COVID-19: An Algorithmic Model for Critical Medical Resource Rationing in a Public Health Emergency

The aim of the project is to develop an algorithmic model that calibrates a dynamic index for patient priority by addressing the shortcomings of the current allocation protocols of scarce medical resources. The total number of confirmed Covid-19 deaths in the UK has already passed the 45,000 mark. Such a horrific number of deaths is partly attributable to the shortage of PPE, medical staff, and ICU beds in the early stages of UK pandemic. For a second wave of Covid-19 likely in the winter when the healthcare system is most stretched, scientists have estimated that the UK could see about 120,000 new coronavirus deaths. To achieve the greatest good for the greatest number of patients, it is essential to have in place ethically and clinically sound policies on the allocation of scarce resources. Existing triage guidelines determine patient priority based on several attributes, including the illness severity and the near-term prognosis after discharge. They focus on individual patients but ignore the overall mixture of current patient profiles and the uncertainty in the number of patients who become critical ill over time. Previous research has shown that such frameworks could lead to preventative deaths and inefficient usage of scarce resources. We aim to address these limitations in this project via the development of an algorithmic model that calibrates a dynamic index (priority). Its performance is to be compared against the benchmarks via an empirical study using anonymised data of Covid-19 patients collected by Public Health England.