COVID-19 Control and Prevention in Taipei: A Data-Driven Approach: Utilizing data on pandemic prevention

In response to the COVID-19 pandemic, Taipei City Government has infused a data-driven strategy to effectively locate the spread of the virus and allocate medical resources efficiently. This paper discusses the city's approach to resource allocation, emphasizing the significance of data-driven decision-making in current policy trends. The City Government has implemented preventive measures such as quarantine stations, home isolation, and promotion of protective behaviors. Additionally, proactive planning for medical capacity involves measures like dedicated bed activation, quarantine hotel setup, and telemedicine services. The success of these measures is closely tied to predicting new confirmed cases, influencing the proactive deployment of medical resources. The article outlines Taipei's predictive method—the Similar Wave Averaging (SWA)—which involves repositioning time series, identifying relevant epidemic waves, and averaging selected reference waves for predictions. Unlike complex models, this method aims for simplicity and adaptability, acknowledging the unpredictability of the future. The paper presents four predictions covering one to two months each, demonstrating adjustments made in response to the evolving pandemic reality. Despite deviations from traditional modeling rigor, the approach proves pragmatic in the face of real-world variability. The ability to predict epidemic peaks remains valuable for policy-making, and through this article, Taipei City Government seeks expert feedback to enhance understanding and inform future pandemic responses.