Investigation of SARS-CoV-2 faecal shedding in the community: a prospective household cohort study (COVID-LIV) in the UK

Co-infections of SARS-CoV-2 with respiratory syncytial virus and human influenza A in patients with symptoms of COVID-19 in Ghana: A retrospective study

Background

During the COVID-19 pandemic the aetiology of respiratory illnesses were narrowed to SARS-CoV-2. This prevented diagnosis of other pathogens and patients were not notified of the accurate diagnosis of their illnesses when SARS-CoV-2 was absent. It is therefore important to look back and determine what else was present but was missed.

Objective

This retrospective study sought to gain insights into prevalence of respiratory syncytial virus (RSV) and influenza A alongside SARS-CoV-2 in patients who reported with clinical symptoms of respiratory illnesses.

Methods

Samples from patients who had reported of respiratory symptoms were selected at random from a pool. RNA was extracted and RT-PCR was performed for SARS-CoV-2, RSV and Influenza A in parallel. Data on the clinical symptoms was extracted from case-base forms and analysed.

Results

Of the 400 symptomatic samples tested, prevalence of SARS-CoV-2, influenza A and RSV was 20.3 %, 2.0 % and 0.5 % respectively. Only one sample tested positive for SARS-CoV-2 and influenza A. About 77 % of the symptomatic cases did not test positive for any of the three agents. Cough (79 %) was the most common symptom followed by fever and chills, headache, sore throat and runny nose.

Conclusion

The large proportion of symptomatic cases that tested negative for all three respiratory viruses raises a flag and a need for more investigations into the actual burden of respiratory aetiologic agents during the pandemic. With the low levels of co-infections, parallel testing may not be needed however, a strong case for multiplex tests for respiratory agents exists.

Estimating the effective reproduction number of COVID-19 from population-wide wastewater data: An application in Kagawa, Japan

Although epidemiological surveillance of COVID-19 has been gradually downgraded globally, the transmission of COVID-19 continues. It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data. Herein, we propose a comprehensive method for estimating the effective reproduction number using wastewater data. The wastewater virus concentration data, which were collected twice a week, were analyzed using daily COVID-19 incidence data obtained from Takamatsu, Japan between January 2022 and September 2022. We estimated the shedding load distribution (SLD) as a function of time since the date of infection, using a model employing the delay distribution, which is assumed to follow a gamma distribution, multiplied by a scaling factor. We also examined models that accounted for the temporal smoothness of viral load measurement data. The model that smoothed temporal patterns of viral load was the best fit model (WAIC = 2795.8), which yielded a mean estimated distribution of SLD of 3.46 days (95% CrI: 3.01-3.95 days). Using this SLD, we reconstructed the daily incidence, which enabled computation of the effective reproduction number. Using the best fit posterior draws of parameters directly, or as a prior distribution for subsequent analyses, we first used a model that assumed temporal smoothness of viral load concentrations in wastewater, as well as infection counts by date of infection. In the subsequent approach, we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting. Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data. Adding weekly case count data reduced the uncertainty of the effective reproduction number. We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19, and that inferential performance is enhanced when those data are combined with weekly incidence data.

SARS-CoV-2 genomic surveillance in wastewater as a model for monitoring evolution of endemic viruses

As global SARS-CoV-2 burden and testing frequency have decreased, wastewater surveillance has emerged as a key tool to support clinical surveillance efforts. The aims of this study were to identify and characterize SARS-CoV-2 variants in wastewater samples collected from urban centers across South Africa. Here we show that wastewater sequencing analyses are temporally concordant with clinical genomic surveillance and reveal the presence of multiple lineages not detected by clinical surveillance. We show that wastewater genomics can support SARS-CoV-2 epidemiological investigations by reliably recovering the prevalence of local circulating variants, even when clinical samples are not available. Further, we find that analysis of mutations observed in wastewater can provide a signal of upcoming lineage transitions. Our study demonstrates the utility of wastewater genomics to monitor evolution and spread of endemic viruses.

Identification of SARS-CoV-2 variants in wastewater using targeted amplicon sequencing during a low COVID-19 prevalence period in Japan

Wastewater-based epidemiology is expected to be able to identify SARS-CoV-2 variants at an early stage via next-generation sequencing. In the present study, we developed a highly sensitive amplicon sequencing method targeting the spike gene of SARS-CoV-2, which allows for sequencing viral genomes from wastewater containing a low amount of virus. Primers were designed to amplify a relatively long region (599 bp) around the receptor-binding domain in the SARS-CoV-2 spike gene, which could distinguish initial major variants of concern. To validate the methodology, we retrospectively analyzed wastewater samples collected from a septic tank installed in a COVID-19 quarantine facility between October and December 2020. The relative abundance of D614G mutant in SARS-CoV-2 genomes in the facility wastewater increased from 47.5 % to 83.1 % during the study period. The N501Y mutant, which is the characteristic mutation of the Alpha-like strain, was detected from wastewater collected on December 24, 2020, which agreed with the fact that a patient infected with the Alpha-like strain was quarantined in the facility on this date. We then analyzed archived municipal wastewater samples collected between November 2020 and January 2021 that contained low SARS-CoV-2 concentrations ranging from 0.23 to 0.43 copies/qPCR reaction (corresponding to 3.30 to 4.15 log₁₀ copies/L). The targeted amplicon sequencing revealed that the Alpha-like variant with D614G and N501Y mutations was present in municipal wastewater collected on December 4, 2020 and later, suggesting that the variant had already spread in the community before its first clinical confirmation in Japan on December 25, 2020. These results demonstrate that targeted amplicon sequencing of wastewater samples is a powerful surveillance tool applicable to low COVID-19 prevalence periods and may contribute to the early detection of emerging variants.

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.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

SARS-CoV-2 infections in households in a peri-urban community of Lima, Peru: A prospective cohort study

Background

We assessed the prevalence and incidence of SARS‐CoV‐2 infections in a prospective study of households in Lima, Peru.

Methods

Households with a child, a young adult 18–50 years, and an adult age >50 years in peri‐urban Lima were followed with twice‐a‐week household visits during a 2‐month period. Nasal swabs and saliva specimens were collected twice weekly, and nasopharyngeal swabs were collected weekly from each participant, regardless of symptoms. Laboratory‐confirmed SARS‐CoV‐2 infection was defined by two RT‐PCR tests from any of the collected specimens within a week. Blood samples collected at enrollment and end of follow‐up were tested with rapid serological tests. We calculated the prevalence and incidence of laboratory‐confirmed SARS‐CoV‐2 infections.

Results

We enrolled 132 participants from 44 households: 44 children, 44 young adults, and 44 older adults. A total of 13 SARS‐CoV‐2 infections were detected in eight households, for an overall period prevalence of 9.85% (95% confidence interval [CI]: 5.35–16.25). Most (61.54%) infections were symptomatic. Eight of 11 (72.73%) SARS‐CoV‐2 detections corresponded to the Lambda variant. During 218.79 person‐months at risk of follow‐up, there were six new SARS‐CoV‐2 infections detected (2.74 per 100 person‐month, 95% CI: 1.25–6.04). At enrollment, 59 of 128 participants tested had positive SARS‐CoV‐2 IgG serology (46.09%, 95% CI: 37.25–55.12). Five of six new infections occurred among participants with negative baseline serology.

Conclusions

We demonstrated high incidence of SARS‐CoV‐2 infections in households, especially among subjects without evidence of prior infection, most of them not detected by the Ministry of Health system.