Geographically weighted temporally correlated logistic regression model

被引:30
|
作者
Liu, Yang [1 ,2 ]
Lam, Kwok-Fai [3 ]
Wu, Joseph T. [2 ]
Lam, Tommy Tsan-Yuk [1 ,2 ]
机构
[1] Univ Hong Kong, Ctr Influenza Res, State Key Lab Emerging Infect Dis, Pokfulam, Hong Kong, Peoples R China
[2] Univ Hong Kong, Sch Publ Hlth, Pokfulam, Hong Kong, Peoples R China
[3] Univ Hong Kong, Dept Stat & Actuarial Sci, Pokfulam, Hong Kong, Peoples R China
来源
SCIENTIFIC REPORTS | 2018年 / 8卷
关键词
2-STEP ESTIMATION; INFLUENZA;
D O I
10.1038/s41598-018-19772-6
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:14
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