ArticleThe Science of the total environment2023
Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.
Article in The Science of the total environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Wastewater-Based Epidemiology for Infectious Diseases: A New Trick for an Old Threat.Environment & health (Washington, D.C.) · 2026Review
- Environmental and microbial factors shaping SARS-CoV-2 RNA decay in wastewater: insights from batch tests and a lab-scale sewer pipeline simulator.Scientific reports · 2026Article
- Modeling and predicting water consumption in fattening pigs using autoregressive moving average with external parameters.Scientific reports · 2026Article
- Article
- Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives.Microorganisms · 2025Review
- Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China.Infectious Disease Modelling · 2025Article
- Prediction of SARS-CoV-2 infection cases based on the meta-SEIRS model.Epidemiology and infection · 2024Article
- Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater.Scientific reports · 2024Article
- Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky.Environmental research · 2024Article
- Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study.Infectious Disease Modelling · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to develop a novel copula-based time series (CTS) model to forecast COVID-19 cases and trends based on wastewater SARS-CoV-2 viral load and clinical variables. Wastewater samples were collected from wastewater pumping stations in five sewersheds in the City of Chesapeake VA. Wastewater SARS-CoV-2 viral load was measured using reverse transcription droplet digital PCR (RT-ddPCR). The clinical dataset included daily COVID-19 reported cases, hospitalization cases, and death cases. The CTS model development included two steps: an autoregressive moving average (ARMA) model for time series analysis (step I), and an integration of ARMA and a copula function for marginal regression analysis (step II). Poisson and negative binomial marginal probability densities for copula functions were used to determine the forecasting capacity of the CTS model for COVID-19 forecasts in the same geographical area. The dynamic trends predicted by the CTS model were well suited to the trend of the reported cases as the forecasted cases from the CTS model fell within the 99 % confidence interval of the reported cases. Wastewater SARS CoV-2 viral load served as a reliable predictor for forecasting COVID-19 cases. The CTS model provided robust modeling to predict COVID-19 cases.
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