Establishing an SEIR-based framework for local modelling of COVID-19 infections, hospitalisations and deaths.

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.

Small area variation in hospital admission: random or systematic?

For some conditions hospital admission is mandatory. This should lead to low variability in admission rates and no effect on admission rate of distance from hospital. If admission is discretionary, we would expect high variability in small area admission rates, and a decline in admission rate as travel time to hospital increases. We wanted to see if non-random variability of admission rates, as measured by the systematic coefficient of variation (SCV), and distance decay, as estimated in regression models, were related. We examined variability and travel time dependence of hospital admission for seven conditions in 62 small (mean population 9900) areas of Surrey, England. Age and sex standardized admission ratios (SAR) were calculated, and their dependence on travel time, adjusting for deprivation, were estimated by linear multiple regression adjusted for spatial correlation. Deprivation was measured by Jarman's score, and time by computerized estimates of drive time to the nearest acute hospital. We found an inverse relationship between time to hospital and admission ratio for ischaemic heart disease, bronchopneumonia and chronic bronchitis. Admission ratios for diabetes mellitus and stroke were related to neither deprivation nor time. For these seven conditions there was no simple relationship between SCV and travel time dependence.