COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff (preprint)

Care homes in the UK were disproportionately affected by the first wave of the COVID-19 pandemic, accounting for almost half of COVID-19 deaths over the course of the period from 6th March - 15th June 2020. Understanding how infectious diseases establish themselves throughout vulnerable communities is crucial for minimising deaths and lowering the total stress on the National Health Service (NHS Scotland). We model the spread of COVID-19 in the health-board of NHS Lothian, Scotland over the course of the first wave of the pandemic with a compartmental Susceptible - Exposed - Infected reported - Infected unreported - Recovered - Dead (SEIARD), metapopulation model. Care home residents, care home workers and the rest of the population are modelled as subpopulations, interacting on a network describing their mixing habits. We explicitly model the outbreaks reproduction rate and care home visitation level over time for each subpopulation, and execute a data fit and sensitivity analysis, focusing on parameters responsible for intra-subpopulation mixing: staff sharing, staff shift patterns and visitation. The results suggest that hospital discharges were not predominantly responsible for the early outbreak in care homes, and that only a few such cases led to infection seeding in care homes by the 6th of March Sensitivity analysis show the main mode of entry into care homes are infections by staff interacting with the general population. Visitation (before cancellation) and staff sharing were less significant in affecting outbreak size. Focusing on the protection and monitoring of staff, followed by reductions in staff sharing and quick cancellations of visitation can significantly reduce future infection attack rates of COVID-19 in care homes. Author SummaryCOVID-19 has spread throughout care homes in the UK, leading to many deaths of those most vulnerable in our population. This has sparked the need for further understanding of how infectious diseases spread throughout vulnerable communities such as care homes. We developed a model focused on the first wave in the Scottish health board of Lothian, which indicated pathways most likely leading to COVID-19 establishment within care homes. We found that care home visitation and hospital discharges did not significantly affect total COVID-19 cases in care home residents. The most significant route of entry for COVID-19 into care homes was through staff infections from the general population. We suggest to prioritise minimising infections in this pathway to reduce the number of outbreaks in care homes. Our model indicated that care homes were three weeks behind the general population in reducing the reproduction rate of COVID-19. This delay emphasises the need for more planning and support for care homes in organising effective responses to emerging pandemics.

Modelling a novel Coronavirus (COVID-19): A stochastic SEIR-HCD approach, with real-time parameter estimation & forecasting for Scotland (preprint)

Faced with the 2020 SARS-CoV2 epidemic, public health officials have been seeking models that could be used to predict not only the number of new cases but also the levels of hospitalisation, critical care and deaths. In this paper we present a stochastic compartmental model capable of real-time monitoring and forecasting of the pandemic incorporating multiple streams of real-world data, reported cases, testing intensity, deaths, hospitalisations and critical care occupancy. Model parameters are estimated via a Bayesian particle filtering technique. The model successfully tracks the key variables (reported cases, critical care and deaths) throughout the two waves (March-June and September-November 2020) of the COVID-19 outbreak in Scotland. The model hospitalisation predictions in Summer 2020 are consistently lower than the recorded data, but consistent with the change to the reporting criteria by the Health Protection Scotland on 15th September. Most parameter estimates were constant over the two waves, but the infection rate and consequently the reproductive number decrease in the later stages of the first wave and increase again from July 2020. The death rates are initially high but decrease over Summer 2020 before rising again in November. The model can also be used to provide short-term predictions. We show that the 2-week predictability is very good for the period from March to June 2020, even at early stages of the pandemic. The model has been slower to pick up the increase in the case numbers in September 2020 but forecasting improves again in the later stages of the epidemic.

Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model. (preprint)

Restrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures. In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring locally (i.e. in the model, 80% of transmission events occurred within spatially defined communities of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account. The best fit model also shows a considerable influence of the health index of deprivation (part of the index of multiple deprivations or iMD) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.