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Table 2 Plan for examination of the design and implementation challenges of the existing PI models

From: A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

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)