Interlaboratory comparison using inactivated SARS-CoV-2 variants as a feasible tool for quality control in COVID-19 wastewater monitoring

Wastewater-based SARS-CoV-2 epidemiology (WBE) has proven as an excellent tool to monitor pandemic dynamics supporting individual testing strategies. WBE can also be used as an early warning system for monitoring the emergence of novel pathogens or viral variants. However, for a timely transmission of results, sophisticated sample logistics and analytics performed in decentralized laboratories close to the sampling sites are required. Since multiple decentralized laboratories commonly use custom in-house workflows for sample purification and PCR-analysis, comparative quality control of the analytical procedures is essential to report reliable and comparable results. In this study, we performed an interlaboratory comparison at laboratories specialized for PCR and high-throughput-sequencing (HTS)-based WBE analysis. Frozen reserve samples from low COVID-19 incidence periods were spiked with different inactivated authentic SARS-CoV-2 variants in graduated concentrations and ratios. Samples were sent to the participating laboratories for analysis using laboratory specific methods and the reported viral genome copy numbers and the detection of viral variants were compared with the expected values. All PCR-laboratories reported SARS-CoV-2 genome copy equivalents (GCE) for all spiked samples with a mean intra- and inter-laboratory variability of 19 % and 104 %, respectively, largely reproducing the spike-in scheme. PCR-based genotyping was, in dependence of the underlying PCR-assay performance, able to predict the relative amount of variant specific substitutions even in samples with low spike-in amount. The identification of variants by HTS, however, required >100 copies/ml wastewater and had limited predictive value when analyzing at a genome coverage below 60 %. This interlaboratory test demonstrates that despite highly heterogeneous isolation and analysis procedures, overall SARS-CoV-2 GCE and mutations were determined accurately. Hence, decentralized SARS-CoV-2 wastewater monitoring is feasible to generate comparable analysis results. However, since not all assays detected the correct variant, prior evaluation of PCR and sequencing workflows as well as sustained quality control such as interlaboratory comparisons are mandatory for correct variant detection.

Background rates for severe cutaneous reactions in the US: Contextual support for safety assessment of COVID-19 vaccines and novel biologics

The global COVID-19 public health crisis has resulted in extraordinary collaboration to expeditiously develop vaccines and therapeutics. The safety of these biologics is closely monitored by the US Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention (CDC). Novel products may have limited safety data, and although serious medical outcomes associated with vaccination are rare, knowledge of background incidence rates of medical conditions in the US population puts reported adverse events (AEs) in perspective for further study. Although relatively minor vaccination skin reactions are common, rare instances of severe delayed hypersensitivity reactions such as erythema multiforme (EM), Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and SJS/TEN overlap syndrome may occur. To aid in the assessment of these events, we performed a literature search in PubMed and Web of Science on the background incidence of EM, SJS, SJS/TEN, and TEN in the US population and on published reports of these conditions occurring post-vaccination. The US background annual incidence rates per million individuals of all ages ranged from 5.3 to 63.0 for SJS, from 0.4 to 5.0 for TEN, and from 0.8 to 1.6 for SJS/TEN. Since these conditions may overlap, some studies reported rates for EM/SJS/TEN combined, however we did not find studies with exclusive EM incidence rates. The published literature, including studies of reports submitted to the FDA/CDC Vaccine Adverse Event Reporting System (VAERS), describes post-vaccination EM, SJS, SJS/TEN and/or TEN as rare occurrences. The vaccines most frequently associated with these conditions were measles, mumps, and rubella; diphtheria, tetanus, and pertussis; and varicella. The majority of VAERS reports of EM, SJS, SJS/TEN, or TEN occurred in children within 30 days of vaccination. This review summarizes background rates of these disorders in the general population and published AEs among vaccine recipients, to support safety surveillance of COVID-19 vaccines and other biologics.

One health system supporting surveillance during COVID-19 epidemic in Abruzzo region, southern Italy

The Istituti Zooprofilattici Sperimentali (IZSs) are public health institutes dealing with the aetiology and pathogenesis of infectious diseases of domestic and wild animals. During Coronavirus Disease 2019 epidemic, the Italian Ministry of Health appointed the IZSs to carry out diagnostic tests for the detection of SARS-CoV-2 in human samples. In particular, the IZS of Abruzzo and Molise (IZS-Teramo) was involved in the diagnosis of SARS-CoV-2 through testing nasopharyngeal swabs by Real Time RT-PCR. Activities and infrastructures were reorganised to the new priorities, in a "One Health" framework, based on interdisciplinary, laboratory promptness, accreditation of the test for the detection of the RNA of SARS-CoV-2 in human samples, and management of confidentiality of sensitive data. The laboratory information system - SILAB - was implemented with a One Health module for managing data of human origin, with tools for the automatic registration of information improving the quality of the data. Moreover, the "National Reference Centre for Whole Genome Sequencing of microbial pathogens - database and bioinformatics analysis" - GENPAT - formally established at the IZS-Teramo, developed bioinformatics workflows and IT dashboard with ad hoc surveillance tools to support the metagenomics-based SARS-CoV-2 surveillance, providing molecular sequencing analysis to quickly intercept the variants circulating in the area. This manuscript describes the One Health system developed by adapting and integrating both SILAB and GENPAT tools for supporting surveillance during COVID-19 epidemic in the Abruzzo region, southern Italy. The developed dashboard permits the health authorities to observe the SARS-CoV-2 spread in the region, and by combining spatio-temporal information with metagenomics provides early evidence for the identification of emerging space-time clusters of variants at the municipality level. The implementation of the One Health module was designed to be easily modelled and adapted for the management of other diseases and future hypothetical events of pandemic nature.

Wastewater-based epidemiology for comprehensive community health diagnostics in a national surveillance study: Mining biochemical markers in wastewater

This manuscript showcases results from a large scale and comprehensive wastewater-based epidemiology (WBE) study focussed on multi-biomarker suite analysis of both chemical and biological determinants in 10 cities and towns across England equating to a population of ∼7 million people. Multi-biomarker suite analysis, describing city metabolism, can provide a holistic understanding to encompass all of human, and human-derived, activities of a city in a single model: from lifestyle choices (e.g. caffeine intake, nicotine) through to health status (e.g. prevalence of pathogenic organisms, usage of pharmaceuticals as proxy for non-communicable disease, NCD, conditions or infectious disease status), and exposure to harmful chemicals due to environmental and industrial sources (e.g. pesticide intake via contaminated food and industrial exposure). Population normalised daily loads (PNDLs) of many chemical markers were found, to a large extent, driven by the size of population contributing to wastewater (especially NCDs). However, there are several exceptions providing insights into chemical intake that can inform either disease status in various communities or unintentional exposure to hazardous chemicals: e.g. very high PNDLs of ibuprofen in Hull resulting from its direct disposal (confirmed by ibuprofen/2-hydroxyibuprofen ratios) and bisphenol A (BPA) in Hull, Lancaster and Portsmouth likely related to industrial discharge. An importance for tracking endogenous health markers such as 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA, an oxidative stress marker) as a generic marker of health status in communities was observed due to increased levels of HNE-MA seen at Barnoldswick wastewater treatment plant that coincided with higher-than-average paracetamol usage and SARS-CoV-2 prevalence in this community. PNDLs of virus markers were found to be highly variable. Being very prevalent in communities nationwide during sampling, SARS-CoV-2 presence in wastewater was to a large extent community driven. The same applies to the fecal marker virus, crAssphage, which is very prevalent in urban communities. In contrast, norovirus and enterovirus showed much higher variability in prevalence across all sites investigated, with clear cases of localized outbreaks in some cities while maintaining low prevalence in other locations. In conclusion, this study clearly demonstrates the potential for WBE to provide an integrated assessment of community health which can help target and validate policy interventions aimed at improving public health and wellbeing.

COVID-19 surveillance in Democratic Republic of Congo, Nigeria, Senegal and Uganda: strengths, weaknesses and key Lessons

INTRODUCTION

As part of efforts to rapidly identify and care for individuals with COVID-19, trace and quarantine contacts, and monitor disease trends over time, most African countries implemented interventions to strengthen their existing disease surveillance systems. This research describes the strengths, weaknesses and lessons learnt from the COVID-19 surveillance strategies implemented in four African countries to inform the enhancement of surveillance systems for future epidemics on the continent.

METHODS

The four countries namely the Democratic Republic of Congo (DRC), Nigeria, Senegal, and Uganda, were selected based on their variability in COVID-19 response and representation of Francophone and Anglophone countries. A mixed-methods observational study was conducted including desk review and key informant interviews, to document best practices, gaps, and innovations in surveillance at the national, sub-national, health facilities, and community levels, and these learnings were synthesized across the countries.

RESULTS

Surveillance approaches across countries included - case investigation, contact tracing, community-based, laboratory-based sentinel, serological, telephone hotlines, and genomic sequencing surveillance. As the COVID-19 pandemic progressed, the health systems moved from aggressive testing and contact tracing to detect virus and triage individual contacts into quarantine and confirmed cases, isolation and clinical care. Surveillance, including case definitions, changed from contact tracing of all contacts of confirmed cases to only symptomatic contacts and travelers. All countries reported inadequate staffing, staff capacity gaps and lack of full integration of data sources. All four countries under study improved data management and surveillance capacity by training health workers and increasing resources for laboratories, but the disease burden was under-detected. Decentralizing surveillance to enable swifter implementation of targeted public health measures at the subnational level was a challenge. There were also gaps in genomic and postmortem surveillance including community level sero-prevalence studies, as well as digital technologies to provide more timely and accurate surveillance data.

CONCLUSION

All the four countries demonstrated a prompt public health surveillance response and adopted similar approaches to surveillance with some adaptations as the pandemic progresses. There is need for investments to enhance surveillance approaches and systems including decentralizing surveillance to the subnational and community levels, strengthening capabilities for genomic surveillance and use of digital technologies, among others. Investing in health worker capacity, ensuring data quality and availability and improving ability to transmit surveillance data between and across multiple levels of the health care system is also critical. Countries need to take immediate action in strengthening their surveillance systems to better prepare for the next major disease outbreak and pandemic.

Quantification of SARS-CoV-2 in wastewater samples from hospitals treating COVID-19 patients during the first wave of the pandemic in Brazil

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

Using clinical surveillance software to support effective infection prevention and control for managing COVID-19

The speed, severity and scale of the COVID-19 pandemic challenged infection prevention and control (IPC) efforts at hospitals worldwide. Dorset County Hospital NHS Foundation Trust in Dorchester had an established pandemic plan, which had been developed in response to the swine flu (H1N1) pandemic in 2009. However, the COVID-19 pandemic developed to a level that modern health care had not seen before and it remains the largest challenge for health care to date. This article outlines the experience of a rural district general hospital using digital solutions for infection prevention and control before and during the pandemic. The author will explore the experience of a hospital that implemented specialised clinical surveillance software, how it was applied to management and control of COVID-19 cases, and how the availability of that system allowed for continued focus on controlling other pathogens in the hospital environment, even at the height of the pandemic.

SARS-CoV-2 infection dynamics revealed by wastewater sequencing analysis and deconvolution

The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.

Wastewater surveillance allows early detection of SARS-CoV-2 omicron in North Rhine-Westphalia, Germany

Wastewater-based epidemiology (WBE) has demonstrated its importance to support SARS-CoV-2 epidemiology complementing individual testing strategies. Due to their immune-evasive potential and the resulting significance for public health, close monitoring of SARS-CoV-2 variants of concern (VoC) is required to evaluate the regulation of early local countermeasures. In this study, we demonstrate a rapid workflow for wastewater-based early detection and monitoring of the newly emerging SARS-CoV-2 VoCs Omicron in the end of 2021 at the municipal wastewater treatment plant (WWTP) Emschermuendung (KLEM) in the Federal State of North-Rhine-Westphalia (NRW, Germany). Initially, available primers detecting Omicron-related mutations were rapidly validated in a central laboratory. Subsequently, RT-qPCR analysis of purified SARS-CoV-2 RNA was performed in a decentral PCR laboratory in close proximity to KLEM. This decentralized approach enabled the early detection of K417N present in Omicron in samples collected on 8th December 2021 and the detection of further mutations (N501Y, Δ69/70) in subsequent biweekly sampling campaigns. The presence of Omicron in wastewater was confirmed by next generation sequencing (NGS) in a central laboratory with samples obtained on 14th December 2021. Moreover, the relative increase of the mutant fraction of Omicron was quantitatively monitored over time by dPCR in a central PCR laboratory starting on 12th December 2021 confirming Omicron as the dominant variant by the end of 2021. In conclusions, WBE plays a crucial role in surveillance of SARS-CoV-2 variants and is suitable as an early warning system to identify variant emergence. In particular, the successive workflow using RT-qPCR, RT-dPCR and NGS demonstrates the strength of WBE as a versatile tool to monitor variant spreading.

Assessment of two types of passive sampler for the efficient recovery of SARS-CoV-2 and other viruses from wastewater

Wastewater-based epidemiology (WBE) has proven to be a useful surveillance tool during the ongoing SARS-CoV-2 pandemic, and has driven research into evaluating the most reliable and cost-effective techniques for obtaining a representative sample of wastewater. When liquid samples cannot be taken efficiently, passive sampling approaches have been used, however, insufficient data exists on their usefulness for multi-virus capture and recovery. In this study, we compared the virus-binding capacity of two passive samplers (cotton-based tampons and ion exchange filter papers) in two different water types (deionised water and wastewater). Here we focused on the capture of wastewater-associated viruses including Influenza A and B (Flu-A & B), SARS-CoV-2, human adenovirus (AdV), norovirus GII (NoVGII), measles virus (MeV), pepper mild mottle virus (PMMoV), the faecal marker crAssphage and the process control virus Pseudomonas virus phi6. After deployment, we evaluated four different methods to recover viruses from the passive samplers namely, (i) phosphate buffered saline (PBS) elution followed by polyethylene glycol (PEG) precipitation, (ii) beef extract (BE) elution followed by PEG precipitation, (iii) no-elution into PEG precipitation, and (iv) direct extraction. We found that the tampon-based passive samplers had higher viral recoveries in comparison to the filter paper. Overall, the preferred viral recovery method from the tampon passive samplers was the no-elution/PEG precipitation method. Furthermore, we evidenced that non-enveloped viruses had higher percent recoveries from the passive samplers than enveloped viruses. This is the first study of its kind to assess passive sampler and viral recovery methods amongst a plethora of viruses commonly found in wastewater or used as a viral surrogate in wastewater studies.

An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US

The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments' decision-making.

Reported Exposures Among In-Person Workers With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection in 6 States, September 2020-June 2021

BACKGROUND

Surveillance systems lack detailed occupational exposure information from workers with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The National Institute for Occupational Safety and Health partnered with 6 states to collect information from adults diagnosed with SARS-CoV-2 infection who worked in person (outside the home) in non-healthcare settings during the 2 weeks prior to illness onset.

METHODS

The survey captured demographic, medical, and occupational characteristics and work- and non-work-related risk factors for SARS-CoV-2 infection. Reported close contact with a person known or suspected to have SARS-CoV-2 infection was categorized by setting as exposure at work, exposure outside of work only, or no known exposure/did not know. Frequencies and percentages of exposure types are compared by respondent characteristics and risk factors.

RESULTS

Of 1111 respondents, 19.4% reported exposure at work, 23.4% reported exposure outside of work only, and 57.2% reported no known exposure/did not know. Workers in protective service occupations (48.8%) and public administration industries (35.6%) reported exposure at work most often. More than one third (33.7%) of respondents who experienced close contact with ≥10 coworkers per day and 28.8% of respondents who experienced close contact with ≥10 customers/clients per day reported exposures at work.

CONCLUSIONS

Exposure to occupational SARS-CoV-2 was common among respondents. Examining differences in exposures among different worker groups can help identify populations with the greatest need for prevention interventions. The benefits of recording employment characteristics as standard demographic information will remain relevant as new and reemerging public health issues occur.

Evaluation of ICD-10-CM-based case definitions of ambulatory encounters for COVID-19 among Department of Defense health care beneficiaries

SARS-CoV-2 ICD-10-CM-based case definitions are lacking in the literature. This analysis was conducted to evaluate the performance metrics of 3 COVID-19 case definitions among Department of Defense (DoD) beneficiaries. SARS-CoV-2 tested specimens collected from 1 March 2020 to 28 February 2021 were matched to ambulatory medical encounters (68% match). The COVID-19 case definition (ICD-10-CM: U07.1) had high specificity (99%) and positive predictive value (PPV) (94%) but low to moderate (29%-66%) sensitivity. The COVID-specific case definition (10 additional codes added), had moderate to high specificity (82-93%), moderate sensitivity (65-75%), and low to moderate PPV (23%-77%). The COVID-like illness case definition (19 additional codes added to the COVID-specific definition), had moderate specificity (65%-86%), moderate sensitivity (76%-79%), and low to moderate PPV (15%-62%). Regardless of the case definition, all metrics improved over the surveillance period. The COVID-19 case definition is ideal for studies that need to ensure all cases are true positives. However, for broad surveillance efforts, the COVID-specific case definition may be the best to maximize specificity without a large decrease in sensitivity and PPV.

The Impact of Cocirculating Pathogens on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/Coronavirus Disease 2019 Surveillance: How Concurrent Epidemics May Introduce Bias and Decrease the Observed SARS-CoV-2 Percentage Positivity

BACKGROUND

Circulation of seasonal non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory viruses with syndromic overlap during the coronavirus disease 2019 (COVID-19) pandemic may alter the quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures.

METHODS

Using a multipathogen susceptible-exposed-infectious-recovered (SEIR) transmission model formalizing cocirculation of SARS-CoV-2 and another respiratory virus, we assessed how an outbreak of secondary virus may affect 2 COVID-19 surveillance indicators: testing demand and positivity. Using simulation, we assessed to what extent the use of multiplex polymerase chain reaction tests on a subsample of symptomatic individuals can help correct the observed SARS-CoV-2 percentage positivity and improve surveillance quality.

RESULTS

We find that a non-SARS-CoV-2 epidemic strongly increases SARS-CoV-2 daily testing demand and artificially reduces the observed SARS-CoV-2 percentage positivity for the duration of the outbreak. We estimate that performing 1 multiplex test for every 1000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percentage positivity.

CONCLUSIONS

This study showed that cocirculating respiratory viruses can distort SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex polymerase chain reaction tests, and a low number of samples is sufficient to avoid bias in SARS-CoV-2 surveillance.

Wastewater-based SARS-CoV-2 environmental monitoring for Piedmont, Italy

The experience gained over the last hundred years clearly indicates that two groups of viruses represent the main risk for the development of highly transmissible epidemics and pandemics in the human species: influenza viruses and coronaviruses (CoV). Although the search for viruses with pandemic potential in the environment may have an important predictive and monitoring role, it is still based on empirical methodologies, mostly resulting from the clinic and not fully validated for environmental matrices. As far as the SARS-CoV-2 pandemic, currently underway, is concerned, environmental monitoring activities aiming at checking the presence of SARS-CoV-2 in wastewater can be extremely useful to predict and check the diffusion of the disease. For this reason, the present study aims at evaluating the SARS-CoV-2 diffusion by means of a wastewater-based environmental monitoring developed in Piedmont, N-W Italy, during the second and third pandemic waves. Wastewater sampling strategies, sampling points sample pre-treatments and analytical methods, data processing and standardization, have been developed and discussed to give representative and reliable results. The following outcomes has been highlighted by the present study: i) a strong correlation between SARS-CoV-2 concentration in untreated wastewater and epidemic evolution in the considered areas can be observed as well as a predictive potential that could provide decision-makers with indications to implement effective policies, to mitigate the effects of the ongoing pandemic and to prepare response plans for future pandemics that could certainly arise in the decades to come; ii) moreover, the data at disposal from our monitoring campaign (almost 500 samples analysed in 11 months) confirm that SARS-CoV-2 concentrations in wastewater are strongly variable and site-specific across the region: the highest SARS-CoV-2 concentration values have been found in sewer networks serving the most populated areas of the region; iii) normalization of viral concentrations in wastewater through Pepper Mild Mottle Virus (a specific faecal marker) has been carried out and commented; iv) the study highlights the potential of wastewater treatment plants to degrade the genetic material referable to SARS-CoV-2 as well. In conclusion, the preliminary data reported in the present paper, although they need to be complemented by further studies considering also other geographical regions, are very promising.

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Simplified Point-of-Care Full SARS-CoV-2 Genome Sequencing Using Nanopore Technology

The scale of the ongoing SARS-CoV-2 pandemic warrants the urgent establishment of a global decentralized surveillance system to recognize local outbreaks and the emergence of novel variants of concern. Among available deep-sequencing technologies, nanopore-sequencing could be an important cornerstone, as it is mobile, scalable, and cost-effective. Therefore, streamlined nanopore-sequencing protocols need to be developed and optimized for SARS-CoV-2 variants identification. We adapted and simplified existing workflows using the ‘midnight’ 1200 bp amplicon split primer sets for PCR, which produce tiled overlapping amplicons covering almost the entire SARS-CoV-2 genome. Subsequently, we applied Oxford Nanopore Rapid Barcoding and the portable MinION Mk1C sequencer combined with the interARTIC bioinformatics pipeline. We tested a simplified and less time-consuming workflow using SARS-CoV-2-positive specimens from clinical routine and identified the CT value as a useful pre-analytical parameter, which may help to decrease sequencing failures rates. Complete pipeline duration was approx. 7 h for one specimen and approx. 11 h for 12 multiplexed barcoded specimens. The adapted protocol contains fewer processing steps and can be completely conducted within one working day. Diagnostic CT values deduced from qPCR standardization experiments can act as principal criteria for specimen selection. As a guideline, SARS-CoV-2 genome copy numbers lower than 4 × 10⁶ were associated with a coverage threshold below 20-fold and incompletely assembled SARS-CoV-2 genomes. Thus, based on the described thermocycler/chemistry combination, we recommend CT values of ~26 or lower to achieve full and high-quality SARS-CoV-2 (+)RNA genome coverage.

The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data

BACKGROUND

COVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia.

OBJECTIVE

This study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks.

METHODS

We extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.

RESULTS

Traditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak.

CONCLUSIONS

Relaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.

An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study

Background

COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions.

Objective

This study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.

Methods

The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes.

Results

We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users.

Conclusions

UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.

Recovery of SARS-CoV-2 from Wastewater Using Centrifugal Ultrafiltration

The COVID-19 pandemic is a global crisis and continues to impact communities as the disease spreads. Clinical testing alone provides a snapshot of infected individuals but is costly and difficult to perform logistically across whole populations. The virus which causes COVID-19, SARS-CoV-2, is shed in human feces and urine and can be detected in human waste. SARS-CoV-2 can be shed in high concentrations (>10⁷ genomic copies/mL) due to its ability to replicate in the gastrointestinal tract of humans through attachment to the angiotensin-converting enzyme 2 (ACE-2) receptors there. Monitoring wastewater for SARS-CoV-2, alongside clinical testing, can more accurately represent the spread of disease within a community. This protocol describes a reliable and efficacious method to recover SARS-CoV-2 in wastewater, quantify genomic RNA levels, and evaluate concentration fluctuations over time. Using this protocol, viral levels as low as 10 genomic copies/mL were successfully detected from 30 mL of wastewater in more than seven-hundred samples collected between August 2020 and March 2021. Through the adaptation of traditional enteric virus methods used in food safety research, targets have been reliably detected with no inhibition of detection (RT-qPCR) observed in any sample processed. This protocol is currently used for surveillance of wastewater systems across New Castle County, Delaware.

Early warning of COVID-19 via wastewater-based epidemiology: potential and bottlenecks

An effective early warning tool is of great administrative and social significance to the containment and control of an epidemic. Facing the unprecedented global public health crisis caused by COVID-19, wastewater-based epidemiology (WBE) has been given high expectations as a promising surveillance complement to clinical testing which had been plagued by limited capacity and turnaround time. In particular, recent studies have highlighted the role WBE may play in being a part of the early warning system. In this study, we briefly discussed the basics of the concept, the benefits and critical points of such an application, the challenges faced by the scientific community, the progress made so far, and what awaits to be addressed by future studies to make the concept work. We identified that the shedding dynamics of infected individuals, especially in the form of a mathematical shedding model, and the back-calculation of the number of active shedders from observed viral load are the major bottlenecks of WBE application in the COVID-19 pandemic that deserve more attention, and the sampling strategy (location, timing, and interval) needs to be optimized to fit the purpose and scope of the WBE project.

Combined RT-qPCR and pyrosequencing of a Spike glycoprotein polybasic cleavage motif can uncover pediatric SARS-CoV-2 infections associated with heterogeneous presentation

BACKGROUND

Reverse transcription of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (+)RNA genome and subgenomic RNAs (sgRNAs) and subsequent quantitative polymerase chain reaction (RT-qPCR) is the reliable diagnostic gold standard for COVID-19 diagnosis and the identification of potential spreaders. Apart from clinical relevance and containment, for specific questions, it might be of interest to (re)investigate cases with low SARS-CoV-2 load, where RT-qPCR alone can deliver conflicting results, even though these cases might neither be clinically relevant nor significant for containment measures, because they might probably not be infectious. In order to expand the diagnostic bandwidth for non-routine questions, particularly for the reliable discrimination between negative and false-negative specimens associated with high CT values, we combined the RT-qPCR workflow with subsequent pyrosequencing of a S-gene amplicon. This expansion can help to confirm SARS-CoV-2 infections without the demand of confirmative antibody testing, which requires to summon patients again for blood sampling few to several weeks after symptom onset.

RESULTS

We successfully established a combined RT-qPCR and S-gene pyrosequencing method which can be optionally exploited after routine diagnostics. This allows a reliable interpretation of RT-qPCR results in specimens with relatively low viral loads and close to the detection limits of qPCR. After laboratory implementation, we tested the combined method in a large pediatric cohort from two German medical centers (n=769). Pyrosequencing after RT-qPCR enabled us to uncover 5 previously unrecognized cases of pediatric SARS-CoV-2-associated diseases, mainly exhibiting mild and heterogeneous presentation-apart from a single case of multisystem inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2, who was hospitalized in the course of the study.

CONCLUSIONS

The proposed protocol allows a specific and sensitive confirmation of SARS-CoV-2 infections close to the detection limits of RT-qPCR. The tested biotinylated primers do not negatively affect the RT-qPCR pipeline and thus can be optionally applied to enable deeper inspection of RT-qPCR results by subsequent pyrosequencing. Moreover, due to the incremental transmission of SARS-CoV-2 variants of concern, we note that the used strategy can uncover (Spike) P681H allowing the pre-selection of SARS-CoV-2 B.1.1.7 candidate specimens for deep sequencing.

Evaluation of viral concentration methods for SARS-CoV-2 recovery from wastewaters

Wastewater-based epidemiology (WBE) is a useful tool that has the potential to act as a complementary approach to monitor the presence of SARS-CoV-2 in the community and as an early alarm system for COVID-19 outbreak. Many studies reported low concentrations of SARS-CoV-2 in sewage and also revealed the need for methodological validation for enveloped viruses concentration in wastewater. The aim of this study was to evaluate different methodologies for the concentration of viruses in wastewaters and to select and improve an option that maximizes the recovery of SARS-CoV-2. A total of 11 concentration techniques based on different principles were evaluated: adsorption-elution protocols with negatively charged membranes followed by polyethylene glycol (PEG) precipitation (Methods 1-2), PEG precipitation (Methods 3-7), aluminum polychloride (PAC) flocculation (Method 8), ultrafiltration (Method 9), skim milk flocculation (Method 10) and adsorption-elution with negatively charged membrane followed by ultrafiltration (Method 11). To evaluate the performance of these concentration techniques, feline calicivirus (FCV) was used as a process control in order to avoid the risk associated with handling SARS-CoV-2. Two protocols, one based on PEG precipitation and the other on PAC flocculation, showed high efficiency for FCV recovery from wastewater (62.2% and 45.0%, respectively). These two methods were then tested for the specific recovery of SARS-CoV-2. Both techniques could recover SARS-CoV-2 from wastewater, PAC flocculation showed a lower limit of detection (4.3 × 102 GC/mL) than PEG precipitation (4.3 × 103 GC/mL). This work provides a critical overview of current methods used for virus concentration in wastewaters and the analysis of sensitivity for the specific recovery of SARS-CoV-2 in sewage. The data obtained here highlights the viability of WBE for the surveillance of COVID-19 infections in the community.

Integrating Metamaterial Antenna Node and LiFi for Privacy Preserving Intelligent COVID-19 Hospital Patient Management

Light fidelity (LiFi) and wireless fidelity (WiFi) can be applied with the same network under the different constraints, which is suitable for COVID-19 surveillance in hospitals. The LiFi network is a high-capacity and security platform. A COVID-19 surveillance system using LiFi is proposed, which consists of two switching modes: communication and surveillance. Firstly, the communication targets are to accommodate the electromagnetic interference (EMI) immunity and high-capacity and security data transmission, where secondly the COVID-19 surveillance can be applied. In operation, the up and downlink system uses a metamaterial antenna embedded by Mach Zehnder interferometer (MZI). An antenna consists of silver bars embedded at the microring center with two-phase modulators at its sides. The entangled source namely a dark soliton is applied to form the transmission, where the information security based on quantum cryptography can be managed. By using the suitable parameters, the whispering gallery modes (WGMs) are generated and the up and downlink nodes are formed. The input information is multiplexed with time to form the multiplexed signals, where the big data transmission (40 Pbit s - 1 ) can be employed. By using the surveillance mode, the plasmonic antenna can be applied for temperature and electric force sensors, which can offer the disinfectant spray and temperature sensor for COVID-19 applications. The optimum plasma force sensitivity is 0.16 N kg-1 mW-1. The center frequencies of 191.48 THz and 199.41 THz are obtained for uplink and downlink antennas, respectively. The optimum temperature sensitivity is 0.05 rads-1 °C-1. In conclusion, the novelty of proposed work is that the integrated sensor circuits are employed for COVID-19 surveillance in the hospital. The fuzzy-based system is designed for critical patient monitoring alert using this surveillance and management inside the hospital for COVID-19 patients.