The title of his talk is Estimation of the Transmission Dynamics in Spain Using a Stochastic

Simulator and Black Box Optimization Techniques.

Please, Marcos, go ahead.

Okay, thank you very much for the invitation.

Hello, everybody.

My name is Marcos Matagona and today I will speak about my recent work on COVID-19.

Before we start with the contents of the talk, I provide a brief summarize of the contents

of the talk.

First, I provide a motivation and a list of the details about the modeling strategy.

Then I summarize briefly the general depicting literature and then we include the formal

aspect of formal model with the mathematical details and then I introduce the results of

the application of our model in Spain.

Finally, I provide some insights about medicine that is my current research topic.

Our modeling goals are twofold.

First, we are interested in developing a mathematical tool to estimate and monitor the current epidemic

and state.

For this, we are interested in the dynamic of the pandemic.

In a retrospective sense, we are more interested in the construct of the dynamic of COVID-19

infections in Spain with the following purpose.

First, we can understand the past and the future and then do better decisions in the

future.

Also, we can create a formal tool to guide, for example, epidemiological interventions.

For example, we are interested in determining specific epidemiological and more advanced

and personal anxiety epidemiological details, for example, to establish local lockdowns.

In this talk, we illustrate the mobile behavior with aims to answer the forming epidemiological

questions.

In the first ways, in order to know the position of the population to the virus, we can estimate

the number of infections.

In addition, we can know what is the peak of new infections happening.

In the overall of the Spanish population, until March of this year, we are interested

in determining also the number of infections.

For example, in October, when some experts discuss about the application of a national

lockdown, we can estimate the real epidemic situation at that time.

Finally, as our modeling study allows us to determine the real number of infections dynamically,

we can assess as was the epidemiological control performed by the Spanish government.

Below, we summarize our modeling strategy.

Our modeling strategy is based on solved, in-vest problem approach using mortality records.

We use mortality records as a source of information because it's a more reliable information,

so than the other epidemiological indicators.

For this purpose, we assume that the dynamic of infection following the next probabilistic

mark of modern.

We divide the population in several compartments and then we support the transition between

the model are given by a finite mark of change.

The time transitions between different model compartments are given by a parametric time

distributions.

The parameters related with the transition of the model and the time distributions are

according to the epidemic evidence and with the in-vest approach, we estimate the daily

mechanism of new infections.

In particular, we assume that the patients belonging to the states A1, A2, A3 and R1

interact with the susceptible paper, the nodes with the states, according to an individual