Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

In the ongoing SARS-CoV-2 pandemic, there is a need for new strategies for surveillance and identification of arising infection waves. Reported cases of new infections based on individual testing are soon deemed inaccurate due to ever changing regulations and limited testing capacity. Wastewater based epidemiology is one promising solution that can be broadly applied with low efforts in comparison to current large-scale testing of individuals. Here, we are combining local wastewater data from the city of Dresden (Germany) along with reported cases and vaccination data from a central database (Robert-Koch-Institute) with virus variant information to investigate the correlation of virus concentrations in the wastewater and reported SARS-CoV-2 cases. In particular, we compared Linear Regression and Machine Learning (ML) models, which are both revealing an existing correlation of virus particles in wastewater and reported cases. Our findings demonstrate that the different virus variants of concern (Alpha, Delta, BA.1, and BA.2) contribute differently over time and parameters vary between variants, as well. By comparing the Linear Regression and ML-based models, we observed that ML can achieve a good fit for training data, but Linear Regression is a more robust tool, especially for new virus variants. We hereby conclude that deriving the rate of new infections from local wastewater by applying Linear Regression may be a robust approximation of tracing the state of the pandemic for practitioners and policy makers alike. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was in part supported by a grant of the State Ministry of Science and Cultural Affairs of Saxony (SMWK) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics application (BO-EK-383072021) has been confirmed by the Ethic Committee of the Dresden Technical Univsersity, which is registered as institutional review board (IRB00001473) at the Office of Human Research Protection. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All used scripts and raw data are available online at