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Title: The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia.
Austin Authors: Koolhof, Iain S;Gibney, Katherine B;Bettiol, Silvana;Charleston, Michael;Wiethoelter, Anke;Arnold, Anna-Lena;Campbell, Patricia T;Neville, Peter J;Aung, Phyo;Shiga, Tsubasa;Carver, Scott;Firestone, Simon M
Affiliation: Victorian Department of Health and Human Services, Communicable Disease Epidemiology and Surveillance, Health Protection Branch, Melbourne, Victoria, Australia
The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia
College of Sciences and Engineering, School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
Department of Health, Western Australia, Public and Aboriginal Health, Environmental Health Directorate, Perth, Western Australia, Australia
College of Health and Medicine, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia
Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria, Australia
Issue Date: Mar-2020 2019-11-05
Publication information: Epidemics 2020; 30: 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.
DOI: 10.1016/j.epidem.2019.100377
PubMed URL: 31735585
Type: Journal Article
Subjects: Arboviruses
Mosquito-borne disease
Predicting epidemics
Appears in Collections:Journal articles

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