Modeling Spatial Heterogeneity with Excess Zeroes from School Absenteeism dSata | Zendy

Xiaoxiao Song | Zendy; Qi ﻿﻿Zhao | Zendy; Changming Zhou | Zendy; Tao Tao | Zendy; Lars Palm | Zendy; Vinod Diwan | Zendy; Hui Yuan | Zendy; Biao Xu | Zendy

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Modeling Spatial Heterogeneity with Excess Zeroes from School Absenteeism dSata

Author(s) -

Xiaoxiao Song,

Qi Zhao,

Changming Zhou,

Tao Tao,

Lars Palm,

Vinod Diwan,

Hui Yuan,

Biao Xu

Publication year - 2015

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v7i1.5827

Subject(s) - absenteeism , construct (python library) , set (abstract data type) , public health , computer science , econometrics , spatial heterogeneity , geography , data set , random effects model , environmental health , cartography , data science , medicine , psychology , mathematics , artificial intelligence , ecology , biology , social psychology , nursing , programming language , meta analysis

Absenteeism has great advantages in promoting the early detection of epidemics. The spatial patterns of the data generally are polytropy and heterogeneity. The public health experts pay more attention to whether an outbreak will happen or/and how large the epidemic will be of school absenteeism data in spatial patterns. We construct simultaneously two set of random effects (u1, u2) in RE-ZIP to quantify this two kind spatial heterogeneity for 62 schools.

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