ABSTRACT
Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future "waves" is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary "Scenario Review Working Group", comprising commissioners, public health officials and academic epidemiologists. The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system's major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.
ABSTRACT
During the first wave of the COVID-19 pandemic it emerged that the nature and magnitude of demand for mental health services was changing. Considerable increases were expected to follow initial lulls as treatment was sought for new and existing conditions following relaxation of 'lockdown' measures. For this to be managed by the various services that constitute a mental health system, it would be necessary to complement such projections with assessments of capacity, in order to understand the propagation of demand and the value of any consequent mitigations. This paper provides an account of exploratory modelling undertaken within a major UK healthcare system during the first wave of the pandemic, when actionable insights were in short supply and decisions were made under much uncertainty. In understanding the impact on post-lockdown operational performance, the objective was to evaluate the efficacy of two considered interventions against a baseline 'do nothing' scenario. In doing so, a versatile and purpose-built discrete time simulation model was developed, calibrated and used by a multi-disciplinary project working group. The solution, representing a multi-node, multi-server queueing network with reneging, is implemented in open-source software and is freely and publicly available.
ABSTRACT
Francisella tularensis is a virulent bacterium which hijacks the host's immune response, turning cells usually tasked with killing foreign organisms as nutrient-rich receptacles for rapid multiplication. It is designated a Tier 1 Select Agent by the US CDC due to its potential to cause widespread mortality upon deliberate release. In this study, the key biological mechanisms of host infection are modelled as a continuous-time Markov chain (CTMC) and solved via discrete event simulation (DES). The efficacy of various medication strategies at the behest of public health authorities are investigated. Without treatment, the median lethal dose - not previously considered in humans - is estimated at approximately 1,000 organisms. Results suggest that fatality can be averted if antibiotics are administered for at least 7 days, no later than 10 days post exposure. In a mass casualty setting, this can reduce symptomatic cases by 18% from the baseline involving no medication. Reductions of 59% are possible if medication can be disseminated no later than 5 days post exposure, mitigating otherwise severe pressure on healthcare services. Novel use of CTMC and DES highlight the potential for OR in the field of immunology, where further opportunities may present in a post COVID-19 world.