Wastewater-based epidemiology surveillance as an early warning system for SARS-CoV-2 in Indonesia

Indah Kartika Murni, Vicka Oktaria, David T McCarthy, Endah Supriyati, Titik Nuryastuti, Amanda Handley, Celeste M Donato, Bayu Satria Wiratama, Rizka Dinari, Ida Safitri Laksono, Jarir At Thobari and Julie E Bines

PLOS ONE, 2024, vol. 19, issue 7, 1-12

Abstract: Background: Wastewater-based epidemiology (WBE) surveillance has been proposed as an early warning system (EWS) for community SARS-CoV-2 transmission. However, there is limited data from low-and middle-income countries (LMICs). This study aimed to assess the ability of WBE surveillance to detect SARS-CoV-2 in formal and informal environments in Indonesia using different methods of sample collection, to compare WBE data with patterns of clinical cases of COVID-19 within the relevant communities, and to assess the WBE potential to be used as an EWS for SARS-CoV-2 outbreaks within a community. Materials and methods: We conducted WBE surveillance in three districts in Yogyakarta province, Indonesia, over eleven months (27 July 2021 to 7 January 2022 [Delta wave]; 18 January to 3 June 2022 [Omicron wave]). Water samples using grab, and/or passive sampling methods and soil samples were collected either weekly or fortnightly. RNA was extracted from membrane filters from processed water samples and directly from soil. Reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the SARS-CoV-2 N and ORF1ab genes. Results: A total of 1,582 samples were collected. Detection rates of SARS-CoV-2 in wastewater reflected the incidence of community cases, with rates of 85% at the peak to 2% at the end of the Delta wave and from 94% to 11% during the Omicron wave. A 2-week lag time was observed between the detection of SARS-CoV-2 in wastewater and increasing cases in the corresponding community. Conclusion: WBE surveillance for SARS-CoV-2 in Indonesia was effective in monitoring patterns of cases of COVID-19 and served as an early warning system, predicting the increasing incidence of COVID-19 cases in the community.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0307364

DOI: 10.1371/journal.pone.0307364

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