Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Biometeorological modelling and forecasting of ambulance demand for Hong Kong: a spatio-temporal approach
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. ; published_or_final_version ; Geography ; Doctoral ; Doctor of Philosophy
Biometeorological modelling and forecasting of ambulance demand for Hong Kong: a spatio-temporal approach
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. ; published_or_final_version ; Geography ; Doctoral ; Doctor of Philosophy
Biometeorological modelling and forecasting of ambulance demand for Hong Kong: a spatio-temporal approach
Wong, Ho-ting. (Autor:in) / 黃浩霆. (Autor:in) / Lai, PC
b4775297
http://hub.hku.hk/bib/B4775297X
Hochschulschrift
Elektronische Ressource
Englisch
DDC:
690
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