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Uncertainty quantification in epidemiological models for the COVID-19 pandemic.
Leila Taghizadeh1, Ahmad Karimi1, Clemens Heitzinger2
1Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstraße 8-10, 1040, Vienna, Austria.
Computers in Biology and Medicine
|October 22, 2020
Summary
This study develops computational models for COVID-19, using logistic and SIR equations with Bayesian inversion. It estimates key epidemiological parameters for Austria, aiding public health decisions and outbreak prediction.
Area of Science:
- Epidemiology
- Computational Mathematics
- Public Health
Background:
- Mathematical modeling is crucial for understanding disease dynamics and outbreaks.
- Forward and inverse modeling are essential components for accurate epidemiological analysis.
Purpose of the Study:
- To develop forward and inverse computational models for COVID-19 pandemic estimation and prediction.
- To support governmental decision-making for effective protective measures and outbreak prevention.
Main Methods:
- Utilized logistic and SIR (susceptible-infected-removed) differential equations for COVID-19 spread modeling.
- Employed Bayesian inversion techniques and an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for parameter estimation.
- Analyzed publicly available data from Austria to validate the models and assess protective measures.
Main Results:
- Estimated logistic model parameters: growth rate (0.28) and carrying capacity (14974).
- Estimated SIR model parameters: transmission rate (0.36) and recovery rate (0.06).
- Calculated reproduction number decay from ~3 to 1 in Austria; estimated case fatality rate (CFR) at 4% and ICU bed usage at 2%.
Conclusions:
- The developed computational methodologies provide accurate estimations and predictions for the COVID-19 pandemic.
- Bayesian inversion techniques offer robust parameter estimation for epidemiological models.
- Findings offer valuable insights for policymakers, enabling more realistic future outbreak forecasts and effective public health interventions.
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