Impact of COVID-19 on the Belfast Bike Sharing Scheme

The COVID-19 pandemic brought the connectivity and mobility of Northern Irish residents to a halt forcing the bike hiring service Belfast Bikes to cease operations. After reintroducing the service in Summer 2020, the usage of the bike hiring service was severely impacted. This paper investigates the usage of Belfast Bikes pre-pandemic and during the pandemic. Research includes how consumer trends for Belfast Bikes customers changed due to the pandemic and predictive modelling is used to predict whether customers hire a Belfast Bike for direct travel from stations or for an indirect, leisurely trip. We will conclude how the connectivity and mobility of Belfast residents have changed due to the pandemic and provide recommendations for Belfast Bikes and Belfast City to recover after the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact of COVID-19 on the Belfast Bike Sharing Scheme

The COVID-19 pandemic brought the connectivity and mobility of Northern Irish residents to a halt forcing the bike hiring service Belfast Bikes to cease operations. After reintroducing the service in Summer 2020, the usage of the bike hiring service was severely impacted. This paper investigates the usage of Belfast Bikes pre-pandemic and during the pandemic. Research includes how consumer trends for Belfast Bikes customers changed due to the pandemic and predictive modelling is used to predict whether customers hire a Belfast Bike for direct travel from stations or for an indirect, leisurely trip. We will conclude how the connectivity and mobility of Belfast residents have changed due to the pandemic and provide recommendations for Belfast Bikes and Belfast City to recover after the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Knowledge Discovery of the Delays Experienced in Reporting COVID-19 Confirmed Positive Cases Using Time to Event Models

Survival analysis techniques model the time to an event where the event of interest traditionally is recovery or death from a disease. The distribution of survival data is generally highly skewed in nature and characteristically can include patients in the study who never experience the event of interest. Such censored patients can be accommodated in survival analysis approaches. During the COVID-19 pandemic, the rapid reporting of positive cases is critical in providing insight to understand the level of infection while also informing policy. In this research, we introduce the very novel application of survival models to the time that suspected COVID-19 patients wait to receive their positive diagnosis. In fact, this paper not only considers the application of survival techniques for the time period from symptom onset to notification of the positive result but also demonstrates the application of survival analysis for multiple time points in the diagnosis pathway. The approach is illustrated using publicly available data for Ontario, Canada for one year of the pandemic beginning in March 2020. © 2021, Springer Nature Switzerland AG.