Developing predictive models for Ross River virus disease in New South Wales, Australia

Abstract

Introduction: This thesis presents research undertaken to assess the possibility of using data on climate, environment and the Ross River virus (RRV) vector and host species to develop early warning predictive models for RRV disease in New South Wales, Australia. Such models may then contribute to the anticipation, prevention and control of RRV disease. Background: Mosquito-borne diseases at the national and global levels are a substantial cause of morbidity and mortality. There are currently few vaccines available for mosquito-borne diseases (yellow fever and Japanese encephalitis); hence mosquito control, public health warnings and outbreak preparedness are the primary measures for preventing mosquito-borne diseases at the population level. On a longer time frame, the abatement of human-induce climate change and of aspects of land use and water management will also be important contributions to reducing the range and risks from these infectious diseases. For the more immediate measures to be successful, knowledge of when the next outbreak will occur within an appropriate response time is essential. As climate and environment have direct influences on components of the disease transmission cycle, they are suitable for inclusion in short-term and long-term predictive modelling of mosquito-borne diseases. RRV disease is the most widespread and most common mosquito-borne disease in Australia and causes considerable morbidity in the population. Over 86,500 notifications of RRV disease have been reported in Australia since 1991, an average of 4,300 notifications per year. RRV is maintained within a wide range of mosquitoes and vertebrate reservoir hosts. Climate and environment are known to influence the distribution and abundance of these RRV vectors and reservoir hosts, and thereby influence the probability of transmission to humans and the risk of clinical disease.