Design and implementation challenges | Plan of examination |
---|---|
Validity of data support (A1) for model parameters | For each PI model and for each of the major model parameters (e.g., reproduction number, illness attack rate) examine: |
 | A1a. Data source for parameter values (actual, simulated, assumed) |
 | A1b. Age of data |
 | A1c. Type of interface for data access and retrieval (manual, automatic) |
 | A1d. Technique to translate raw data into model parameter values (e.g., arithmetic conversion, Bayesian estimation) |
Credibility and validity of model assumptions (A2) | For each of the reviewed PI models, examine assumptions concerning contact probability and frequency of new infection updates |
Represent human behavior (A3) | For each of the PI models: |
 | - identify the human behavioral aspects addressed, |
 | - examine data support using criteria A1a through A1d, and |
 | - assess the reasons for inadequacy of human behavioral considerations |
Accessibility and scalability (A4, A5) | For each PI model, examine: |
 | - if the model software is available to general public (open source or closed source code), |
 | - presence of end user support (user manuals, e-mail/phone technical support), |
 | - information on the number of replicates needed for valid output, |
 | - information on the running time, |
 | - information on the ways to manage the computational load for implementing large-scale scenarios (e.g., the use of distributed and parallel computing), |
 | - use of replicate minimization techniques, and |
 | - type of interface for data access and retrieval (A1c), and data translation (A1d) |