Biometeorological modelling and forecasting of ambulance demand for Hong Kong: a spatio-temporal approach

Abstract

The demand for emergency ambulance services in Hong Kong is on the rise.

Issues such as climate change, ageing population, constrained space, and limited

resource capacity mean that the present way of meeting service demand by injecting

more resources will reach its limit in the near future and unlikely to be sustainable.

There is an urgent need to develop a more realistic forecast model to account for the

anticipated demand for emergency ambulance services to enable better strategic

planning of resources and more effective logistic arrangement. In this connection, the

research objectives of this thesis include the following:

1. To examine relationships between weather and ambulance demand, with

specific reference to temperature effects on demographic and admission

characteristics of patients.

2. To establish a quantitative model for short-term (1-7 days ahead) forecast of

ambulance demand in Hong Kong.

3. To estimate the longer-term demand for ambulance services by sub areas in

Hong Kong, taking into account projected weather and population changes in 2019 and 2036.

The research concurs with the findings of other researchers that temperature was

the most important weather factor affecting the daily ambulance demand in

2006-2009, accounting for 49% of the demand variance. An even higher demand

variance of 74% could be explained among people aged 65 and above. The

incorporation of 1-7 day forecast data of the average temperature improved the

forecast accuracy of daily ambulance demand on average by 33% in terms of R2 and

11% in terms of root mean square error (RMSE). Moreover, the forecast accuracy

could be further improved by as much as 4% for both R2 and RMSE through spatial

sub models. For demand projection of a longer-term, significant underestimation was

observed if changes in the population demographics were not considered. The

underestimation of annual ambulance demand for 2019 and 2036 was 16% and 38%

respectively.

The research has practical and methodological implications. First, the

quantitative model for short-term forecast can inform demand in the next few days to

enable logistic deployment of ambulance services beforehand, which, in turn, ensures

that potential victims can be served in a swift and efficient manner. Second, the

longer-term projection on the demand for ambulance services enables better

preparation and planning for the expected rise in demand in time and space.

Unbudgeted or unnecessary purchases of ambulances can be prevented without

compromising preparedness and service quality. Third, the methodology is adaptable

and the model can be reconstituted when more accurate projections on weather and

population changes become available.