TY - GEN
T1 - Efficient Gaussian process-based inference for modelling spatio-temporal dengue fever
AU - Albinati, Julio
AU - Meira, Wagner
AU - Pappa, Gisele L.
AU - Wilson, Andrew G.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - Dengue fever is a disease that affects hundreds of millions of people every year worldwide. Despite its wide presence around the world, it still requires accurate early warning systems. In this paper, we propose an accurate model to forecast dengue fever incidence at hundreds of Brazilian cities simultaneously. In order to assure efficiency, we devise two strategies to reduce computational effort required for inference under the proposed model. As a result, we not only reduce the computational effort that would be required to fit each model per city, but also increase the accuracy by inducing spatial dependences between cities. These dependences do not require human specification and are learned from data, leading to more accurate predictions than using typical neighborhood or distance-based methods.
AB - Dengue fever is a disease that affects hundreds of millions of people every year worldwide. Despite its wide presence around the world, it still requires accurate early warning systems. In this paper, we propose an accurate model to forecast dengue fever incidence at hundreds of Brazilian cities simultaneously. In order to assure efficiency, we devise two strategies to reduce computational effort required for inference under the proposed model. As a result, we not only reduce the computational effort that would be required to fit each model per city, but also increase the accuracy by inducing spatial dependences between cities. These dependences do not require human specification and are learned from data, leading to more accurate predictions than using typical neighborhood or distance-based methods.
KW - Gaussian process
KW - dengue fever
KW - spatio-temporal modelling
UR - http://www.scopus.com/inward/record.url?scp=85049497006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049497006&partnerID=8YFLogxK
U2 - 10.1109/BRACIS.2017.13
DO - 10.1109/BRACIS.2017.13
M3 - Conference contribution
AN - SCOPUS:85049497006
T3 - Proceedings - 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017
SP - 61
EP - 66
BT - Proceedings - 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th Brazilian Conference on Intelligent Systems, BRACIS 2017
Y2 - 2 October 2017 through 5 October 2017
ER -