Measuring SARS-CoV-2 RNA in Bangkok wastewater treatment plants and estimating infected population after fully opening the country in 2023, Thailand
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Issued Date
2025-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105000486966
Journal Title
Scientific Reports
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.15 No.1 (2025)
Suggested Citation
Saita T., Thitanuwat B., Niyomdecha N., Prasertsopon J., Lerdsamran H., Puthavathana P., Noisumdaeng P. Measuring SARS-CoV-2 RNA in Bangkok wastewater treatment plants and estimating infected population after fully opening the country in 2023, Thailand. Scientific Reports Vol.15 No.1 (2025). doi:10.1038/s41598-025-94938-7 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/108502
Title
Measuring SARS-CoV-2 RNA in Bangkok wastewater treatment plants and estimating infected population after fully opening the country in 2023, Thailand
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Abstract
Wastewater-based epidemiology (WBE) has been employed for monitoring the presence of SARS-CoV-2 infected population. Herein, the study aims to apply the WBE for surveillance and monitoring SARS-CoV-2 in Bangkok, where the highest official covid-19 cases reported in Thailand, during the fully opening for international tourists in early 2023. A total of 200 wastewater samples (100 influent and 100 effluent samples) were collected from 10 wastewater treatment plants (WWTPs) during January–May 2023. SARS-CoV-2 RNA was detected by real time qRT-PCR with accounting for 51% (102/200). Of these, 88% (88/100) and 14% (14/100) were detected in influent and effluent samples, respectively. The SARS-CoV-2 RNA concentration was detected in ranged of 4.76 × 102–1.48 × 105 copies/L. The amount of SARS-CoV-2 RNA has increased approximately 4 times from the lag phase (January–March) to the log phase (April–May). Spearman’s correlation coefficient revealed that correlation between estimated infected population and weekly reported cases was statistically significant (p-value = 0.017). SARS-CoV-2 RNA in influent had a statistically significant relationship with weekly reported cases (r = 0.481, p-value < 0.001). Lag time analysis revealed early warning 1–3 weeks before rising covid-19 cases observed. GIS was applied for spatial-temporal analysis at the province level, suggesting real time dashboard should be further developed.