Disease mapping for spatially semi-continuous data by estimating equations with application to dengue control.
Stat Med
; 42(20): 3636-3648, 2023 09 10.
Article
en En
| MEDLINE
| ID: mdl-37316997
ABSTRACT
Disease mapping is a research field to estimate spatial pattern of disease risks so that areas with elevated risk levels can be identified. The motivation of this article is from a study of dengue fever infection, which causes seasonal epidemics in almost every summer in Taiwan. For analysis of zero-inflated data with spatial correlation and covariates, current methods would either cause a computational burden or miss associations between zero and non-zero responses. In this article, we develop estimating equations for a mixture regression model that accommodates spatial dependence and zero inflation for study of disease propagation. Asymptotic properties for the proposed estimates are established. A simulation study is conducted to evaluate performance of the mixture estimating equations; and a dengue dataset from southern Taiwan is used to illustrate the proposed method.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Dengue
/
Epidemias
Tipo de estudio:
Risk_factors_studies
Aspecto:
Patient_preference
Límite:
Humans
País/Región como asunto:
Asia
Idioma:
En
Revista:
Stat Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
Taiwán
Pais de publicación:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM