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Predicting Ross River virus infection by analysis of seroprevalence data

journal contribution
posted on 2019-05-13, 00:00 authored by JB Sinclair, Narayan GyawaliNarayan Gyawali, Andrew Taylor-Robinson
Infection with arthropod-borne (arbo)viruses presents a significant and growing public health threat to the resident population of Queensland (QLD), the north-eastern state of Australia. Clinical infection with Ross River virus (RRV) is the most commonly detected, and arguably most debilitating, of Australia’s 75 known indigenous arbovirus species. Development of prediction models to forecast arbovirus epidemics aims to provide accurate and reliable tools that may facilitate planned interventions by local and state authorities to curb disease transmission. Acute immunoglobulin (Ig)M-positive enzyme-linked immunosorbent assay results are often misleading, with interpretation cautioned. As such, this serological testing was recently excluded as a means to confirm cases of arbovirus infection in Australia. The purpose of this study was to investigate the seroepidemiological value of acute IgM-positive results across QLD by correlating with RRV case reports and to develop a mathematical model to predict RRV outbreaks. Blood samples from patients throughout QLD suspected of arboviral infection were tested for RRV, with numbers for various serology results grouped by geographical region. The serology data were compared with case reports for each respective region by multiple regression in order to determine any relationships. RRV IgM-positive results correlated significantly to the number of case reports per region (P < 0.05). An estimated multiple regression equation was used to predict RRV case reports from a subset of data extracted for the period December 2015 and January 2016. Predicted cases based on IgM-positive/IgG-negative serology showed no significant correlation to the respective case reports for each region (P > 0.05). Hence, these findings failed to validate the potential use of IgM-positive seroprevalence to predict RRV infection with sufficient accuracy for diagnostic purposes. A possible indirect value may exist, however, in analysing pooled seroprevalence data, which may better inform concurrent surveillance measures and thereby enhance the accuracy of RRV outbreak forecasts.






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Science and Education Publishing

Additional Rights

CC BY 4.0

Peer Reviewed

  • Yes

Open Access

  • Yes

External Author Affiliations

School of Biological Sciences, University of Queensland; QIMR Berghofer Medical Research Institute,

Era Eligible

  • Yes


American Journal of Infectious Diseases and Microbiology