Estimating the state of epidemics spreading with graph neural networks
Abstract When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We an...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Netherlands
2022
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| Online Access: | https://hdl.handle.net/1721.1/143634 |