| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A Bayesian piecewise survival cure rate model for spatially clustered data
|
Hurtado Rúa, Sandra M.
|
|
2019
|
29 |
C |
p. 149-159
|
artikel
|
| 2 |
A Bayesian spatio-temporal analysis on racial disparities in hypertensive disorders of pregnancy in Florida, 2005–2014
|
Hu, Hui
|
|
2019
|
29 |
C |
p. 43-50
|
artikel
|
| 3 |
A data-driven approach for estimating the change-points and impact of major events on disease risk
|
Carroll, R.
|
|
2019
|
29 |
C |
p. 111-118
|
artikel
|
| 4 |
Deep learning for supervised classification of spatial epidemics
|
Augusta, Carolyn
|
|
2019
|
29 |
C |
p. 187-198
|
artikel
|
| 5 |
Editorial Board
|
|
|
2019
|
29 |
C |
p. ii
|
artikel
|
| 6 |
Estimating the health impact of air pollution in Scotland, and the resulting benefits of reducing concentrations in city centres
|
Lee, Duncan
|
|
2019
|
29 |
C |
p. 85-96
|
artikel
|
| 7 |
Exploring socio-demographic-and geographical-variations in prevalence of diabetes and hypertension in Bangladesh: Bayesian spatial analysis of national health survey data
|
Roy, Paritosh K.
|
|
2019
|
29 |
C |
p. 71-83
|
artikel
|
| 8 |
Geography and patient history in long-term lipid lowering medication adherence for primary prevention of cardiovascular disease
|
Hu, Feiyu
|
|
2019
|
29 |
C |
p. 13-29
|
artikel
|
| 9 |
Identifying determinants of obesity in Athens, Greece through global and local statistical models
|
Faka, Antigoni
|
|
2019
|
29 |
C |
p. 31-41
|
artikel
|
| 10 |
Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to visceral leishmaniasis data in Brazil
|
Boaz, R.
|
|
2019
|
29 |
C |
p. 177-185
|
artikel
|
| 11 |
Is active travel a breath of fresh air? Examining children's exposure to air pollution during the school commute
|
Gilliland, Jason
|
|
2019
|
29 |
C |
p. 51-57
|
artikel
|
| 12 |
Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity
|
Zahrieh, David
|
|
2019
|
29 |
C |
p. 97-109
|
artikel
|
| 13 |
Small-area estimates of stunting. Mexico 2010: Based on a hierarchical Bayesian estimator
|
Catalán Nájera, Héctor E.
|
|
2019
|
29 |
C |
p. 1-11
|
artikel
|
| 14 |
Spatially-explicit survival modeling with discrete grouping of cancer predictors
|
Onicescu, Georgiana
|
|
2019
|
29 |
C |
p. 139-148
|
artikel
|
| 15 |
Spatial smoothing models to deal with the complex sampling design and nonresponse in the Florida BRFSS survey
|
Watjou, K.
|
|
2019
|
29 |
C |
p. 59-70
|
artikel
|
| 16 |
Special Issue on Spatial Survival from GEOMED 2017
|
|
|
2019
|
29 |
C |
p. 119
|
artikel
|
| 17 |
Special Issue: Surveillance and Zoonosis
|
|
|
2019
|
29 |
C |
p. 161
|
artikel
|
| 18 |
Where did I get dengue? Detecting spatial clusters of infection risk with social network data
|
Souza, Roberto C.S.N.P.
|
|
2019
|
29 |
C |
p. 163-175
|
artikel
|
| 19 |
Zero-augmented accelerated spatial failure model for modeling hospital length of stay data
|
Feng, Cindy Xin
|
|
2019
|
29 |
C |
p. 121-137
|
artikel
|
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