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Wastewater surveillance to infer COVID-19 transmission: A systematic review.

ABSTRACT: Successful detection of SARS-COV-2 in wastewater suggests the potential utility of wastewater-based epidemiology (WBE) for COVID-19 community surveillance. This systematic review aims to assess the performance of wastewater surveillance as early warning system of COVID-19 community transmission. A systematic search was conducted in PubMed, Medline, Embase and the WBE Consortium Registry according to PRISMA guidelines for relevant articles published until 31st July 2021. Relevant data were extracted and summarized. Quality of each paper was assessed using an assessment tool adapted from Bilotta et al.'s tool for environmental science. Of 763 studies identified, 92 studies distributed across 34 countries were shortlisted for qualitative synthesis. A total of 26,197 samples were collected between January 2020 and May 2021 from various locations serving population ranging from 321 to 11,400,000 inhabitants. Overall sample positivity was moderate at 29.2% in all examined settings with the spike (S) gene having maximum rate of positive detections and nucleocapsid (N) gene being the most targeted. Wastewater signals preceded confirmed cases by up to 63 days, with 13 studies reporting sample positivity before the first cases were detected in the community. At least 50 studies reported an association of viral load with community cases. While wastewater surveillance cannot replace large-scale diagnostic testing, it can complement clinical surveillance by providing early signs of potential transmission for more active public health responses. However, more studies using standardized and validated methods are required along with risk analysis and modelling to understand the dynamics of viral outbreaks.

SUBMITTER: Shah S

PROVIDER: S-EPMC8423771 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Project description:BackgroundIn November 2020, the World Health Organization (WHO) created interim guidance on how to integrate testing for SARS-CoV-2 into existing influenza surveillance systems. Influenza-like illness (ILI) and severe acute respiratory illness (SARI) case definitions have been used to specify the case definition of COVID-19 for surveillance purposes. This review aims to assess whether the common clinical features of COVID-19 have changed to the point that the criteria used to identify both COVID-19 and influenza in surveillance programs needs to be altered.MethodsA systematic review of reviews following PRISMA-P guidelines was conducted using the "COVID-19 evidence review" database from August 19, 2020, to August 19, 2021. Reviews providing pooled estimates of the prevalence of clinical features of COVID-19 within the general population, diagnosed by polymerase chain reaction or rapid diagnostic test, were included. These were critically appraised and sensitivity analysis was undertaken to examine potential causes of bias.ResultsFourteen reviews were identified, including three on adults only and three on children only. For all reviews, combined fever (median prevalence = 73.0%, IQR = 58.3-78.7) and cough (45.1%, IQR = 28.9-54.0) were the most common features. These were followed by loss of taste or smell (45.1%, IQR = 28.9-54.0), hypoxemia (33%, one review), fatigue (26.4%, IQR = 9.0-39.4) and expectoration (23.9%, IQR = 23.3-25.5). Fever and cough continued to be the most prevalent features for adults and children, with subsequent symptoms being similar for adults only. However, the pattern differed for children, with headache (34.3%, IQR = 18-50.7) and nasal congestion (20%, one review) being the third and fourth commonest symptoms.ConclusionsThe prevalent features found in this recent review were the same as the ones identified at the beginning of the pandemic. Therefore, the current approach of using the ILI and SARI criteria which incorporate fever and cough will identify COVID-19 cases in addition to influenza. Interestingly, children may present with different features, as headaches and nasal congestion were more common in this group. Future research could examine this further and investigate whether symptomology changes with new variants of COVID-19.

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Wastewater surveillance to infer COVID-19 transmission: A systematic review.

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