| Summary: | 碩士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 106 === Background and purpose:
At the end of January 2016, influenza outbreaks in Taiwan, reaching a peak in February during the Chinese Festival, and so led to the emergency room at hospitals have been completely packed. On 24 January 2016, the temperature was dropping to 4-Celsius degree in Taipei. We use the line chart to find out the correlation between the outbreak of flu, index of weather traffic and the numbers of holidays. Using time series to build the model and predict the future of the proportion of influenza in the emergency department within eight weeks. The prediction could help each hospital emergency room or outpatient clinic in demand of drugs and human resource.
Methods:
The source of the study is the Taiwan National Infectious Disease Statistics System data of Center for Disease Control, observation data query system of Central Weather Bureau (CWB), Taiwan Area National Freeway Bureau and year calendar from 2007 to 2016. Database obtained from the CDC''s Taiwan National Infectious Disease Statistics System data in the first week of 2007 to the fifty-two week of 2016 Taipei City weekly proportion of Influenza-like illness (ILI) and add the data from CWB and calendar days to build the time series model. Using the developed model to predict the proportion of influenza-like illness in Taipei City from the first week of 2017 to the eighth week of 2017 and compare it with the actual value. The proportion is the number of ILI divided the total number of people visited the emergency department. Using time series to analyze the data from Taiwan Area National Freeway Bureau and weekly proportion of Influenza-like illness in Taiwan to prove that the critical mass may cause the outbreak of flu. Compare the prediction of the model which doesn''t add input variables (temperature and holidays) with the model add input variables (temperature and holidays).
Results
The ARMA (1,1) model of the emergency diagnosis rate of the emergency in Taipei district (model 1), the moving average parameter is estimated to be 0.11688 (p-value= 0.0169), and the autoregression parameter is estimated to be 0.91729 (p-value <.0001). The ARMA (1,1) model of the emergency diagnosis rate of the emergency in Taipei district added the input variables temperature and holidays (model 2), the moving average parameter is estimated to be -0.13852 (p -value = 0.0051), the autoregressive coefficient parameter is estimated to be 0.90485 (p-value <.0001), coefficient of low temperature is -0.04536 (p-value = 0.0387, delay 1) and coefficient of holiday is 0.52171 (p-value <.0001, 0). The model 2 is better than them model 1, D^2 = 25.72 for model 1 and D^2= 18.85 for model 2. Obviously, the difference between model 2 and actual value is small.In addition, the ARMA (2,2) model of emergency diagnosis rate of the emergency in Taiwan added input variable the max of million vehicles kilometer, the moving average parameter MA1,1 is estimated to be 0.73192 (p -value<.0001), the moving average parameter MA1,2 is estimated to be
-0.19545 (p -value=0.0011), the autoregression parameter AR1,1 is estimated to be 1.66721 (p-value <.0001), the autoregression parameter AR1,2 is estimated to be
-0.70858 (p-value <.0001) and coefficient of the max of million vehicles kilometer is - 0.07306 (p-value <.0001, delay 0).
Conclusion:
This study found that the best model was the regression of ARMA(1,1) for the lag a week of the Taipei City weekly proportion of Influenza-like illness at the lowest temperature of the week.
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