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
OBJECTIVE: To examine the hypothesis that detraining decreases the resting metabolic rate (RMR) of long-term exercisers. DESIGN: Eight pairs of subjects were matched for age, mass and training volume. They were then randomly allocated to either a control group (continue normal training) or detraining group (stop normal training but continue activities of daily living). SETTING: Exercise Physiology Laboratory, The Flinders University of South Australia. SUBJECTS: Sixteen male subjects (age 23.1 +/- 4.7 y (s.d.); mass 73.73 +/- 8.9 kg; VO2max 60.2 +/- 6.3 ml. kg-1.min-1; height 180.3 +/- 5.0 cm; body fat 14.6 +/- 5.4%) were selected from a pool of respondents to our advertisements. INTERVENTIONS: Each pair of subjects was measured before and after a 3-week experimental period. RESULTS: Two (groups) x 3 (2-, 3-and 4-compartment body composition models) ANOVAs were conducted on the difference between the pre- and post-treatment scores for percentage body fat, fat-free mass (FFM) and relative RMR (kJ.kg FFM-1.h-1). No significant between-group differences were identified except for the detraining group's small decrease in FFM (0.7 kg, P = 0.05). The main effects for body composition model were all significant; but the overall differences between the multicompartment models and the 2-compartment one were less than their technical errors of measurement. No significant interaction (P = 0.51) resulted from a 2 x 2 ANOVA on the pre- and post-treatment absolute RMR data for the control (315.2 and 311.9 kJ/h) and detraining groups (325.4 and 325.5 kJ/h). CONCLUSIONS: 3-weeks detraining is not associated with a decrease in RMR (kJ/h, kJ.kg FFM-1.h-1) in trained males; hence, our data do not support a potentiation of the RMR via exercise training. The greater sensitivity of the multicompartment models to detect changes in body composition was of marginal value.
