Bayesian
mechanistic model of COVID-19 transmission
dynamics including
the effect of vaccination
Javier
Blecua, Juan
Fernandez-Recio
Instituto de Ciencias de la Vid y
del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja
Brief description:
A new mechanistic model that describes the transmission dynamics of
COVID-19 was applied initially to estimate the effect of the
non-pharmacological measures by means of Bayesian analysis methods. The model
was extended to include also the impact of the vaccination and of the different
virus variants.
The
consistent results obtained for a total of 30 European countries, with data
following very different patterns, confirmed that the model used is an
appropriate method for describing the present and future evolution of the
disease.
Keywords:
COVID-19 modelling, non-pharmacological
measures, vaccination, virus variants, mechanistic model, Bayesian analysis.
Abstract:
COVID-19
(coronavirus disease 2019) is a pandemic disease caused by a new type of
coronavirus called SARS-CoV-2, which has caused unprecedented medical, economic
and social burden worldwide. A variety of models to describe the transmission
dynamics of the virus and the impact of non-pharmacological measures have been
reported. Among them, Bayesian mechanistic models using MCMC optimization have
shown good description of the transmission dynamics and have potential for
accurate predictions of future evolution of the pandemics.
In our
group, we have been working on optimizing a previously reported COVID-19
transmission model, which has been extended here for the analysis of multiple
periods of different transmission rates, enabling the inclusion of an arbitrary
number of non-pharmacological measures. Additionally, the model has been
extended to include the effect of vaccination and the impact of the different
virus variants on the transmission dynamics.
The algorithm computes the evolution of the daily
number of infections by fitting a SEIR model to the observed daily deaths, in a
Bayesian framework, using MCMC optimization to obtain the a posteriori
distribution for the parameters that best describe the impact on the
transmission rate of each intervention measure. The model captures the positive
impact of the vaccination on the evolution of the disease, takes into account
the immunity of the recovered population and considers specific transmission
parameters for the different virus variants.
The model was successfully applied to a total of 30
European countries, obtaining good fit results and conclusions related to the
impact of the different interventions that were consistent with results from
other studies.
Interestingly, the model also estimates the percentage
of immune population required to reach the herd immunity in the different
countries, which is a valuable tool to understand the evolution of the
pandemics on the long term and help in future worldwide control strategies.
Methodology and results:
All details, including the description of the
methodology and the results and conclusions obtained from the application of
this model to data of a total of 30 European countries, will be described in a
paper, currently under preparation.
The access to the results is already possible
using the links in the navigation panel located on the left side of this web
page.
The values that quantify the impact of
different intervention measures, presented as numeric values in tables and
graphics, represent the factor with which each intervention contributes to
changes in the value of the reproduction number (Rt). Values less
than 1 correspond to effective measures that have contributed to a reduction of
the transmission rate, while values greater than 1 are associated with periods
or events that have led to an increase in the reproduction number (Rt)
and thus in the number of infections.
References:
Blecua, J., Fernandez-Recio, J. (2026) Bayesian mechanistic model of
COVID-19 transmission dynamics including the effect of vaccination (currently
under preparation)
Blecua, J.,
Fernandez-Recio, J., Gutierrez, J.M. (2024) A Probabilistic Description of the
Impact of Vaccine-Induced Immunity in the Dynamics of COVID-19 Transmission.
Open Journal of Modelling and Simulation, 12, 59-73. doi:
10.4236/ojmsi.2024.122004.
Blecua, J. (2021) Optimisation
of a COVID-19 transmission model for its application to multiple intervention
periods (Master Thesis, Universitat Oberta de Catalunya)
Fernandez-Recio, J.
(2020) Modelling the Evolution of COVID-19
in High-Incidence European Countries and Regions: Estimated Number of
Infections and Impact of Past and Future Intervention Measures. 2020 J. Clin. Med. 9, no. 6, 1825
Flaxman, S., Mishra, S., Gandy, A. et
al. (2020) Estimating the effects of
non-pharmaceutical interventions on COVID-19 in Europe. 2020 Nature 584, pp. 257-261
