Assessing the quality of data for drivers of disease emergence.

Abstract

Drivers are factors that have the potential to directly or indirectly influence the likelihood of infectious diseases emerging or re-emerging. It is likely that an emerging infectious disease (EID) rarely occurs as the result of only one driver; rather, a network of sub-drivers (factors that can influence a driver) are likely to provide conditions that allow a pathogen to (re-)emerge and become established. Data on sub-drivers have therefore been used by modellers to identify hotspots where EIDs may next occur, or to estimate which sub-drivers have the greatest influence on the likelihood of their occurrence. To minimise error and bias when modelling how sub-drivers interact, and thus aid in predicting the likelihood of infectious disease emergence, researchers need good-quality data to describe these sub-drivers. This study assesses the quality of the available data on sub-drivers of West Nile virus against various criteria as a case study. The data were found to be of varying quality with regard to fulfilling the criteria. The characteristic with the lowest score was completeness, i.e. where sufficient data are available to fulfil all the requirements for the model. This is an important characteristic as an incomplete data set could lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good-quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and identifying the points on the risk pathway where preventive measures may be taken.