A predictive model for the post-pandemic delay in elective treatment.

The COVID-19 pandemic had a major impact on healthcare systems across the world. In the United Kingdom, one of the strategies used by hospitals to cope with the surge in patients infected with SARS-Cov-2 was to cancel a vast number of elective treatments planned and limit its resources for non-critical patients. This resulted in a 30% drop in the number of people joining the waiting list in 2020-2021 versus 2019-2020. Once the pandemic subsides and resources are freed for elective treatment, the expectation is that the patients failing to receive treatment throughout the pandemic would trigger a significant backlog on the waiting list post-pandemic with major repercussions to patient health and quality of life. As the nation emerges from the worst phase of the pandemic, hospitals are focusing on strategies to prioritise patients for elective treatments. A key challenge in this context is the ability to quantify the expected backlog and predict the delays experienced by patients as an outcome of the prioritisation policies. This study presents an approach based on discrete-event simulation to predict the elective waiting list backlog along with the delay in treatment based on a predetermined prioritisation policy. The model is demonstrated using data on the endoscopy waiting list at Cambridge University Hospitals. The model shows that 21% of the patients on the waiting list will experience a delay less than 18-weeks, the acceptable threshold set by the National Health Service (NHS). A longer-term scenario analysis based on the model reveals investment in NHS resources will have a significant positive outcome for addressing the waiting lists. The model presented in this paper has the potential to be an invaluable tool for post-pandemic planning for hospitals around the world that are facing a crisis of treatment backlog.

Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period.

The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives, including the way people interact with buildings. Leveraging the advances in machine learning and other supporting digital technologies, recent attempts have been sought to establish exciting smart building applications that facilitates better facility management and higher energy efficiency. However, relying on the historical data collected prior to the pandemic, the resulting smart building applications are not necessarily effective under the current ever-changing situation due to the drifts of data distribution. This paper investigates the bidirectional interaction between human and buildings that leads to dramatic change of building performance data distributions post-pandemic, and evaluates the applicability of typical facility management and energy management applications against these changes. According to the evaluation, this paper recommends three mitigation measures to rescue the applications and embedded machine learning algorithms from the data inconsistency issue in the post-pandemic era. Among these measures, incorporating occupancy and behavioural parameters as independent variables in machine learning algorithms is highlighted. Taking a Bayesian perspective, the value of data is exploited, historical or recent, pre- and post-pandemic, under a people-focused view.

A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality.

An ecologic analysis was conducted to explore the correlation between air pollution, and COVID-19 cases and fatality rates in London. The analysis demonstrated a strong correlation (R2 > 0.7) between increment in air pollution and an increase in the risk of COVID-19 transmission within London boroughs. Particularly, strong correlations (R2 > 0.72) between the risk of COVID-19 fatality and nitrogen dioxide and particulate matter pollution concentrations were found. Although this study assumed the same level of air pollution across a particular London borough, it demonstrates the possibility to employ air pollution as an indicator to rapidly identify the city's vulnerable regions. Such an approach can inform the decisions to suspend or reduce the operation of different public transport modes within a city. The methodology and learnings from the study can thus aid in public transport's response to COVID-19 outbreak by adopting different levels of human-mobility reduction strategies based on the vulnerability of a given region.

The impact of government measures and human mobility trend on COVID-19 related deaths in the UK.

The COVID-19 global pandemic has rapidly expanded, with the UK being one of the countries with the highest number of cases and deaths in proportion to its population. Major clinical and human behavioural measures have been taken by the UK government to control the spread of the pandemic and to support the health system. It remains unclear how exactly human mobility restrictions have affected the virus spread in the UK. This research uses driving, walking and transit real-time data to investigate the impact of government control measures on human mobility reduction, as well as the connection between trends in human-mobility and severe COVID-19 outcomes. Human mobility was observed to gradually decrease as the government was announcing more measures and it stabilized at a scale of around 80% after a lockdown was imposed. The study shows that human-mobility reduction had a significant impact on reducing COVID-19-related deaths, thus providing crucial evidence in support of such government measures.