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. 2022 Jun 30:218:118451.
doi: 10.1016/j.watres.2022.118451. Epub 2022 Apr 13.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

Guangming Jiang  1 , Jiangping Wu  2 , Jennifer Weidhaas  3 , Xuan Li  2 , Yan Chen  2 , Jochen Mueller  4 , Jiaying Li  4 , Manish Kumar  5 , Xu Zhou  6 , Sudipti Arora  7 , Eiji Haramoto  8 , Samendra Sherchan  9 , Gorka Orive  10 , Unax Lertxundi  10 , Ryo Honda  11 , Masaaki Kitajima  12 , Greg Jackson  13
Affiliations

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

Guangming Jiang et al. Water Res. .

Abstract

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Keywords: Artificial neural network; COVID-19; Incidence; Prevalence; SARS-CoV-2; Wastewater-based epidemiology.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Guangming Jiang reports financial support was provided by Australian Research Council. Guangming Jiang reports financial support was provided by Australian Academy of Science. Guangming Jiang reports financial support was provided by Australian Government Department of Industry Science Energy and Resources.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Correlations between all ANN input features and targets (P4 and P14d, representing incidence and prevalence rate, respectively, and Ri) in the WBE datasets obtained in Utah, USA. The numbers and circle sizes indicate the correlation coefficient; and blank cells indicate insignificant correlations by a cut-off p=0.01.
Fig. 2
Fig. 2
(A) Box plots of the correlation coefficient (R) and estimation error (MSE) of ANN models being trained to predict incidence rates, i.e., P1, P2, …, P7 (ANN-IR) and prevalence rates, i.e., P3d, P7d, P14d and P3dF, P7dF and P14dF (ANN-PR) in Utah, USA. (B) ANN outputs vs. clinical testing reported incidence rate, i.e. case numbers on the 4th day of the wastewater sampling date. (C) ANN outputs vs. the prevalence rate reported by clinical test, i.e., the 14-day running average of the wastewater sampling date.
Fig. 3
Fig. 3
Daily new cases (green circles) on the 4th day (A) and 14-day running average (B) case numbers of wastewater sampling date and ANN estimated cases (lines) for selected wastewater treatment plants with different populations, i.e., 500, 250, 100, 25, and 6 thousand people in Utah, USA.
Fig. 4
Fig. 4
The regression plot of the ANN estimated effective reproduction rate vs. the reported effective reproduction rate determined in conventional approach in Utah, USA.
See this image and copyright information in PMC

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