# Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Modelling Procedure

- In the first category of the proposed modelling algorithm, the only source of information is the timeline of the wastewater signal, that is, the SARS-CoV-2 N1-gene copy numbers. Typically, this signal comes with significant noise and outliers, which influence the model training performance [45] Thus, a time series filtering method (the Spline method is used herein; [46]) is applied to filter out the noisy information adhered to the gene copy numbers.
- In the second category, in addition to the gene copy number, the information on the number of tests taken in the communities is used where available (in only two of the case studies). In addition, we apply further data pre-processing steps such as population-normalisation of the viral load, time lag, and time series filtering (as in the first category), the details of which are discussed in Section 2.3, Section 2.4 and Section 2.5.

^{2}).

#### 2.2. Dataset

_{4}-N concentration, which are used as population markers. The WWTPs are anonymised by the letters A–D, with the mean of the data given in Table 1 and the timeline of the raw SARS-CoV-2 signal as well as the active cases depicted in Figure 2. Details of the measurement programs are given elsewhere [21,45] and not repeated herein. Note that the wastewater composition in these four case studies is in the range typically found in industrialised countries and will thus not influence the methodology.

#### 2.3. Normalisation

^{3}/d], Q is the inflow to the WWTP [m

^{3}/d], and P is the population connected to the treatment plant. To estimate the dynamics of the population in the catchment, population-based biomarkers are suggested [43,44]. To capture the temporal dynamics, we assume that the population can be estimated by means of the daily personal biomarker load ${f}_{bm}$ [M/m

^{3}/d/cap] as follows:

^{3}). Substituting Equation (3) into Equation (2) gives

_{4}-N was routinely monitored. To estimate the representative value ${f}_{NH4N}$, the 50th percentile of the daily personal NH

_{4}-N load was determined during the period of the first lockdown in Austria (16 February to 13 April 2020) because the inflow fluctuations to the WWTPs are minimal during that period (see also [45]). f

_{NH4N}was found to range between 6.5 and 10.7 g NH4_N/m

^{3}/cap/d.

#### 2.4. Filtering

#### 2.5. Lagging

#### 2.6. Regression Models

#### 2.7. Evaluation

^{2}) and root mean square error (RMSE). The equations for R

^{2}and RMSE are presented in the Appendix A.

## 3. Results and discussion

#### 3.1. Time Series Lag

#### 3.2. Global Parameter Tuning

#### 3.3. Model Metrics

^{2}and RMSE metrics calculated for the different statistical models are plotted in Appendix B Figure A1 for the example of WWTP A. It is worth mentioning that the PI values for each station are based on the average of the metrics over a nine-fold trial. Models containing additional information (category 2) outweigh models that are fed only with a filtered SARS-CoV-2 signal (S), proving the importance of signal lagging, normalisation (N), and the number of tests taken (T) within the target communities. An increase in the model performance of approximately 10% to 20% and 1.0% to 5.0% in terms of R

^{2}and RMSE, respectively, is observed (data found in Supplementary Materials Figures S1–S3).

#### 3.4. Model Comparison

## 4. Conclusions

- There is a consistent time shift between the (earlier) wastewater signal and the clinical test records, varying from 2 to 7 days in our dataset—depending both on time period and site;
- A thorough pre-processing of the data, such as population-based normalisation and smoothing, leads to more robust models and is important for practical application;
- The inclusion of additional information (most importantly the time lag and number of tests taken) by applying multivariate models significantly increases the performance of all investigated models;
- All multivariate models are generally applicable for the regression, and even a simple linear regression can be used, despite showing the poorest performance.
- While the differences are small, PL and KNN outperform more complex models such as GAM, SVR, and MLP;
- As seen from above, regression between the wastewater signal and incidence values is derived easily—also, in a practical context. The information supplements—but could even replace—individual testing for incidence.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

^{2}is 1.

## Appendix B

**Figure A1.**Model performance as spider chart—WWTP A. (

**a**) RMSE, and (

**b**) RSQ metric comparing signal (S) only with combined information of signal (S), normalisation (N) and tests taken (T).

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**Figure 2.**Raw data timeline of SARS-CoV-2 gene copy numbers (copies/mL) and epidemiological timelines at the four sampling sites, where (

**A**–

**D**) corresponds to WWTPs A–D.

**Figure 3.**Cross-correlation (CC) plots between SARS-CoV-2 signals and active cases in WWTPs (

**A**–

**D**). The negative and positive lags correspond to the forward and backward lags between the incidence time series and viral load time series. The solid dashed lines indicate the significant levels at 95% confidence.

**Figure 4.**Correlation matrix for model regressors. Solid circles indicate Pearson’s correlation coefficients for multivariate models, and the dashed circle shows the same metric for a univariate model. Notations: A, active cases; X, SARS-CoV-2 load; N, normalised signal; S, smoothed signal; T, number of tests taken; and (number), a signal with number time step delay.

**Figure 5.**Evaluation of regression models ((

**a**) KNN, (

**b**) polynomial, (

**c**) SVR, (

**d**) MLP), for different ranges of parameters using the mean and variance of RMSE for the test data—WWTP A.

**Figure 6.**Visualisation of the number of active cases recorded versus model prediction in WWTP A: (

**A**,

**B**) model predictions against recorded data for training subset under univariate and multivariate inputs, respectively, and (

**C**,

**D**) the same plots for the testing subset.

**Figure 7.**Taylor diagram, displaying statistical comparison of the eight model predictions against the actual number of recorded active cases—WWTP A.

**Figure 8.**Data timeline of smoothed and normalized SARS-CoV-2 titer values (Megacopies/cap/d) versus active cases in observed/predicted timelines for the best models in WWTPs (

**A**–

**D**).

WWTP | Start Date | End Date | Avg. Daily SARS-CoV-2 Gene Copies/mL | Avg. Daily Active Cases | Avg. Daily Number of Tests | Avg. Daily Inflow (m^{3}/d) | Population (1 January 2020) | Avg. Daily NH_{4}-N (mg/L) |
---|---|---|---|---|---|---|---|---|

A | 4 May 2020 | 30 Dec 2020 | 464 | 5325 | 4218 | 539,450 | 1,900,000 | 38.09 |

B | 3 Aug 2020 | 28 Dec 2020 | 609 | 249 | 1126 | 83,187 | 320,681 | 29.49 |

C | 27 Jul 2020 | 28 Dec 2020 | 658 | 186 | 16,344 | 41,696 | 28.90 | |

D | 27 Jul 2020 | 21 Dec 2020 | 781 | 136 | 4899 | 23,600 | 34.18 |

Model | Reference | Application in Covid 19 Modeling |
---|---|---|

Linear (LR) | [54] | [34] |

Polynomial (PL) | [55] | [56] |

K Nearest Neighbor (KNN) | [57] | [58] |

Multilayer Perceptron (MLP) | [59] | [36] |

Support Vector Regression (SVR) | [60] | [61] |

Generalized Additive Models (GAM) | [62] | [63] |

Decision Tree (DT) | [64] | [65] |

Random Forest (RF) | [15] | [66] |

WWTP | Models Metrics | ||||||
---|---|---|---|---|---|---|---|

R-Squared | Adj. R-Squared | F_Statistic | Prob (F-Statistic) | Log-Likelihood | AIC | BIC | |

A | 0.939 | 0.936 | 303.600 | 0.000 | −554.100 | 1116.000 | 1125.000 |

B | 0.942 | 0.939 | 365.600 | 0.000 | −425.690 | 859.400 | 868.500 |

C | 0.868 | 0.862 | 148.300 | 0.000 | −280.880 | 567.800 | 573.400 |

D | 0.873 | 0.869 | 196.800 | 0.000 | −356.860 | 719.700 | 726.000 |

Models parameters | |||||||

Const. | S2 * | S3 | S4 | S5 | S7 | T ** | |

A | 573.150 | 49.890 | - | −16.710 | - | - | 0.003 |

B | −278.660 | 3.230 | - | - | - | 2.040 | 0.001 |

C | 167.425 | - | - | - | 2.482 | −1.060 | - |

D | −69.215 | - | 1.035 | - | - | 0.916 | - |

Parameters significance: P > │t│ | |||||||

A | 0.193 | 0.000 | - | 0.001 | - | - | 0.010 |

B | 0.000 | 0.000 | - | - | - | 0.000 | 0.000 |

C | 0.000 | - | - | - | 0.000 | 0.000 | - |

D | 0.001 | - | 0.000 | - | - | 0.000 | - |

Model/WWTP | A | B | C | D |
---|---|---|---|---|

DT | ||||

GAM | ○ | ○ | ||

KNN | ● | ● | ○ | |

LR | ||||

MLP | ● | |||

PL | ○ | ○ | ○ | ○ |

RF | ||||

SVR | ○ | ● |

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**MDPI and ACS Style**

Aberi, P.; Arabzadeh, R.; Insam, H.; Markt, R.; Mayr, M.; Kreuzinger, N.; Rauch, W.
Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology. *Int. J. Environ. Res. Public Health* **2021**, *18*, 10778.
https://doi.org/10.3390/ijerph182010778

**AMA Style**

Aberi P, Arabzadeh R, Insam H, Markt R, Mayr M, Kreuzinger N, Rauch W.
Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology. *International Journal of Environmental Research and Public Health*. 2021; 18(20):10778.
https://doi.org/10.3390/ijerph182010778

**Chicago/Turabian Style**

Aberi, Parisa, Rezgar Arabzadeh, Heribert Insam, Rudolf Markt, Markus Mayr, Norbert Kreuzinger, and Wolfgang Rauch.
2021. "Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology" *International Journal of Environmental Research and Public Health* 18, no. 20: 10778.
https://doi.org/10.3390/ijerph182010778