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Seasonal forecasting and health impact models: challenges and opportunities

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Seasonal forecasting and health impact models: challenges and opportunities. / Ballester, Joan; Lowe, Rachel; Diggle, Peter John et al.
In: Annals of the New York Academy of Sciences, Vol. 1382, No. 1, 10.2016, p. 8-20.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ballester, J, Lowe, R, Diggle, PJ & Rodo, X 2016, 'Seasonal forecasting and health impact models: challenges and opportunities', Annals of the New York Academy of Sciences, vol. 1382, no. 1, pp. 8-20. https://doi.org/10.1111/nyas.13129

APA

Ballester, J., Lowe, R., Diggle, P. J., & Rodo, X. (2016). Seasonal forecasting and health impact models: challenges and opportunities. Annals of the New York Academy of Sciences, 1382(1), 8-20. https://doi.org/10.1111/nyas.13129

Vancouver

Ballester J, Lowe R, Diggle PJ, Rodo X. Seasonal forecasting and health impact models: challenges and opportunities. Annals of the New York Academy of Sciences. 2016 Oct;1382(1):8-20. Epub 2016 Jul 18. doi: 10.1111/nyas.13129

Author

Ballester, Joan ; Lowe, Rachel ; Diggle, Peter John et al. / Seasonal forecasting and health impact models : challenges and opportunities. In: Annals of the New York Academy of Sciences. 2016 ; Vol. 1382, No. 1. pp. 8-20.

Bibtex

@article{9bcc94dccc1347cca7107cf4170b9927,
title = "Seasonal forecasting and health impact models: challenges and opportunities",
abstract = "After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real-time climate and health data collection, state-of-the-art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.",
keywords = "climate variability, seasonal forecasting, climate services, health impacts, statistical models",
author = "Joan Ballester and Rachel Lowe and Diggle, {Peter John} and Xavier Rodo",
year = "2016",
month = oct,
doi = "10.1111/nyas.13129",
language = "English",
volume = "1382",
pages = "8--20",
journal = "Annals of the New York Academy of Sciences",
issn = "0077-8923",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Seasonal forecasting and health impact models

T2 - challenges and opportunities

AU - Ballester, Joan

AU - Lowe, Rachel

AU - Diggle, Peter John

AU - Rodo, Xavier

PY - 2016/10

Y1 - 2016/10

N2 - After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real-time climate and health data collection, state-of-the-art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.

AB - After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real-time climate and health data collection, state-of-the-art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.

KW - climate variability

KW - seasonal forecasting

KW - climate services

KW - health impacts

KW - statistical models

U2 - 10.1111/nyas.13129

DO - 10.1111/nyas.13129

M3 - Journal article

VL - 1382

SP - 8

EP - 20

JO - Annals of the New York Academy of Sciences

JF - Annals of the New York Academy of Sciences

SN - 0077-8923

IS - 1

ER -