Name
Abstract | ||
|---|---|---|
Simulation systems are human artifacts to capture the abstraction and simplification of the real world. Study the output of simulation systems can help us understand the real world better. Deep learning system needs large volume and high quality data, therefore, a perfect match with simulation systems. We use the data from an agent based simulation system for disease transmission, to train the deep neural network to perform several prediction tasks. The model reaches 80 percent accuracy to predict the infectious level of virus, the prediction of the peak date is off by at most 8 days 90 percent of the time, and the prediction of the peak value is off at most 20 percent 90 percent of the time at the end of the 7th week. We use some preprocessing tricks and relative error leveling to resolve the magnitude problem. Among all these encouraging results, we did encounter some difficulty when predicting the index date given information at the middle of an epidemic. We note that if some interesting concepts are difficult to predict in a simulated world, it sheds some lights on the difficulty for real world scenarios. To learn the effects of mitigation strategies is an interesting and sensible next step. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0007829702310238 | SIMULTECH: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS, 2019 |
Keywords | Field | DocType |
Agent based Simulation System, Machine Learning, Epidemiology | Computer science,Simulation,Artificial intelligence,Deep learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Ju Chen | 1 | 13 | 2.47 |
| Tsan-sheng Hsu | 2 | 737 | 101.00 |
| Zong-De Jian | 3 | 0 | 0.34 |
| Ting-Yu Lin | 4 | 16 | 5.65 |
| Mei-Lien Pan | 5 | 0 | 1.69 |
| Da-Wei Wang | 6 | 0 | 3.38 |