SynthesisThe Science of the total environment2021
Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.
Synthesis in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 4 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling.The Science of the total environment · 2023Pooled it
- Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis.Journal of hazardous materials · 2023Pooled it
- SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology.Journal of hazardous materials · 2022Pooled it
- SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation.Environmental research · 2022Pooled it
- Advances in Biosensor Technology for Illicit Drug Detection Enable Effective Wastewater Surveillance.Chem & bio engineering · 2026Review
- The potential of long-term wastewater-based surveillance to predict COVID-19 waves peak in Mexico.Water environment research : a research publication of the Water Environment Federation · 2025Article
- The influence of environmental factors on the detection and quantification of SARS-CoV-2 variants in dormitory wastewater at a primarily undergraduate institution.Microbiology spectrum · 2025Article
- Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China.BMC public health · 2025Article
- Article
- Morbidity of SARS-CoV-2 in the evolution to endemicity and in comparison with influenza.Communications medicine · 2024Article
- Article
- Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review.Pathogens (Basel, Switzerland) · 2024Review
- Estimating actual SARS-CoV-2 infections from secondary data.Scientific reports · 2024Article
- Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Article
- Article
- SARS-CoV-2 detection and inactivation in water and wastewater: review on analytical methods, limitations and future research recommendations.Emerging microbes & infections · 2023Review
- Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models.Scientific reports · 2023Article
- Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models.Heliyon · 2023Review
- Predictive potential of SARS-CoV-2 RNA concentration in wastewater to assess the dynamics of COVID-19 clinical outcomes and infections.The Science of the total environment · 2023Article
- Moving forward with COVID-19: Future research prospects of wastewater-based epidemiology methodologies and applications.Current opinion in environmental science & health · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.
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