DAILY REPORT – APRIL 15

It is expected that the number of cases will continue to grow in the different provinces of Andalusia. The provinces with the highest number of cases will be Seville, Granada and Malaga.

HISTORICAL AND PREDICTION OF INFECTED CASES

PREDICTION OF NEW INFECTED CASES

 

 

PEAK UCI ESTIMATION

COMPARISON BETWEEN HEALT PROTOCOLS AND UCI CASES ESTIMATION

 

If the 9,000 Plan had been maintained, it would probably have been necessary to strengthen the ICUs available in some provinces. This justifies the activation of Plan 15,000 on 8 April, which will allow the Andalusian Health System (SAS) to cover all the cases of infected people foreseen by the mathematical model with sufficient notice and guarantees.

SUMMARY REPORT – APRIL 16

The provinces most infected are Malaga, Seville and Granada. In the following table can be consulted the official data provided by the Regional Government of Andalusia .

PROVINCEINFECTEDICUDEATHSRECOVERED
ALMERIA428353890
CADIZ1,0727168264
CORDOBA1,2476263301
GRANADA1,926117187480
HUELVA337302873
JAEN1,20162128252
MALAGA2,363156211772
SEVILLE2,2223138189402

APPLIED MODEL

SIR MODEL

The SIR (Susceptible-Infected-Recovered) model is a mathematical model commonly used in epidemiology since it is capable of capturing many of the typical characteristics of epidemic outbreaks. The simplicity of this model is based on three assumptions. Firstly, the population is fixed during the analysis. Secondly, there is no any socioeconomic characteristic that could influence in the probability of being infected. Thirdly, there is no inherited immunity.

Monte Carlo Database Generation:  In order to obtain a set of SIR models that can be approximated to the real curves, a set of data is generated by altering the model’s β , γ and porcentaje of population variables. In this manner, with a random set of parameters is drawn from a uniform distribution, which is used to develop a unique SIR model that can be identified that correspond to the real curves of the pandemic in the different regions of Andalusia.

Deep Neural Network: The purpose of the development of the DNN is to estimate the optimal parameters for the data of infected people in the different regions of Andalusia. For the development of the DNN, the period between 03/03/2020 and 03/04/2020 was considered as the period in which the growth of infected people in Andalusia was observed.

CONTACT INFORMATION

Team:

Pedro Rodríguez Cortés, Head of LOYOLATECH. prodriguez@uloyola.es

Nicholas Gregory Baltas, PhD Fellow. ngbaltas@uloyola.es

Laura Ríos Pena, Senior Research. lrios@uloyola.es

Francisco A. Prieto, PhD Fellow. faprietorodriguez@al.uloyola.es

Acknowledgements: 

M. Frantzi – German Research Center Mosaiques Diagnostics & Therapeutics.

Carlos García – Vice-Rector of Research at Loyola University

Address:

Universidad Loyola Andalucía. Avda. de las Universidades s/n Dos Hermanas, Sevilla, España.

LINKS OF INTEREST

World Health OrganizationLatest updates and recommendations

COVID-19 evolution dashboard around the world

Protein Data Bank Structure of COVID-19 main protease

National Institute of Allergy and Infectious Diseases

National Institute of Health