TY - GEN
T1 - A Bayesian Joint Spatio-temporal Model for Multiple Mosquito-Borne Diseases
AU - Pavani, Jessica
AU - Moraga, Paula
N1 - KAUST Repository Item: Exported on 2022-12-15
PY - 2022/11/27
Y1 - 2022/11/27
N2 - Many infectious diseases studied in the epidemiological context are caused by insects, mainly mosquitoes. These infections are known as arboviruses because they need vectors to be transmitted. Some of them may be related to each other since a same mosquito species can transmit different diseases. This study aims to describe geographic and temporal patterns of two mosquito-borne diseases, dengue and chikungunya, and their possible risk factors in the Brazilian state of Ceará in 2017. To pursue this, we consider a Bayesian hierarchical spatio-temporal model for the joint analysis of both arboviruses. This specification also uses a Zero-Inflated Poisson (ZIP) model to overcome the high proportion of zeros. Moreover, it includes covariates as well as disease-specific and shared spatial and temporal effects, which are estimated and mapped to identify similarities among diseases. Our findings help understand geographic and temporal disease patterns, and to identify high risk areas and potential risk factors, and can inform the development and implementation of strategies for disease prevention and control.
AB - Many infectious diseases studied in the epidemiological context are caused by insects, mainly mosquitoes. These infections are known as arboviruses because they need vectors to be transmitted. Some of them may be related to each other since a same mosquito species can transmit different diseases. This study aims to describe geographic and temporal patterns of two mosquito-borne diseases, dengue and chikungunya, and their possible risk factors in the Brazilian state of Ceará in 2017. To pursue this, we consider a Bayesian hierarchical spatio-temporal model for the joint analysis of both arboviruses. This specification also uses a Zero-Inflated Poisson (ZIP) model to overcome the high proportion of zeros. Moreover, it includes covariates as well as disease-specific and shared spatial and temporal effects, which are estimated and mapped to identify similarities among diseases. Our findings help understand geographic and temporal disease patterns, and to identify high risk areas and potential risk factors, and can inform the development and implementation of strategies for disease prevention and control.
UR - http://hdl.handle.net/10754/686416
UR - https://link.springer.com/10.1007/978-3-031-16427-9_7
U2 - 10.1007/978-3-031-16427-9_7
DO - 10.1007/978-3-031-16427-9_7
M3 - Conference contribution
SN - 9783031164262
SP - 69
EP - 77
BT - Springer Proceedings in Mathematics & Statistics
PB - Springer International Publishing
ER -
