BACKGROUND: The COVID-19 pandemic, driven by SARS-CoV-2, has posed significant global health challenges since late 2019. Efficient monitoring of the virus's spread is crucial for controlling outbreaks and implementing effective public health measures. Wastewater-based epidemiology (WBE) has emerged as a promising method for surveillance by detecting viral RNA in sewage, indicating community-level infection rates. This study aims to evaluate the presence of SARS-CoV-2 RNA in wastewater samples from various residential buildings within the University College Hospital (UCH) complex in Ibadan, Nigeria. METHODS: Conducted as a cross-sectional study, wastewater samples were collected from residential buildings housing medical staff and students within the UCH complex. A total of 180 untreated wastewater samples were systematically collected and analyzed using reverse transcription-polymerase chain reaction (RT-PCR) to detect SARS-CoV-2 RNA. Factors such as building type, population density, and proximity to medical facilities were examined for their influence on the presence of viral RNA. RESULT: Among the 180 wastewater samples analyzed, 3 (1.9%) tested positive for SARS-CoV-2 RNA. Most samples (98.1%) did not contain detectable viral RNA. A preliminary pilot study demonstrated the methodology's capability to detect the virus in wastewater, irrespective of the building type. The distribution of positive samples suggests the potential of WBE for early detection and monitoring of COVID-19 outbreaks within densely populated healthcare settings. CONCLUSION: The findings underscore the feasibility of employing WBE as a robust surveillance tool for tracking SARS-CoV-2 in complex urban environments like UCH. The low prevalence of detectable viral RNA highlights the effectiveness of current public health measures and the importance of continuous monitoring. This study contributes valuable insights into the dynamics of viral shedding and transmission, supporting the implementation of WBE in other healthcare and urban settings for pandemic surveillance and response.
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Aliu Olalekan Olatunji (2024) studied this question.
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