A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

Li, Guangquan; Denise, Hubert; Diggle, Peter J.; Grimsley, Jasmine M. S.; Holmes, Chris; James, Daniel; Jersakova, Radka; Mole, Callum; Nicholson, George; Smith, C.; Richardson, Sylvia; Rowe, William; Rowlingson, Barry; Torabi, Fatemeh; Wade, Matthew J.; Blangiardo, Marta

doi:10.1016/j.envint.2023.107765

Cited by 11 publications

(14 citation statements)

References 40 publications

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“…We might expect model performance in nowcasting local prevalence to improve with the addition of variables which might act as effect modifiers between wastewater viral load and disease prevalence. We included two local population characteristics, % BAME and IMD, which have been found to be associated with prevalence 15 and were used in estimating local wastewater viral load 12 , but did not find any improvement in nowcast performance. Two possible explanations for this are, firstly, that the spatially varying random effects in the model are able to capture variation that would otherwise be attributed to the covariates, secondly, that their effects are adequately captured by their inclusion in the sub-model for wastewater viral load 12 .…”

Section: Regionmentioning

confidence: 99%

“…We included two local population characteristics, % BAME and IMD, which have been found to be associated with prevalence 15 and were used in estimating local wastewater viral load 12 , but did not find any improvement in nowcast performance. Two possible explanations for this are, firstly, that the spatially varying random effects in the model are able to capture variation that would otherwise be attributed to the covariates, secondly, that their effects are adequately captured by their inclusion in the sub-model for wastewater viral load 12 . This suggests that, although these variables might indeed be associated with disease prevalence, when the aim is prediction a parsimonious model that only includes random effects might be preferable.…”

Section: Regionmentioning

confidence: 99%

“…The estimation of viral concentration of SARS-CoV-2 in wastewater is available as the output of the modelling framework presented in 12 which uses weekly measurements from 303 sewage treatment works, obtained from the Environmental Monitoring for Health Protection (EMHP) wastewater surveillance programme 16 . The model evaluates the relationship of measured viral concentration with a set of covariates covering population socio-demographics, land use and genomic information of the virus.…”

Section: Estimates Of the Sars-cov-2 Viral Concentration In Wastewatermentioning

confidence: 99%

“…One exception is Li et al ( 2023) 12 , who used data on SARS-CoV-2 viral load in wastewater across a network of 303 sewage treatment sites in England to obtain probabilistic predictions at high spatial-temporal resolution covering the whole country. Their study also included an exploratory analysis of the relationship between wastewater viral load and disease prevalence and found that these two metrics showed good qualitative agreement, but that the quantitative relationship between them appeared to vary both in space and in time 12 . This implies that wastewater alone cannot be used to estimate prevalence, which was also observed in 13 .…”

Section: Introductionmentioning

confidence: 99%

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Integrating wastewater and randomised prevalence survey data for national COVID surveillance

Li,

Diggle,

Blangiardo

2023

Preprint

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During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. However, all of these studies have been set in a specific city or region; none have attempted to embed wastewater in a country-level surveillance system. In the post-pandemic era, a likely scenario is that prevalence surveys will be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate dis- ease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.

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Section: Regionmentioning

confidence: 99%

Section: Regionmentioning

confidence: 99%

Section: Estimates Of the Sars-cov-2 Viral Concentration In Wastewatermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating wastewater and randomised prevalence survey data for national COVID surveillance

Li,

Diggle,

Blangiardo

2023

Preprint

Self Cite

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“…To mitigate the effects of natural variability, smoothing can be applied over time and space [18,[21][22][23][24]. This approach can, however, have the unintended consequence of distorting some important features of the data, such as inflection points where there is a sudden change in the epidemic trajectory, or a sharp increase in one location that subsequently spreads to the wider region.…”

mentioning

confidence: 99%

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

Colman

Kao

2023

Preprint

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Background: Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods: We introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. Results: The real-time growth rate of the SARS-CoV-2 prevalence is estimated at 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions data. Conclusion: We provide a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.

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Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey

Chen,

Wang,

Kaur

et al. 2024

Epidemics

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A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

Cited by 11 publications

References 40 publications

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey

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