Abstract: Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems, and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as potential reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.
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