Evidence map›Paper›PMID 38053867›Full record

ReviewHeliyon2023

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models.

Fatemeh Torabi, Guangquan Li, Callum Mole, George Nicholson, Barry Rowlingson, Camila Rangel Smith, Radka Jersakova, Peter J Diggle, Marta Blangiardo

Open access · goldAbstract readReview
In one paragraph

Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 1 country.

Fatemeh TorabiTuring-RSS Health Data Lab, London, UK.
Guangquan LiTuring-RSS Health Data Lab, London, UK.
Callum MoleTuring-RSS Health Data Lab, London, UK.
George NicholsonTuring-RSS Health Data Lab, London, UK.
Barry RowlingsonTuring-RSS Health Data Lab, London, UK.
Camila Rangel SmithTuring-RSS Health Data Lab, London, UK.
Radka JersakovaTuring-RSS Health Data Lab, London, UK.
Peter J DiggleTuring-RSS Health Data Lab, London, UK.
Marta BlangiardoTuring-RSS Health Data Lab, London, UK.
The Alan Turing Institute · GBLancaster University · GBMRC Centre for Environment and HealthNorthumbria University · GBSwansea University · GBUniversity of Oxford · GB

Funding

Medical Research Council MR/S019669/1
6 · The paper itself

Abstract

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.

Indexed as

COVID-19Spatio-temporal statistical modellingWastewater-based epidemiologyWastewater-based surveillance

Identifiers

PMID38053867
PMCPMC10694161
OpenAlexW4388499166

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.