Research Interests
Peihua Qiu's Research on Spatio-Temporal Modeling and Monitoring
To monitor the incidence rates of cancers, AIDS, cardiovascular diseases, and
other chronicle or infectious diseases, some global, national and regional
reporting systems have been built to collect/provide population-based data
about the disease incidence. Such databases usually report daily, monthly,
or yearly disease incidence numbers at the city, county, state or country
level, and the disease incidence numbers collected at different places and
different times are correlated: with the ones closer in place or time being
more correlated. The correlation reflects the impact of various confounding
risk factors, such as weather, demographic factors, life styles, and other
cultural and environmental factors. Because such impact is complicated and
challenging to describe, the spatio-temporal (ST) correlation in the observed
disease incidence data has complicated ST structure as well. Furthermore,
the ST correlation is hidden in the observed data, and cannot be observed
directly. In the literature, there has been much discussion about ST disease
incidence data modeling. But, the existing methods either impose various
assumptions on the ST correlation that are difficult to justify, or ignore
partially or entirely the ST correlation. We are in the process of developing
flexible and effective new methods for ST disease incidence data modeling,
using nonparametric local smoothing methods. These methods can properly
accommodate the ST correlation. Based on these modeling results, we will
also develop statistical online monitoring methods for spatio-temporal
disease surveillance. Some preliminary studies have been discussed in
Zhang, Kang, Yang and Qiu (2015) and Zhang, Qiu and Chen (2016).
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