The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making

Chinwe Iwu-Jaja; Nkosenhle Lindo Ndlovu; Said Rachida; Mukhlid Yousif; Setshaba Taukobong; Mokgaetji Macheke; Laurette Mhlanga; Cari van Schalkwyk; Juliet R C Pulliam; Tom Moultrie; Wouter le Roux; Lisa Schaefer; Gina Pocock; Leanne Z Coetzee; Janet Mans; Faizal Bux; Leanne Pillay; Dariah de Villiers; A P du Toit; Don Jambo; Annancietar Gomba; Shaun Groenink; Neil Madgewick; Martie van der Walt; Awelani Mutshembele; Natascha Berkowitz; Melinda Suchard; Kerrigan McCarthy; SACCESS network

doi:10.1016/j.scitotenv.2023.165817

The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making

Sci Total Environ. 2023 Dec 10:903:165817. doi: 10.1016/j.scitotenv.2023.165817. Epub 2023 Jul 26.

Authors

Chinwe Iwu-Jaja¹, Nkosenhle Lindo Ndlovu², Said Rachida³, Mukhlid Yousif⁴, Setshaba Taukobong³, Mokgaetji Macheke³, Laurette Mhlanga⁵, Cari van Schalkwyk⁵, Juliet R C Pulliam⁵, Tom Moultrie⁶, Wouter le Roux⁷, Lisa Schaefer⁷, Gina Pocock⁸, Leanne Z Coetzee⁸, Janet Mans⁹, Faizal Bux¹⁰, Leanne Pillay¹⁰, Dariah de Villiers¹¹, A P du Toit¹¹, Don Jambo¹², Annancietar Gomba¹², Shaun Groenink¹³, Neil Madgewick¹⁴, Martie van der Walt¹⁵, Awelani Mutshembele¹⁵, Natascha Berkowitz¹⁶, Melinda Suchard⁴, Kerrigan McCarthy¹⁷; SACCESS network

Affiliations

¹ Centre for Vaccines and Immunology, National Institute for Communicable Diseases, South Africa. Electronic address: chinwej@nicd.ac.za.
² Centre for Vaccines and Immunology, National Institute for Communicable Diseases, South Africa. Electronic address: nkosenhlen@nicd.ac.za.
³ Centre for Vaccines and Immunology, National Institute for Communicable Diseases, South Africa.
⁴ Centre for Vaccines and Immunology, National Institute for Communicable Diseases, South Africa; School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.
⁵ South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
⁶ Centre for Actuarial Research, University of Cape Town, South Africa.
⁷ Water Centre, Council for Scientific and Industrial Research, Pretoria, South Africa.
⁸ Waterlab (Pty) Ltd, Pretoria, South Africa.
⁹ Department of Medical Virology, University of Pretoria, Pretoria, South Africa.
¹⁰ Institute for Water and Wastewater Technology, Durban University of Technology, Durban, South Africa.
¹¹ Lumegen Laboratories (Pty) Ltd, Potchefstroom, South Africa.
¹² National Institute for Occupational Health, South Africa.
¹³ Greenhill Laboratories, South Africa.
¹⁴ Praecautio, South Africa.
¹⁵ South African Medical Research Council-Tuberculosis Platform, South Africa.
¹⁶ City of Cape Town, South Africa.
¹⁷ Centre for Vaccines and Immunology, National Institute for Communicable Diseases, South Africa; School of Pathology, University of the Witwatersrand, Johannesburg, South Africa; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.

PMID: 37506905
DOI: 10.1016/j.scitotenv.2023.165817

Abstract

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R² = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

Keywords: COVID-19; SARS-CoV-2; South Africa; Surveillance; Wastewater based epidemiology.