Geographically weighted temporally correlated logistic regression model

Detecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data,...

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Bibliographic Details
Published in:Scientific reports 2018-01, Vol.8 (1), p.1417-14, Article 1417
Main Authors: Liu, Yang, Lam, Kwok-Fai, Wu, Joseph T., Lam, Tommy Tsan-Yuk
Format: Article
Language:English
Subjects:
639/705/531
692/699/255/1578
Disease
Epidemics
Epidemiology
Generalized linear models
Geographic information science
Geography
Humanities and Social Sciences
Humans
Influenza
Influenza, Human - epidemiology
Laboratories
Logistic Models
Models, Theoretical
multidisciplinary
Public health
Regression analysis
Science
Science (multidisciplinary)
Spatial Regression
Temporal variations
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Summary:Detecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference. The local likelihood method is adopted to estimate the spatial relationship, while the smoothing method is employed to estimate the temporal variation. We present the construction and implementation of GWTCLR and the study of the asymptotic properties of the proposed estimator. Simulation studies were conducted to evaluate the robustness of the proposed model. GWTCLR was applied on real epidemiologic data to study the climatic determinants of human seasonal influenza epidemics. Our method obtained results largely consistent with previous studies but also revealed certain spatial and temporal varying patterns that were unobservable by previous models and methods.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-19772-6