Modelling COVID-19 epidemic curve in Taipei City, Taiwan by a citywide wastewater SARS-CoV-2 Surveillance

Abstract

Over 70 countries have adopted wastewater surveillance during the COVID-19 pandemic as a novel tool to detect unidentified cases and monitor epidemic curves. However, epidemic prediction models are highly site-specific, necessitating tailored approaches. This study aimed to establish a citywide wastewater surveillance system and develop an epidemic prediction model for Taipei City, Taiwan. From May to August 2022, wastewater samples were collected daily from the Xinyi and Neihu districts and twice weekly from the remaining 10 districts. SARS-CoV-2 genetic material was quantified using RT-qPCR, and a “relative signal” was calculated as the ratio of SARS-CoV-2 viral concentration to the concentration of the human RNase P gene to normalize variability in sample collection. Regression analysis based on data from the two districts was conducted to forecast new COVID-19 cases. On average, wastewater samples contained 1,829.0 ± 2,237.7 viral copies per liter, with relative signals averaging 17.1 ± 16.7. The best-fitting model, adjusted for temperature, indicated that a 1 % increase in viral signals corresponded to an approximately 0.27 % rise in the future 5-day moving average of new cases. With an R-squared value of 0.78, the model demonstrated robust explanatory power. The model, validated via a paired sample t-test, reliably estimated epidemic trends with no significant difference between predicted and reported cases in the other 10 districts. These findings suggest that wastewater viral surveillance can be an effective supplementary tool for epidemic forecasting in urban settings like Taipei, where high sewer connectivity is in place.