Comprative evaluation of Advanced Oxidation Processes (AOPs) for reducing SARS-CoV-2 viral load from campus sewage water

Although the presence of SARS-CoV-2 fragments in raw sewage water are not much concerning, since it is a new pathogen and its fate in the environment is poorly understood; therefore efforts are needed for their effective removal. In under-developed countries with poor sewersheds and sanitation practices, the raw sewage water might come in contact with rivers and other water bodies and is generally used by the population for various purposes including drinking water. Hence it is important to properly treat sewage water to reduce public health risks, if any. Our study evaluated various advanced oxidation processes (AOPs) for disinfection of SARS-CoV-2 from sewage water collected from the academic institutional residential campus. The present study is the first report showing hydrodynamic cavitation (HC) used to reduce the SARS-CoV-2 viral load from sewage water. Additionally, we have also evaluated hybrid techniques like HC/O3, HC/O3/H2O2, HC/H2O2, O3/UV, UV/H2O2, UV/H2O2/O3, and O3/H2O2 for the minimization of the SARS-CoV-2 viral load from sewage water. The sewage water treatment techniques were evaluated based on its viral concentration-reducing efficiency by comparing it with the same raw sewage water sample. However, ozone alone and its combination with other disinfecting techniques (like HC, UV, and H2O2) showed >95% SARS-CoV-2 specific RNA-reducing efficiency (also known as viral load). The AOPs treated sewage water was subjected to total nucleic acid isolation followed by RT-qPCR for viral load estimation. Interestingly, all sewage water treatment techniques used in this study significantly reduces both the SARS-CoV-2 viral load as well as PMMoV (faecal indicator) load.

A machine learning-based approach to determine infection status in recipients of BBV152 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 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.

High throughput sequencing based detection of SARS-CoV-2 prevailing in wastewater of Pune, West India

Given a large number of SARS-CoV-2 infected individuals, clinical detection has proved challenging. The wastewater-based epidemiological paradigm would cover the clinically escaped asymptomatic individuals owing to the faecal shedding of the virus. We hypothesised using wastewater as a valuable resource for analysing SARS-CoV-2 mutations circulating in the wastewater of Pune region (Maharashtra; India), one of the most affected during the covid-19 pandemic. We conducted a case study in open wastewater drains from December 2020-March 2021 to assess the presence of SARS-CoV-2 nucleic acid and further detect mutations using ARTIC protocol of MinION sequencing. The analysis revealed 108 mutations across six samples categorised into 40 types of mutations. We report the occurrence of mutations associated with B.1.617 lineage in March-2021 samples, simultaneously also reported as a Variant of Concern (VoC) responsible for the rapid increase in infections. The study also revealed four mutations; S:N801, S:C480R, NSP14:C279F and NSP3:L550del not currently reported from wastewater or clinical data in India but reported in the world. Further, a novel mutation NSP13:G206F mapping to NSP13 region was observed from wastewater. Notably, S:P1140del mutation was observed in December 2020 samples while it was reported in February 2021 from clinical data, indicating the instrumentality of wastewater data in early detection. This is the first study in India to conclude that wastewater-based epidemiology to identify mutations associated with SARS-CoV-2 virus from wastewater as an early warning indicator system.

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 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)