A pinboard by
Eloise Stephenson

PhD Candidate, Griffith University


Better understanding how animal ecology drives more than 75% of new infectious diseases in humans.

Despite being Australia’s most common mosquito-borne disease, the transmission dynamics of Ross River virus remain vague. Ross River virus is a zoonotic mosquito-borne disease, meaning a mosquito generally bites an infected animal before biting a human. Management of this debilitating disease is currently limited to the management of mosquito populations. Although this can help, it does not prevent outbreaks of the disease. As such, this research aims to better understand what drives outbreaks of Ross River virus, in particular, the role non-human species play in the maintenance and spread of the disease. This new information can help guide more effective management strategies for Ross River virus, in turn lowering the number of human infections maintaining a healthier population.

This study is highly significant because:

  • It addresses a central unresolved issue of Australia’s largest mosquito-borne disease.
  • The proposed research is important internationally because it addresses the question of identifying non-human reservoirs for communicable diseases world-wide.
  • It will be the first to attempt mosquito-borne disease surveillance in non-human species through a network of veterinary clinics. This has the potential to provide a framework for other disease investigations.

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models.

Abstract: Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.

Pub.: 11 Jan '17, Pinned: 27 Jul '17

Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.

Abstract: Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.

Pub.: 21 Dec '16, Pinned: 27 Jul '17

Clinical presentation, progression and management of 5 cases of Ross River virus infection in performance horses located in southeast Queensland: A longitudinal case series

Abstract: Publication date: Available online 6 January 2017 Source:Journal of Equine Veterinary Science Author(s): A.J. Barton, H. Bielefeldt-Ohmann Background Ross River virus (RRV), a mosquito-transmitted alphavirus prevalent in Australia, is believed to cause poor performance, lethargy and muscle stiffness in Australian horses. However, disease progression and management is poorly documented. A better understanding of disease presentation, acute therapy and long-term management is required. Objectives To describe clinical presentation, diagnosis, acute treatment and long term management of RRV-infection in horses Study design Retrospective case series Methods Clinical and diagnostic data were obtained from both veterinary records and owner interviews for 5 performance horses that presented with acute poor performance coupled with serological evidence of RRV exposure. Clinical and owner reports were evaluated from the time of presentation until the horses appeared asymptomatic and had returned to normal performance. Results RRV was suspected to be the cause of generalized muscle stiffness and poor performance in 5 performance horses located in southeast Queensland between 2011 and 2015. Clinical symptoms included pyrexia, tachypnoea, exercise intolerance, generalized muscle stiffness, synovial effusion, and oedema of the lower limbs. Serological investigations (ELISA and/or virus neutralization assay) detected antibody responses to RRV. Horses were treated with non-steroidal anti-inflammatory drugs (n=5) and disease-modifying osteoarthritis drugs (n=2). Most horses returned to previous athletic capabilities between 7 and 12 months after onset of symptoms. Main limitations Not all horses in the study had pre-clinical serology or submitted paired blood samples for serology, meaning assumption of acute infection in those horses was made based on clinical signs coupled with positive serology Conclusion RRV is a significant but poorly understood cause of poor performance in Australian horses. This report is the only one to document longitudinal management of performance horses affected by RRV infection. Much more research is needed to gain a better understanding of this infection in horses.

Pub.: 07 Jan '17, Pinned: 27 Jul '17