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. 2022 Mar:161:107143.
doi: 10.1016/j.envint.2022.107143. Epub 2022 Feb 14.

Wastewater-based epidemiology in hazard forecasting and early-warning systems for global health risks

B Kasprzyk-Hordern  1 , B Adams  2 , I D Adewale  3 , F O Agunbiade  4 , M I Akinyemi  5 , E Archer  6 , F A Badru  7 , J Barnett  8 , I J Bishop  9 , M Di Lorenzo  10 , P Estrela  11 , J Faraway  2 , M J Fasona  12 , S A Fayomi  13 , E J Feil  14 , L J Hyatt  15 , A T Irewale  13 , T Kjeldsen  16 , A K S Lasisi  17 , S Loiselle  9 , T M Louw  18 , B Metcalfe  11 , S A Nmormah  19 , T O Oluseyi  4 , T R Smith  2 , M C Snyman  20 , T O Sogbanmu  21 , D Stanton-Fraser  8 , S Surujlal-Naicker  22 , P R Wilson  11 , G Wolfaardt  6 , C O Yinka-Banjo  23
Affiliations

Wastewater-based epidemiology in hazard forecasting and early-warning systems for global health risks

B Kasprzyk-Hordern et al. Environ Int. 2022 Mar.

Abstract

With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) - a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities' wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms.

Keywords: Citizen science; Early warning system; Global health; Socio-economic fingerprints; Urban water fingerprinting; Wastewater-based epidemiology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Multi-hazard early warning system utilising urban water.
Fig. 2
Fig. 2
Hazard forecasting and early-warning system for health risks, a four-stage approach.
Fig. 3
Fig. 3
Environment fingerprinting platform for public, environmental health diagnostics and hazard forecasting based on Internet of Things sensors and utilising the cloud for distributed data storage and analytics.
Fig. 4
Fig. 4
a-d. Environment fingerprinting platform using multiple IoT sensor platforms, edge computation and distributed visualization tools.
Fig. 5
Fig. 5
Framework for DUEF data collection and Geospatial Modelling.
See this image and copyright information in PMC

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