Abstract
Winters are a difficult period for the National Health Service (NHS) in the United Kingdom (UK), due to the combination of cold weather and the increased likelihood of respiratory infections, especially influenza. In this article we present a proper statistical time series approach for modelling and analysing weekly hospital admissions in the West Midlands in the UK during the period week 15/1990 to week 14/1999. We consider three variables, namely, hospital admissions, general practitioner consultants, and minimum temperature. The autocorrelations of each series are shown to decay hyperbolically. The correlations of hospital admission and the lag of other series also decay hyperbolically but with different speed and directions. One of the main objectives of this paper is to show that each of the three series can be represented by a Fractional Differenced Autoregressive integrated moving average model, (FDA). Further, the hospital admission winter and summer residuals shows significant interdependency, which may be interpreted as hidden periodicities within the last 10-years time interval. The short-range (8 weeks) forecasting of hospital admission of the FDA model and a fourth-order AutoRegressive AR(4) model are quite similar. However, our results reveal that the long-range forecasting of FDA is more realistic. This implies that, using the FDA approach, the respective authority can plan for winter pressure properly.