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

Abstract

Background The world is experiencing local/regional hotspots and spikes in 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 utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets 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 subsequent validation. Results The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing 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 utilized 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 organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame 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 impacts of COVID-19 transmission.

Reformulating the susceptible–infectious–removed model in terms of the number of detected cases: well-posedness of the observational model

“…As a result, leaders from Public Health Intelligence in the local authority organizations approached the University of Sussex to undertake COVID-19 epidemiological modelling that is specific to Sussex as the national level modelling from the Scientific Advisory Group for Emergencies was not applicable at the regional, integrated care level. In response to their request, we developed a data-driven SIR-type mathematical model, which in practice has been used to answer public health questions based around healthcare demand and capacity, and mortuary capacity, such as the impact of second waves, future lockdowns and vaccination supply [ 18 ]. In our early parameter estimation attempts, it became clear to us that the data provided were not compatible with the standard SIR-type formulation, in particular, the data did not provide an accurate estimation of all the initial conditions of the compartments of the model.…”

“…For example, being admitted into hospital is a change of state from being infectious in the community to being in a hospital bed, whereby the infectious state and hospital bed state are compartments of a mathematical model, while the admitted component is the change between compartments. In this set-up, the admissions data are proportional to the infectious compartment, but not directly as it is an integral, so identifying the initial conditions of the compartments from the data becomes a real challenge, see [ 18 ] for more details.…”

“…The ‘observational model’, as coined in [ 18 ], is a representation of a compartmental model, such as the SIR equations, described in terms of the parameters and compartments that are captured, or to be inferred, by the mathematical interpretation of the data. This observational model gives an intuitive understanding of what parameters can be identified and how the data affects different compartments in the model.…”

Basic reproduction number projection for novel pandemics and variants. Ancestral SARS-CoV2 R0 projection

“…The coronavirus disease 2019 (COVID-19) pandemic highlighted the need to anticipate the impact of a novel pathogen on healthcare [1][2][3][4] or the economy [5,6]. One of the impact factors is the basic reproduction number (R 0 ) -a demographic concept that has been repurposed for infectious disease epidemiology [7][8][9][10][11].…”

Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman

“…At the same time, they used the Brownian motion process to calculate the environmental noise of the data centre. Campillo-Funollet et al [ 3 ] used SEIR-D quantitative epidemiological modeling for healthcare demand, capacity, and the impact of local outbreaks of COVID-19 predicting, and the model exhibits a high accuracy in the prediction. Savi et al [ 2 ] based on the framework of the SEIR model to analyze different scenarios of COVID-19 in Brazil.…”