Assessing SARS-CoV-2 Testing Adherence in a University Town: Recurrent Event Modeling Analysis.

BACKGROUND: Healthy Davis Together was a program launched in September 2020 in the city of Davis, California, to mitigate the spread of COVID-19 and facilitate the return to normalcy. The program involved multiple interventions, including free saliva-based asymptomatic testing, targeted communication campaigns, education efforts, and distribution of personal protective equipment, community partnerships, and investments in the local economy. OBJECTIVE: This study identified demographic characteristics of individuals that underwent testing and assessed adherence to testing over time in a community pandemic-response program launched in a college town in California, United States. METHODS: This study outlines overall testing engagement, identifies demographic characteristics of participants, and evaluates testing participation changes over 4 periods of the COVID-19 pandemic, distinguished by the dominant variants Delta and Omicron. Additionally, a recurrent model is employed to explore testing patterns based on the participants' frequency, timing, and demographic characteristics. RESULTS: A total of 770,165 tests were performed between November 18, 2020, and June 30, 2022, among 89,924 (41.1% of total population) residents of Yolo County, with significant participation from racially or ethnically diverse participants and across age groups. Most positive cases (6351 of total) and highest daily participation (895 per 100,000 population) were during the Omicron period. There were some gender and age-related differences in the pattern of recurrent COVID-19 testing. Men were slightly less likely (hazard ratio [HR] 0.969, 95% CI 0.943-0.996) to be retested and more likely (HR 1.104, 95% CI 1.075-1.134) to stop testing altogether than women. People aged between 20 and 34 years were less likely to be retested (HR 0.861, 95% CI 0.828-0.895) and more likely to stop testing altogether (HR 2.617, 95% CI 2.538-2.699). However, older age groups were less likely to stop testing, especially those aged between 65-74 years and 75-84 years, than those aged between 0 and 19 years. The likelihood of stopping testing was lower (HR 0.93, 95% CI 0.889-0.976) for the Asian group and higher for the Hispanic or Latino (HR 1.185, 95% CI 1.148-1.223) and Black or African American (HR 1.198, 95% CI 1.054-1.350) groups than the White group. CONCLUSIONS: The unique features of a pandemic response program that supported community-wide access to free asymptomatic testing provide a unique opportunity to evaluate adherence to testing recommendations and testing trends over time. Identification of individual and group-level factors associated with testing behaviors can provide insights for identifying potential areas of improvement in future testing initiatives.

Pooling of samples to optimise SARS-CoV-2 detection in nasopharyngeal swabs and gargle lavage self-samples for covid-19 diagnostics and surveillance.

BACKGROUND: Testing of pooled samples is an effective strategy for increasing testing capacity while saving resources and time. This study aimed to validate pooled testing and gather real-life data on its use for Covid-19 surveillance with a gargle lavage (GL) self-sampling strategy. METHODS: Two-stage pooled testing with pools of 6 and 12 samples was used for preventive testing of an asymptomatic population and Covid-19 surveillance in Czech schools. Both GL and nasopharyngeal swabs were used for sampling. RESULTS: In total, 61,111 samples were tested. The use of pooled testing for large-scale Covid-19 surveillance reduced consumable costs by almost 75% and increased testing capacity up to 3.8-fold compared to standard methods. RT-PCR experiments revealed a minimal loss of sensitivity (0-2.2%) when using pooled samples, enabling the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genes with Ct values >35. The minor loss of sensitivity was counterbalanced by a significantly increased throughput and the ability to substantially increase testing frequencies. CONCLUSIONS: Pooled testing is considerably more cost-effective and less time-consuming than standard testing for large-scale Covid-19 surveillance even when the prevalence of SARS-CoV-2 is fluctuating. Gargle lavage self-sampling is a non-invasive technique suitable for sample collection without a healthcare worker's assistance.

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.

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.

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.

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.

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.

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.

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.

COVID-19 smart surveillance: Examination of Knowledge of Apps and mobile thermometer detectors (MTDs) in a high-risk society.

Background: Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring. Materials and Methods: In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making. Results: The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze. Conclusion: The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.

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.

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.

Early Detection of SARS-CoV-2 Omicron BA.4 and BA.5 in German Wastewater.

Wastewater-based SARS-CoV-2 epidemiology (WBE) has been established as an important tool to support individual testing strategies. The Omicron sub-variants BA.4/BA.5 have spread globally, displacing the preceding variants. Due to the severe transmissibility and immune escape potential of BA.4/BA.5, early monitoring was required to assess and implement countermeasures in time. In this study, we monitored the prevalence of SARS-CoV-2 BA.4/BA.5 at six municipal wastewater treatment plants (WWTPs) in the Federal State of North Rhine-Westphalia (NRW, Germany) in May and June 2022. Initially, L452R-specific primers/probes originally designed for SARS-CoV-2 Delta detection were validated using inactivated authentic viruses and evaluated for their suitability for detecting BA.4/BA.5. Subsequently, the assay was used for RT-qPCR analysis of RNA purified from wastewater obtained twice a week at six WWTPs. The occurrence of L452R carrying RNA was detected in early May 2022, and the presence of BA.4/BA.5 was confirmed by variant-specific single nucleotide polymorphism PCR (SNP-PCR) targeting E484A/F486V and NGS sequencing. Finally, the mutant fractions were quantitatively monitored by digital PCR, confirming BA.4/BA.5 as the majority variant by 5 June 2022. In conclusion, the successive workflow using RT-qPCR, variant-specific SNP-PCR, and RT-dPCR demonstrates the strength of WBE as a versatile tool to rapidly monitor variants spreading independently of individual test capacities.

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.

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.

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.

[Impact of the COVID-19 epidemic on total and cause-specific mortality in Rome (Italy) in 2020]. / Impatto dell'epidemia di COVID-19 sulla mortalità totale e per causa a Roma nel 2020.

OBJECTIVES: to estimate the impact of the COVID-19 epidemic on total and cause-specific mortality in people residing and dead in the Municipality of Rome (Italy) in 2020, and to describe the causes of death of subjects with SARS-CoV-2 infection confirmed by molecular test. DESIGN: descriptive analysis of total and cause-specific mortality in 2020 in Rome and comparison with a reference period (2015-2018 for total mortality and 2018 for cause-specific mortality); descriptive analysis of cause-specific mortality in the cohort of SARS-CoV-2 infected subjects. SETTING AND PARTICIPANTS: 27,471 deaths registered in the Lazio mortality-cause Registry, relating to people residing and died in the municipality of Rome in 2020, 2,374 of which died from COVID-19.MAIN OUCOME MEASURES: all-cause mortality by month, gender, age group and place of death, cause-specific mortality (ICD-10 codes). RESULTS: in the municipality of Rome in 2020, an excess of mortality from all causes equal to +10% was observed, with a greater increase in the months of October-December (+27%, +56%, and +26%, respectively) in people aged 50+, with the greatest contribution from the oldest age groups (80+) who died in the nursing homes or at home. Lower mortality was observed in the age groups 0-29 years (-30%) and 40-49 years (-13%). In 2020, COVID-19 represents the fourth cause of death in Rome after malignant tumours, diseases of the circulatory system, and respiratory diseases. Excess mortality was observed from stroke and pneumonia (both in men and women), from respiratory diseases (in men), from diabetes, mental disorders, dementia and Parkinson's disease (in women). On the contrary, mortality is lower for all cancers, for diseases of the blood and haematopoietic organs and for the causes of the circulatory system. The follow-up analysis of SARS-CoV-2 positive subjects residing in Rome shows that a share of deaths (about 20%) reports other causes of death such as cardiovascular diseases, malignant tumours, and diseases of the respiratory system on the certificate collected by the Italian National Statistics Institute. CONCLUSIONS: the 2020 mortality study highlighted excesses for acute and chronic pathologies, indicative of possible delays in the diagnosis or treatment of conditions indirectly caused by the pandemic, but also a share of misclassification of the cause of death that is recognized as COVID-19 death.

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.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.