Research map / Probabilistic and scientific modeling / Variational and generative sampling
Learning the Probability of Activation in the Presence of Latent Spreaders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model neighbor spreader states as latent, characteristic-dependent variables and infer exposure alongside susceptibility | PALS · Synthetic infection-prediction experiments | AUC | Helped |
| Use PALS rather than the strongest combined exposure-and-susceptibility benchmark | CDI prediction · Hospital CDI test set | AUC: 0.700 -> 0.705 | Helped |
| Use PALS (PartObs) rather than the strongest benchmark | CDI prediction · Hospital CDI test set, FPR = 0.1 | TPR: 0.300 -> 0.324 | Helped |
| Add partially observed spreader labels (ablation) | PALS · Synthetic infection and spreader prediction | AUC | Helped |
| Add partially observed spreader labels (ablation) | PALS (room network) · CDI test set | AUC: 0.704 -> 0.701 | Hurt |
| Add partially observed spreader labels (ablation) | PALS (nurse network) · CDI test set | AUC: 0.700 -> 0.705 | Helped |
| Add partially observed spreader labels (ablation) | PALS (room network) · CDI test set, FPR = 0.1 | TPR: 0.299 -> 0.324 | Helped |
| Add partially observed spreader labels (ablation) | PALS (nurse network) · CDI test set, FPR = 0.1 | TPR: 0.298 -> 0.310 | Helped |
| Rely on susceptibility-only prediction rather than modeling exposure | Synthetic infection prediction · Exposure-independent infection | AUC: 0.5 | No clear effect |
| Increase infection probability conditional on susceptibility | PALS spreader prediction · Synthetic spreader prediction | AUC | No clear effect |
| Increase the proportion of spreaders known during training (ablation) | y-PALS-T · Synthetic infection prediction | AUC | No clear effect |
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