This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 day days in advance. have an RMS error of 3% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH errors, will result in an increase in waiting times in the A&E Department. Furthermore. volatility gives more warning of waiting times in A&E than total bed occupancy.
This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 February 2019.
All research outputs
#3,837,121
of 31,513,782 outputs
Outputs from Health Care Management Science
#39
of 391 outputs
Outputs of similar age
#6,828
of 68,278 outputs
Outputs of similar age from Health Care Management Science
#1
of 2 outputs
Altmetric has tracked 31,513,782 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 391 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 90% of its peers.
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We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them