Table 2. SEIR and ML model comparison across implementation considerations for SARS-CoV-2 incidence prediction.
| Model | SEIR | ML | ||
|---|---|---|---|---|
| Quantitative model comparison | Simulated data MSE (in %) | 0.6 (95% CI, 0.60–0.64%) | Random Forest†: 54 (95% CI, 39–83%) | |
| Qualitative model comparisons | Sampling type | Random sampling | Random sampling | |
| Number of SARS-CoV-2-positive samples for which model best suited | Small (»30) | Large (>1000) | ||
| Sampling frequency | Single/ multiple snapshots | Daily snapshots | ||
| Flexibility | Modelling of transmission | Fixed in time | Time-independent | |
| Ability to add in multiple predictors | Unable to incorporate | Able to incorporate, and flexible in their representation | ||
| Scalability | Single outbreak setting | Population level | ||
| Computational complexity* | Low | Low-moderate | ||
| Predictive power requirements | Good in single setting with well-mixed population and stable contact behaviour/infection control | No requirements other than sufficient sample size for Ct summary statistics by snapshot | ||
| Additional sampling requirements | None | Ordered in time, restricted to fixed interval sampling | ||
Ct, cycle threshold; ML, machine learning; SARS-CoV-2, severe acute respiratory syndrome coronavirus type 2; SEIR, susceptible-exposed-infected-recovered.
*
Relative computational complexity based on assumed sample size listed in Sacability row.
†
Random Forest presented as was the top performing ML model.