ABSTRACT
Analysing the mass of time series data accumulating daily and weekly from the coronavirus pandemic has become ever more important as the pandemic has progressed through its numerous phases. Econometric techniques are particularly suited to analysing this data and research using these techniques is now appearing. Much of this research has focused on short-term forecasting of infections, hospital admissions and deaths, and on generalising to stochastic settings compartmental epidemiological models, such as the well-known "susceptible (S), infected (I) and recovered or deceased (R)", or SIR, model. The focus of the present paper is rather different, however, in that it investigates the changing dynamic relationship between infections, hospital admissions and deaths using daily data from England. It does this using two approaches, balanced growth models and autoregressive distributed lag/error correction models. It is found that there has been a substantial decrease over time in the number of deaths and hospital admissions associated with an increase in infections, with patients being kept alive longer, as clinical practice has improved and the vaccination program rolled out. These responses may be tracked and monitored through time to ascertain whether such improvements have been maintained.