Data

Forecast of Barmah Forest Virus disease in relation to variations of temperature and rainfall

Queensland University of Technology
Adjunct Professor Shilu Tong (Associated with) Distinguished Professor Kerrie Mengersen (Associated with) Dr Sue Naish (Associated with) Professor Wenbiao Hu (Associated with)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://goo.gl/4g6uFg&rft.title=Forecast of Barmah Forest Virus disease in relation to variations of temperature and rainfall&rft.identifier=10378.3/8085/1018.16064&rft.publisher=Queensland University of Technology&rft.description=Data was obtained on notified Barmah Forest Virus (BFV) cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the potential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The figure shows: (a) Geographical distribution of BFV disease under current climatic conditions for Queensland's entire coastal regions, (b) forecast of potential probabilities of risk of BFV disease under climate change scenarios for 2025, (c) 2050 and (d) 2100.&rft.creator=Anonymous&rft.date=2015&rft.relation=http://eprints.qut.edu.au/67158/&rft.coverage=153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730&rft_rights=© 2013 Naish et al.&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/4.0/&rft_subject=Infectious disease modeling&rft_subject=Infectious disease epidemiology&rft_subject=ECOLOGY&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Population modeling&rft_subject=Computational biology&rft_subject=Public health&rft_subject=Infectious diseases&rft_subject=Epidemiology&rft_subject=Zoonoses&rft_subject=Barmah Forest Virus&rft_subject=rainfall&rft_subject=BFV&rft_subject=Population biology&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/4.0/

© 2013 Naish et al.

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Contact Information

Postal Address:
Distinguished Professor Kerrie Mengersen
Ph: +61 7 3138 2063
Fax: +61 7 3138 2310

k.mengersen@qut.edu.au

Full description

Data was obtained on notified Barmah Forest Virus (BFV) cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the potential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios.
The figure shows: (a) Geographical distribution of BFV disease under current climatic conditions for Queensland's entire coastal regions, (b) forecast of potential probabilities of risk of BFV disease under climate change scenarios for 2025, (c) 2050 and (d) 2100.

Data time period: 2000 to 2008

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153.55292,-9.92973 137.99458,-9.92973 137.99458,-29.17859 153.55292,-29.17859 153.55292,-9.92973

145.7737475,-19.554159

Subjects
BFV | Biological Sciences | Barmah Forest Virus | Computational biology | Ecology | Epidemiology | Infectious disease epidemiology | Infectious disease modeling | Infectious diseases | Population biology | Population modeling | Public health | Zoonoses | rainfall |

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  • Local : 10378.3/8085/1018.16064
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