Model-Based Assessment of COVID-19 Epidemic Dynamics by Wastewater Analysis

Proverbio, Daniele; Kemp, Françoise; Magni, Stefano; Ogorzaly, Leslie; Cauchie, Henry-Michel; Skupin, Alexander; Aalto, Atte

doi:10.2139/ssrn.4012473

Cited by 7 publications

(10 citation statements)

References 56 publications

(72 reference statements)

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“…Two general methods have been reported to connect viral concentration in wastewater to epidemic model. (A) is representative of Brouwer et al and Nourbakhsh et al (Brouwer et al, 2022; Nourbakhsh et al, 2022), (B) is representative of Proverbio et al (Proverbio et al, 2022), and (C) is the proposed model framework.…”

Section: Supplementary Figuresmentioning

confidence: 99%

“…Few recent studies have tried to build classical susceptible-infected-removed (SIR)-type models to bridge the measured viral concentration and reported case number. For example, Proverbio et al (2022) added a variable that keeps track of actively shedding individuals in a stochastic susceptible-exposed-infectious-recovered (SEIR) model and used a constant viral shedding rate to connect the number of infected cases to viral concentration in wastewater (Proverbio et al, 2022). Conversely, Brouwer et al (2022) accounted for time dependent viral shedding rates by incorporating multiple subclasses with different shedding rates within each infected stage of the model to better predict viral concentrations and reported cases (Brouwer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Phan

Brozak

Pell

et al. 2022

Preprint

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Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 – Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds (R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

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Section: Supplementary Figuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Phan

Brozak

Pell

et al. 2022

Preprint

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show abstract

“…Backward and forward recursion approximates a state that corresponds to the signal [107]. The Kalman Filter is a popular recursion method for implementing state-space models [121,[134][135][136][137][138][139].…”

Section: Introductionmentioning

confidence: 99%

“…The observation (measurement) equation’s independent variable is the signal. In infectious epidemiology; reported new cases, disease mortality [120], waste water serology [121] and combinations thereof are examples of signals. The signal can be a proxy [122, 123] that can be affected by both testing strategy and implementation [120].…”

Section: Introductionmentioning

confidence: 99%

Basic reproduction number projection for novel pandemics and variants. Ancestral SARS-CoV2 R0 projection

Benjamin¹

2022

Preprint

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The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic epidemic compartment model and an alternative to the Susceptible-Infected-Removed (SIR) model prototype. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and some common host behavioural characteristics. The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number ($\mathcal{R}_0$) for the United Kingdom (UK) could have been projected on the individual states of the United States of America (USA) prior to being detected in the USA. The Imperial College London (ICL) R0 estimate for the UK is projected onto each USA state. The difference between these projections and ICL estimates for USA states is either not statistically significant on the paired student t-test or epidemiologically insignificant. Projection provides a baseline for evaluating the real-time impact of an intervention. Sensitivity analysis was conducted because of considerable variance in parameter estimates across studies. Although the HI-STR predicts that increasing symptomatic ratio and inherently immune ratio reduce R0, relative to the uncertainty in the estimates of R0 for the ancestral SARS-CoV2, the projection is insensitive to the inherently immune ratio and the symptomatic ratio.

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“…The predictive capacity of WBE has been already explored through different modelling approaches. Compartmental SEIR-type models (S standing for Susceptible, E for Exposed, I for Infectious and R for Recovered) have been adapted to include wastewater RNA determinations 28,29 . These models allow for an extremely informative mechanistic approach; nevertheless, they entail the determination of several parameters that make their generalisation difficult to other sites.…”

mentioning

confidence: 99%

Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia

Joseph-Duran

Serra-Compte

Sarrias

et al. 2022

Sci Rep

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While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.

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Model-Based Assessment of COVID-19 Epidemic Dynamics by Wastewater Analysis

Cited by 7 publications

References 56 publications

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Basic reproduction number projection for novel pandemics and variants. Ancestral SARS-CoV2 R0 projection

Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia

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