Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus.

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Consent, Refusal, and Waivers in Patient-Centered Dysphagia Care: Using Law, Ethics, and Evidence to Guide Clinical Practice.

Purpose: When patients refuse medical or rehabilitation procedures, waivers of liability have been used to bar future lawsuits. The purpose of this tutorial is to review the myriad issues surrounding consent, refusal, and waivers. The larger goal is to invigorate clinical practice by providing clinicians with knowledge of ethics and law. This tutorial is for educational purposes only and does not constitute legal advice. Method: The authors use a hypothetical case of a "noncompliant" individual under the care of an interdisciplinary neurorehabilitation team to illuminate the ethical and legal features of the patient-practitioner relationship; the elements of clinical decision-making capacity; the duty of disclosure and the right of informed consent or informed refusal; and the relationship among noncompliance, defensive practices, and iatrogenic harm. We explore the legal question of whether waivers of liability in the medical context are enforceable or unenforceable as a matter of public policy. Conclusions: Speech-language pathologists, among other health care providers, have fiduciary and other ethical and legal obligations to patients. Because waivers try to shift liability for substandard care from health care providers to patients, courts usually find waivers of liability in the medical context unenforceable as a matter of public policy.