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

TL;DR

This paper introduces a stochastic SEIR-HCD model with real-time data integration and Bayesian parameter estimation to predict COVID-19 dynamics and healthcare needs in Scotland, successfully capturing key epidemic variables over two waves.

Contribution

It presents a novel real-time stochastic modeling approach with Bayesian parameter estimation for COVID-19, incorporating multiple data streams for accurate forecasting.

Findings

Model accurately tracks cases, critical care, and deaths during two waves.

Parameter estimates remain stable, with infection rate changes reflecting epidemic dynamics.

Short-term forecasts are reliable, especially over two-week periods.

Abstract

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.