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The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia
Citation
Koolhof, IS and Gibney, KB and Bettiol, SS and Charleston, M and Wiethoelter, A and Arnold, A-L and Campbell, PT and Neville, PJ and Aung, P and Shiga, T and Carver, S and Firestone, SM, The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia, Epidemics ISSN 1755-4365 (2019) [Refereed Article]
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Copyright Statement
© 2019 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) http://creativecommons.org/licenses/by-nc-nd/4.0/
DOI: doi:10.1016/j.epidem.2019.100377
Abstract
Ross River virus (RRV) is Australia’s most epidemiologically important mosquito-borne disease.
During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications can
account for up to 30% of national RRV notifications. However, little is known about factors which can
forecast RRV transmission in Victoria. We aimed to understand factors associated with RRV
transmission in epidemiologically important regions of Victoria and establish an early warning
forecast system. We developed negative binomial regression models to forecast human RRV
notifications across 11 Local Government Areas (LGAs) using climatic, environmental, and
oceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 to
June 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing data
set. Evapotranspiration and precipitation were found to be common factors for forecasting RRV
notifications across sites. Several site-specific factors were also important in forecasting RRV
notifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreak
in 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreak
in 2016/17, however only six LGAs could predict the outbreak using the same model. We document
similarities and differences in factors useful for forecasting RRV notifications across Victoria and
demonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexity
of RRV transmission where additional epidemiological information is needed to accurately predict
RRV activity. Our findings have been applied to produce a Ross River virus Outbreak Surveillance
System (ROSS) to aid in public health decision making in Victoria.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | epidemiology, epidemic, forecasting, mosquito-borne disease, public health management |
| Research Division: | Medical and Health Sciences |
| Research Group: | Public Health and Health Services |
| Research Field: | Health Information Systems (incl. Surveillance) |
| Objective Division: | Health |
| Objective Group: | Public Health (excl. Specific Population Health) |
| Objective Field: | Disease Distribution and Transmission (incl. Surveillance and Response) |
| UTAS Author: | Koolhof, IS (Mr Iain Koolhof) |
| UTAS Author: | Bettiol, SS (Dr Silvana Bettiol) |
| UTAS Author: | Charleston, M (Professor Michael Charleston) |
| UTAS Author: | Carver, S (Dr Scott Carver) |
| ID Code: | 135624 |
| Year Published: | 2019 |
| Web of Science® Times Cited: | 1 |
| Deposited By: | Medicine |
| Deposited On: | 2019-11-06 |
| Last Modified: | 2020-04-20 |
| Downloads: | 3 View Download Statistics |
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