Wastewater-based epidemiology for surveillance of infectious diseases in healthcare settings

Abstract

Purpose of review Wastewater-based surveillance (WBS) (epidemiology) using near-source sampling (NSS) in large buildings, hospitals and care homes is reviewed covering three main areas: state-of-the-art WBS, benefits/opportunities NSS has for hospital infection control systems and new insights from hospital wastewater surveillance and policy implications. Recent findings Wastewater provides aggregate, anonymous sources of data where the spatial resolution can be linked to populations being served. In hospitals, clear links established between wastewater RNA-fragments signal to nosocomial COVID-19 cases/outbreaks. Detecting other targets from hospital wastewater such as antimicrobial resistance markers is considered a substantial opportunity for this technology. Other clinically relevant infections, that is influenza and monkeypox, can be perceived, and sub-variant resolution to target public health response in near real time to benefit hospital infection control. WBS can reduce hospitals’ clinical testing requirements, as diagnostic costs are aggregated into fewer samples while still detecting single cases. Summary WBS using NSS can inform infectious disease monitoring earlier, faster and cheaper than conventional monitoring. Routine sampling using wastewater provides a platform for risk-based sampling and enables smarter allocation of resources. Finally, hospital wastewater can be used for the benefit of the wastewater surveillance field as a promising source to monitor emerging threats and resolve longstanding questions on faecal shedding. Hospital monitoring in low-income settings is considered a priority for future research.

Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19

“…On a clinical side, one could hypothesize that an emerging new disease with significant impact on health would lead to new patterns of radiological depictions that could be captured with NLP before the semiology of the disease has been deciphered, which is inherently shifted by several weeks due to the time needed to understand patterns, collect databases, and statistically verify associations between features and diseases. Thus, such NLP-based detection methods on radiological reports could complement other efforts to detect emerging new disease notably wastewater-based surveillance in addition to clinical surveillance [ 5 , 6 ].…”

DataSheet1.docx

“…As with any research related to SARS-CoV-2, there is an extensive literature on its surveillance in wastewater (Amman and Bergthaler, 2022;Dutta et al, 2022;Gonçalves et al, 2022;Greenbaum et al, 2022;Kumar et al, 2022;Burdorf and Rugulies, 2023;Du Toit, 2023;Gahlot et al, 2023;Hopkins et al, 2023;Oloye et al, 2023;Sodhi and Singh, 2023;Tavazzi et al, 2023). This has resulted in a renaissance of WBE and early warning systems based on pathogen surveillance (O'Brien and Xagoraraki, 2019;Guo et al, 2022a;Guo et al, 2022b;Fitzmorris-Brisolara et al, 2022;Demeter et al, 2023;Hassard et al, 2023;Shaw et al, 2023;Wolfe et al, 2023) On the other hand, WBE also place significant emphasis on detecting small molecules and/or metabolites excreted by humans. These substances can offer valuable insights into population habits, particularities, and health status serving as biomarkers (Vitale et al, 2021).…”