Using statistical methods to evaluate and improve mosquito control for mosquito-borne disease reduction in the Northern Territory of Australia

Abstract

Mosquito borne-diseases are responsible for high rates of morbidity and mortality worldwide. Ross River, Barmah Forest, Murray Valley encephalitis and Kunjin viruses are presently endemic in the Northern Territory, while dengue virus and malaria are at risk of re-establishment. In this thesis, I describe the epidemiology of the major endemic mosquito-borne diseases of the Northern Territory, past and present, and evaluate control strategies adopted to reduce mosquito abundance and disease transmission. Some findings include; Aedes vigilax larval densities were almost 10 times greater in artificially incised drains compared with lower mangroves, despite the creation of drains for mosquito control purposes. I further analyse the effects of drainage channels, habitat modification, over time in three Darwin salt-marshes and one freshwater swamp near Alice Springs. In addition to significantly reducing vector-borne disease transmission and pest biting; habitat modification has restored all swamps to closer to their original ecosystems. To optimise the efficiency of environmentally-sensitive mosquito control programs, I examine current surveillance and control regimes. Findings indicate that most regimes are required to maintain tight mosquito control, although coastal surveillance could be reduced without a loss of information. I develop accurate predictive models for RRV infections in the Northern Territory, using weather and vector numbers. Such models assist public health campaigns, but rarely provide timely notification for vector control. I therefore created methodologies to produce rainfall thresholds —as early warning systems, which guide mosquito control before vector numbers peak, thereby reducing transmission potential. These findings can assist mosquito control programs in northern Australia and tropical regions worldwide.

Date of Award	Jun 2011
Original language	English
Supervisor	Mike Lawes (Supervisor) & Allen Cheng (Supervisor)