PDF
Full
Outline
Toconstruct a Genetic Algorithm optimized Support Vector Machine (GA-SVM) model based on multi-source data predicting acute respiratory infectious diseases and toevaluate its predictive effectiveness, providing a reference for establishing an early warning system for respiratory infectious diseases.
Symptom surveillance cases, meteorological and atmospheric pollution, data and stringency index obtained from 2020 to 2022 were used as modeling and forecasting samples, respectively. By picking up the optimum lagging week number of the potential predictive variables and filter out the most important variables successively, the independent variables were obtained. Then the full time series data were divided into validation set and training set in a 1:4 ratio. The parameters were optimized by genetic algorithm. We used the weekly number of new cases of respiratory infectious diseases as the dependent variable to structure the GA-SVM model. The performance was evaluated based on the following metrics: root mean square error (RMSE), meansabsolute percentage error (MAPE), predictive correlation coefficient (PCC) and R-squared (R2).
The most important variables were stringency index with 2-weeks-lag, symptom surveillance cases with 1-week-lag, maximum temperature with 1-week-lag, school activities with 2-weeks-lag and O3 index with 1-week-lag. The GA-SVM model performed best when C=18.04, γ=0.175 4 while average RMSE=6.362, average MAPE=24.59%, average PCC=0.896 and average R2=0.804.
The model showsgood predictive performance for the reported cases of acute respiratory infectious diseases in Xuhui District, which confirms the feasibility of applying GA-SVM to multi-source data based on symptom monitoring for predicting respiratory infectious diseases, providing methodological references for the application of multi-source data in the early warning of infectious diseases.
PDF
62
25
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
