[ABE-L] Seminário DEST/UFMG em 31/03/2023

[Marcos Prates]

Caros,

Na próxima sexta-feira (31 de julho, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do prof. Jorge
Mateu.

O Prof. Jorge é professor Titular do departamento de Matemática da
Universitat Jaume I de Castellón. Possui graduação, mestrado e doutorado em
Estatística pela Universitat de Valencia. Suas áreas de pesquisa são:
Estatística Espacial e Processos Pontuais Espaço Temporal.

Título: Statistical models for the analysis, prediction and monitoring of
space-time data. Applications to infectious diseases and crime

Resumo:
We present several statistical approaches to understand the underlying
temporal and spatial dynamics of infectious diseases (with a focus on
Covid-19 data) that can result in informed and timely public health
policies. Most studies in the context of infectious diseases commonly
report figures of the overall infection at a state- or county-level,
reporting the aggregated number of cases in a particular region at one
time. However, we focus on analysing high-resolution Covid-19 datasets in
form of spatio-temporal point patterns, offering vital insights for the
spatio-temporal interaction between individuals concerning the disease
spread in a metropolis.

We develop a non-stationary spatio-temporal point process, assuming that
previously infected cases trigger newly confirmed ones, and introduce
a neural network-based kernel to capture the spatially varying triggering
effect. The neural network-based kernel is carefully crafted to
enhance expressiveness while maintaining results interpretability. We also
incorporate some exogenous influences imposed by city landmarks.
Additionally, we propose some mechanistic models giving particular
data-driven forms to the spatio-temporal intensity function. Particular
cluster spatio-temporal models to identify unknown parents are also
depicted. For  completeness, we present a method to evaluate the direction
and velocities of the spread
by considering the intensity comes from a growth differential equation.

Crime science deals with the analysis of crime data from many perspectives.
This type of data brings up a large variety of problems linked with data
science and big data analysis. In general grounds, crime data provides
heterogeneous patterns in space and time, and we present  methods able
to handle this heterogeneity. In particular, we consider statistical models
to detect generators of crime in cities together with potential
focuses that attract or inhibit crimes in a spatio-temporal region. We also
consider methods to reduce potential large dimensionality in the data, and
some artificial intelligent methods to help handling large amounts of crime
data. Two final crucial probabilistic models will be presented. One
is modelling crime data using stochastic point pattern processes, such as
log-Gaussian Cox processes, that will be used to forecast and predict
risk of crimes in subregions of space and time of a city. This aspect will
be complemented with another type of stochastic models with differential.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/@seminariosdest-ufmg>".

https://www.youtube.com/@seminariosdest-ufmg

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