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. 2021 Sep 10:786:147451.
doi: 10.1016/j.scitotenv.2021.147451. Epub 2021 Apr 30.

On forecasting the community-level COVID-19 cases from the concentration of SARS-CoV-2 in wastewater

Yongtao Cao  1 , Roland Francis  2
Affiliations

On forecasting the community-level COVID-19 cases from the concentration of SARS-CoV-2 in wastewater

Yongtao Cao et al. Sci Total Environ. .

Abstract

The building of an effective wastewater-based epidemiological model that can translate SARS-CoV-2 concentrations in wastewater to the prevalence of virus shedders within a community is a significant challenge for wastewater surveillance. The objectives of this study were to investigate the association between SARS-CoV-2 wastewater concentrations and the COVID-19 cases at the community-level and to assess how SARS-CoV-2 wastewater concentrations should be integrated into a wastewater-based epidemiological statistical model that can provide reliable forecasts for the number of COVID-19 infections and the evolution over time as well. Weekly variations on the SARS-CoV-2 wastewater concentrations and COVID-19 cases from April 29, 2020 through February 17, 2021 were obtained in Borough of Indiana, PA. Vector autoregression (VAR) model with different data forms were fitted on this data from April 29, 2020 through January 27, 2021, and the performance in three weeks ahead forecasting (February 3, 10, and 17) were compared with measures of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). A stationary block bootstrapping VAR method was also presented to reduce the variability in the forecasting values. Our results demonstrate that VAR(1) estimated with the logged data has the best interpretation of the data, but a VAR(1) estimated with the original data has a stronger forecasting ability. The forecast accuracy, measured by MAPE, for 1 week, 2 weeks, and 3 weeks in the future can be as low as 11.85%, 8.97% and 21.57%. The forecasting performance of the model on a short time span is unfortunately not very impressive. Also, a single increase in the SARS-CoV-2 concentration can impact the COVID-19 cases in an inverted-U shape pattern with the maximum impact occur in the third week after. The flexibility of this approach and easy-to-follow explanations are suitable for many different locations where the wastewater surveillance system has been implemented.

Keywords: COVID-19; Community wastewater surveillance; Forecasting; SARS-CoV-2.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Graphical abstract
Fig. 1
Fig. 1
15,701 and 15,705 (white area in the center) Zip Code Postal Districts. Please note, 7.55% of 15,701 Zip District is not sewered.
Fig. 2
Fig. 2
Distributions of the COVID-19 cases, logged COVID-19 cases, SARS-CoV-2 concentrations, and logged SARS-CoV-2 concentrations in Indiana Borough data.
Fig. 3
Fig. 3
Time series plot of SARS-CoV-2 and logged SARS-CoV-2 concentration and their ACF and PACF plots for Indiana Borough data.
Fig. 4
Fig. 4
Time series plot of COVID-19 cases and logged COVID-19 cases concentration and their ACF and PACF plots for Indiana Borough data.
Fig. 5
Fig. 5
The correlation between weekly change in COVID-19 cases and SARS-CoV-2 concentration; and logged weekly change in COVID-19 cases and logged SARS-CoV-2 concentration in IB data.
Fig. 6
Fig. 6
The orthogonal impulse response of logged weekly change in COVID-19 cases from the logged change in SARS-CoV-2 concentration with 95% bootstrap CI (b = 500).
Fig. 7
Fig. 7
The Bootstrap distributions for the first week, second week and third week forecasts. Results are from 1000 non-parametric stationary block bootstraps. The black dot denotes the median value in the distribution.
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See this image and copyright information in PMC

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