@article{Robinson_B-2022_80442,
title = {Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol},
author = {Robinson, B. and Edwards, J. and Kendzerska, T. and Pettit, C. and Poirel, D. and Daly, J. and Ammi, M. and Khalil, M. and Taillon, P. and Sandhu, R. and Mills, S. and Mulpuru, S. and Walker, T. and Percival, V. and Dolean, V. and Sarkar, A.},
month = {mar},
year = {2022},
abstract = {Introduction: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.  Methods and analysis: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.  Ethics and dissemination: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication.},
volume = {12},
issue = {3},
journal = {BMJ Open},
publisher = {BMJ Publishing Group},
url = {https://doi.org/10.1136/bmjopen-2021-052681},
}
