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 .

PROVINCE	INFECTED	ICU	DEATHS	RECOVERED
ALMERIA	428	35	38	90
CADIZ	1,072	71	68	264
CORDOBA	1,247	62	63	301
GRANADA	1,926	117	187	480
HUELVA	337	30	28	73
JAEN	1,201	62	128	252
MALAGA	2,363	156	211	772
SEVILLE	2,2223	138	189	402

APPLIED MODEL

COVID 19 Impact Estimation on ICU Capacity at Andalusia Spain Using Artiﬁcial Intelligence – Version 1 Descarga

COVID-19 Impact Estimation on ICU Capacity at Andalusia, Spain, Using Artiﬁcial Intelligence – Version 2 Descarga

Monte-Carlo-Deep-Neural-Network-Model-for-Spread-and-Peak-Prediction-of-COVID-19 Descarga

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.