As an initial approach, observed and expected disease reports by area will be compared after standardising for demographic factors, to determine whether the spatial pattern of the disease is explainable by population effects alone. Linear and logistic regression will be used to build explanatory models of disease occurrence, using climatic and other variables. Thorough ecological analysis will be essential with maps of disease distribution according to the timecourse of ENSO, paying careful attention to the categorization of risk estimates. We expect to use time series analyses to characterise the component of temporal variations in crude disease rates. These will enable the observed variations to be broken down into various components of time, including an overall linear trend, a cyclical component, a seasonal component and an irregular random component.
Spatial analysis is a new field, particularly in relation to disease data.
This project provides an important opportunity for exploration of these methods in relation to disease surveillance in Australia. Building on data collected at national, state and local levels, it will lead to improvements in the recognition and response to arboviral diseases in the region. A multi disciplinary and geographically representative team has been developed with the broad base of essential expertise. International collaboration is invited.