Aortic stenosis post-COVID-19: A mathematical model on waiting lists and mortality (preprint)

ObjectivesTo provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect NHS England waiting list duration and associated mortality. DesignWe constructed a mathematical model of the excess waiting list and found the closed-form analytic solution to that model. From published data, we calculated estimates for how the following strategies may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality. Interventions1) increasing the capacity for the treatment of severe AS, 2) converting proportions of cases from surgery to transcatheter aortic valve implantation, and 3) a combination of these two. ResultsIn a capacitated system, clearing the backlog by returning to pre-COVID-19 capacity is not possible. A conversion rate of 50% would clear the backlog within 666 (95% CI, 533-848) days with 1419 (95% CI, 597-2189) deaths whilst waiting during this time. A 20% capacity increase would require 535 (95% CI, 434-666) days, with an associated mortality of 1172 (95% CI, 466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (95% CI, 281-410) days) with 784 (95% CI, 292-1324) deaths whilst awaiting treatment. ConclusionA strategy change to the management of severe AS is required to reduce the NHS backlog and waiting list deaths during the post-COVID-19 recovery period. However, plausible adaptations will still incur a substantial wait and many hundreds dying without treatment.

An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England (preprint)

Problem definition: This paper describes the real-time participatory modeling work that our team of academics, public health officials, and clinical decision makers has been undertaking to support the regional efforts to tackle COVID-19 in the East of England (EoE). Methodology: Since March 2020, we have been studying research questions that have allowed us to address the pandemic's rapidly evolving current and near-future epidemiological state, as well as short-term (a few weeks) and medium-term (several months) bed capacity demand. Frequent data input from and consultations with our public health and clinical partners allow our academic team to apply dynamic data-driven approaches using time series modeling and system dynamics modeling. We thus obtain a broad view of the evolving situation.Results: The academic team presents the model outcomes and insights during weekly joint meetings among public health services, national health services, and other academics to support COVID-19 planning activities in the EoE, contributing to the discussion of the COVID-19 response and issues beyond immediate COVID-19 planning. Academic/practical relevance: As COVID-19 planning efforts necessitate a rapid response, our short- and medium-term forecasting models aim to achieve the right balance between rigor and speed in the face of an uncertain and constantly changing situation. Managerial implications: Our regional and local focus enables us to better understand the pandemic's progression and to help decision makers make more informed short- and medium-term capacity plans in different localities in the EoE. In addition, the knowledge gained through our collaborative experiences may inform guidance on how academics and practitioners can successfully collaborate in rapid response to disasters such as COVID-19.