Predicting and forecasting the impact of local resurgence and outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity

Background: The world is experiencing local/regional hot-spots and spikes of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy making, and public health decisions. Methods The model utilised the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges, and bed occupancy) from the local NHS hospitals and COVID-19 related weekly deaths in hospitals and other settings in Sussex (population 1·7M), Southeast England. These datasets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequently validated. Results The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national datasets.28 We validate the predictive power of our model by using a subset of the available data and compare the model predictions for the next 10, 20, and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting. Conclusions We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilised to guide the local healthcare demand and capacity, policy making, and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organisation of services. The flexibility of timings in the model, in combination with other early warning systems, produces a timeframe for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities, and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impact of COVID-19 transmission.