Abstract
Purpose
Methods
Figures and Tables
Fig. 1
Box plot showing distribution of patients with asthma by sex and age group. M, man subject; W, woman subject. M1 and W1: 0–2 years old, M2 and W2: 3–6 years old, M3 and W3: 7–18 years old, M4 and W4: 19–64 years old, M5 and W5: 65 years old.
![aard-4-328-g001](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-g001.jpg)
Fig. 2
Histogram showing distribution of patients with asthma by sex and age group. M, man subject; W, woman subject. M1 and W1: 0–2 years old, M2 and W2: 3–6 years old, M3 and W3: 7–18 years old, M4 and W4: 19–64 years old, M5 and W5: 65 years old.
![aard-4-328-g002](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-g002.jpg)
Table 1
Potential predictors of asthma
![aard-4-328-i001](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i001.jpg)
Numbers indicate the significant lag times (days) between environmental factors and the Health Insurance Review and Assessment Service data (occurrence of asthma symptoms).
A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; PL, pollen; FL, proportion of flu patients; PM, concentration of yellow sand; YS, presence of yellow sand; D, day of the week; M, man; W, woman.
Table 2
Assessment of model validity and predictability
![aard-4-328-i002](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i002.jpg)
Actual case | Forecasted category | ||
---|---|---|---|
Continuous management | Attention | Total | |
Continuous management | A | B | A+B |
Attention | C | D | C+D |
Total | A+C | B+D | A+B+C+D |
Table 3
Asthma cases in the HIRA dataset according to sex, age group (1–5), and season
![aard-4-328-i003](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i003.jpg)
Table 4
Potential predictors
![aard-4-328-i004](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i004.jpg)
A circle means that the predictor is significantly correlated with the corresponding group.
A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; YS, presence of yellow sand; PM, concentration of yellow sand; PL, pollen; FL, proportion of flu patients; M, man; W, woman.
Table 5
Comparison of binary forecasting models for the spring season
![aard-4-328-i005](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i005.jpg)
A threshold cannot be used to determine the category of symptoms in the multiple regression model.
HR, hit rate; POD, probability of detection; FAR, false alarm rate; A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; PL, pollen; FL, proportion of flu patients; PM, concentration of yellow sand; YS, presence of yellow sand; D, day of the week; M, man; W, woman.
*No numerical value. **There is no significant predictive factor or dummy variable denoting day of week.
Table 6
Comparison of the binary forecasting models for the summer season
![aard-4-328-i006](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i006.jpg)
A threshold cannot be used to determine the category of symptoms in the multiple regression model.
HR, hit rate; POD, probability of detection; FAR, false alarm rate; A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; PL, pollen; FL, proportion of flu patients; PM, concentration of yellow sand; YS, presence of yellow sand; D, day of the week; M, man; W, woman.
*No numerical value. **There is no significant predictive factor or dummy variable denoting day of week.
Table 7
Comparison of the binary forecasting models for the autumn season
![aard-4-328-i007](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i007.jpg)
A threshold cannot be used to determine the category of symptoms in the multiple regression model.
HR, hit rate; POD, probability of detection; FAR, false alarm rate; A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; PL, pollen; FL, proportion of flu patients; PM, concentration of yellow sand; YS, presence of yellow sand; D, day of the week; M, man; W, woman.
*No numerical value. **There is no significant predictive factor or dummy variable denoting day of week.
Table 8
Comparison of the binary forecasting models for the winter season.
![aard-4-328-i008](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i008.jpg)
A threshold cannot be used to determine the category of symptoms in the multiple regression model.
HR, hit rate; POD, probability of detection; FAR, false alarm rate; A, autocorrelated factor; T, mean temperature; DT, daily range; MH, minimum humidity; PR, pressure; HS, hours of sunshine; OZ, concentration of ozone; PL, pollen; FL, proportion of flu patients; PM, concentration of yellow sand; YS, presence of yellow sand; D, day of the week; M, man; W, woman.
*No numerical value.
Table 9
Proposed models and thresholds for binary asthma forecasting
![aard-4-328-i009](/upload/SynapseData/ArticleImage/0206aard/aard-4-328-i009.jpg)
References
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