Predictive modelling of Ross River virus using climate data in the Darling Downs

Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for...

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Bibliographic Details
Published in:Epidemiology and infection 2023-03, Vol.151, p.e55-e55, Article e55
Main Authors: Meadows, Julia, McMichael, Celia, Campbell, Patricia T.
Format: Article
Language:English
Subjects:
Alphavirus Infections - epidemiology
Animals
Arthralgia
Australia - epidemiology
Climate
Climate change
Climatic data
Datasets
Disease
Epidemics
Humans
Humidity
Infections
Lethargy
Mosquito Vectors
Mosquitoes
Original Paper
Outbreaks
Precipitation
Prediction models
Public health
Rain
Regression analysis
Regression models
Rivers
RNA viruses
Ross River virus
Statistical methods
Variables
Vectors
Viruses
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Summary:Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 – June 2014 were used for model training. Model predictions were tested using data for July 2014 – June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.
ISSN:0950-2688
1469-4409
DOI:10.1017/S0950268823000365