Summary of each scenario used in the simulation-based validation, illustrating a progression towards more realistic data generation processes from Scenario A to D and Scenario E covering a scenario of varying observation probability without mortality.

<p>Summary of each scenario used in the simulation-based validation, illustrating a progression towards more realistic data generation processes from Scenario A to D and Scenario E covering a scenario of varying observation probability without mortality.</p>

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Bibliographic Details
Main Author:	Belinda Lombard (20496382) (author)
Other Authors:	Harry Moultrie (355765) (author), Juliet R.C. Pulliam (17027753) (author), Cari van Schalkwyk (174758) (author)
Published:	2025
Subjects:	Medicine Biotechnology Ecology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> given possible biases arising may bias inference high global seroprevalence become increasingly important model parameters converged model outputs aligning model &# 8217 low observation probabilities including imperfect observation therefore smaller numbers similar scenarios may incorporated different processes accurately detect changes catalytic model assumes simulated reinfection datasets simulated data representative south african sars imperfect observation low numbers detect changes catalytic model simulated data south africa smaller epidemics model projections track trends scenarios leading positive detection poor convergence outbreak magnitude observed infections observed cases models developed false positives caution must bayesian approach based validation avoid false anticipated outcomes 10 %)
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Summary of each scenario used in the simulation-based validation, illustrating a progression towards more realistic data generation processes from Scenario A to D and Scenario E covering a scenario of varying observation probability without mortality.

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