| WP |
White & Pagano Method, due to [42]. Serial distribution can be assumed known or can be estimated using MLE; method developed under branching process model; simple method which yields real-time estimates (when serial interval is unknown the method takes longer to compute). |
| seqB |
Sequential Bayes Method, due to [43]. Serial distribution assumed known (only the mean is used); method developed assuming SIR model and uses sequential Bayes methods; simple method which yields real-time estimates. |
| ID |
Incidence Decay Method (see [44]). Serial distribution assumed known (only the mean is used); method developed assuming an SIR model structure and uses least squares estimation. It is a simple method which yields real-time estimates. |
| IDEA |
The Incidence Decay and Exponential Adjustment Method is presented in [44]. Serial distribution assumed known (only the mean is used); method developed assuming SIR model and uses least squares estimation; simple method which yields real-time estimates. IDEA uses a slightly more complex model for fitting than ID. |
| plug-and-play |
Plug-and-Play Method. See [45]. Serial distribution assumed unknown; method selects one of SIR/SEIR/SEAIR model; implementations available though not real-time (depending on input selection). Generally, this approach fits the complete model using maximum likelihood and relying on Monte Carlo to fill in missing observations. The R-package, called POMP, is quite technical and can be difficult to implement [45]. |
| fullBayes |
Full Bayes Method. See [46]. Serial distribution assumed unknown; method selects one of SIR/SEIR/SEAIR model; not real-time. this approach fits the complete model using maximum likelihood and relying on Monte Carlo to fill in missing observations. Can be quite technical in implementation. |
