounding states shows an early phase of the upsurge driven by the entry of the more transmissible B.1.1.7 variant of concern (VOC) into the region in January, with at least one B.1.1.7 super spreader event in February 2021, relatable to known mass gatherings over this period. This was followed by seeding of the B.1.617 VOC, which too is highly transmissible, with rapid expansion of B.1.617.2 sub-lineage outpacing all other lineages. This unprecedented growth of cases occurred in the background of high seropositivity, but with low median neutralizing antibody levels, in a serially sampled cohort. Vaccination breakthrough cases over this period were noted, disproportionately related to VOC in sequenced cases, but usually mild. We find that this surge of SARS-CoV2 infections in Delhi is best explained by the introduction of a new highly transmissible VOC, B.1.617.2, with likely immune-evasion properties; insufficient neutralizing immunity, despite high seropositivity; and social behavior that promoted transmission.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
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
BackgroundIndia has been amongst the most affected nations during the SARS-CoV2 pandemic, with sparse data on country-wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS-CoV2 sero-positivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. MethodsCouncil of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi-urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecsys SARS CoV2 specific antibody kit and GENScript cPass SARS-CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings1058 individuals (10{middle dot}14%) had antibodies against SARS-CoV2. A follow-up on 346 sero-positive individuals after three months revealed stable to higher antibody levels against SARS-CoV2 but declining plasma activity for neutralizing SARS-CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of sero-positive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one-fourth reporting loss of taste or smell. Significantly associated risks for sero-positivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1{middle dot}79, 1{middle dot}43 - 2{middle dot}24, 2{middle dot}81561E-06), occupational responsibilities such as security, housekeeping personnel etc. (2{middle dot}23, 1{middle dot}92 - 2{middle dot}59, 6{middle dot}43969E-26), non-smokers (1{middle dot}52, 1{middle dot}16 - 1{middle dot}99, 0{middle dot}02) and non-vegetarianism (1{middle dot}67, 1{middle dot}41 - 1{middle dot}99, 3{middle dot}03821E-08). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for sero-positivity was noted with specific ABO blood groups -O was associated with a lower risk. InterpretationIn a first-of-its-kind study from India, we report the sero-positivity in a country-wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much-sought-for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. FundingCouncil of Scientific and Industrial Research, India (CSIR)
Subject(s)
FeverABSTRACT
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.
Subject(s)
COVID-19 , PneumoniaABSTRACT
In the last few months, there has been a global catastrophic outbreak of severe acute respiratory syndrome disease caused by the novel corona virus SARS-CoV-2 affecting millions of people worldwide. Early diagnosis and isolation is key to contain the rapid spread of the virus. Towards this goal, we report a simple, sensitive and rapid method to detect the virus using a targeted mass spectrometric approach, which can directly detect the presence of virus from naso-oropharyngeal swabs. Using a multiple reaction monitoring we can detect the presence of two peptides specific to SARS-CoV-2 in a 2.3 minute gradient run with 100% specificity and 90.4 % sensitivity when compared to RT-PCR. Importantly, we further show that these peptides could be detected even in the patients who have recovered from the symptoms and have tested negative for the virus by RT-PCR highlighting the sensitivity of the technique. This method has the translational potential of in terms of the rapid diagnostics of symptomatic and asymptomatic COVID-19 and can augment current methods available for diagnosis of SARS-CoV-2.
Subject(s)
COVID-19 , Severe Acute Respiratory SyndromeABSTRACT
India first detected SARS-CoV-2, causal agent of COVID-19 in late January-2020, imported from Wuhan, China. March-2020 onwards; importation of cases from rest of the countries followed by seeding of local transmission triggered further outbreaks in India. We used ARTIC protocol based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT from Oxford Nanopore Technology to understand introduction and local transmission. The analyses revealed multiple introductions of SARS-CoV-2 from Europe and Asia following local transmission. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, here, proposed as A4-clade based on its divergence within A-cluster. The viral haplotypes may link their persistence to geo-climatic conditions and host response. Despite the effectiveness of non-therapeutic interventions in India, multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.