COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave

Estimation of the Real Incidence of a Contagious Disease Through a Bayesian Multilevel Model: Study of COVID-19 in Spanish Provinces

BACKGROUND

Pandemic outbreaks have emerged as a significant global threat, with the potential to cause waves of infections that challenge public health systems and disrupt societal norms. Understanding the underlying behavior of disease transmission can be of great use in the design of informed and timely public health policies. It is very common for many contagious diseases not to have actual incidence but rather incidence in a given subgroup. For example, in Spain, as of 28 March 2022, the incidence of COVID-19 in people under 60 years of age is not registered.

METHODS

This work provides a Bayesian methodology to model the incidence of any infectious disease in the general population when its cases are only registered in a specific subgroup of that population. The case study used was the coronavirus disease (COVID-19), with data for 52 Spanish provinces during the period of 1 January 2020 to 29 August 2022.

RESULTS

Explicitly, two multilevel models were proposed, one for people over or of 60 years of age and the other for people under 60 years of age. Performance of the models was 5.9% and 12.7% MAPE, respectively.

CONCLUSIONS

Despite the limitations of the data and the complexity and uncertainty in the propagation of COVID-19, the models were able to fit the data well and predict incidence with very low MAPE.

Predicting COVID-19 pandemic waves including vaccination data with deep learning

Introduction

During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear.

Methods

We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines.

Results

We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions.

Discussion

Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.