Surveillance systems you can count on
“You cannot prove an area is free of a particular pest”, says Queensland researcher Mark Stanaway, “you can only estimate the probability. The more information you bring into your surveillance systems, the more accurate your estimate.”
Mr Stanaway and Western Australian researcher Nichole Hammond are both working on CRC for National Plant Biosecurity PhD projects designed to analyse the effectiveness of current surveillance systems and identify areas for improvement.
Mr Stanaway’s project is based on surveillance data and modelling of the spread of spiralling whitefly, a sap-sucking bug from South America first identified in Cairns in 1998. Ms Hammond has focused on an assessment of surveillance systems in WA for the fungal disease Karnal bunt, which affects cereals.
Both are using hierarchical Bayesian models to estimate the uncertainty attached to each level of decision-making in the surveillance process and ranking the importance of each decision, to arrive at a ‘confidence rating’ for the effectiveness of their respective surveillance systems.
For his project, Mr Stanaway has collated more than 11 years of surveillance data to track the spread of the exotic spiralling whitefly (Aleurodicus dispersus) through northern Queensland. His central challenge is to calculate the likelihood that spiralling whitefly is in a particular area, based on the available surveillance strategies and current knowledge about the pest, by quantifying the uncertainty in the surveillance system.
Since arriving more than a decade ago, spiralling whitefly has established itself in Surveillance systems you can count on Statistical modelling provides greater confidence in pest surveillance systems tropical coastal Queensland, from Torres Strait to Gladstone, and has also been found in the Darwin region. Restrictions have been placed on the transport of nursery stock from Queensland because the insect attacks crops and ornamental plants.
Mr Stanaway’s modelling includes all known information about the insect, such as growth and reproductive rates and its spread to date. It also includes uncertainty calculations about the pest’s ecology and the sensitivity of monitoring techniques. It effectively provides a statistical fact check for predictive modelling about how the insect may spread, which can highlight problems in surveillance systems and in the modelling.
He uses a scoring system that identifies how good the ‘presence’ and ‘absence’ data collected by the surveillance program is – how likely a ‘false negative’ might be and whether the insect is actually present, despite the fact that it hasn’t been found.
His analysis has already identified the most effective host plants to monitor for the whitefly and a method of assessing how effective inspectors are. He says his analysis will be used to estimate whether spiralling whitefly has spread as far south as it is likely to, based on ‘absence’ data from southern-most monitoring points for several years, and the fact that once the pest spreads to a new location numbers build up to the point where it is easily identified in surveillance. This finding may allow restrictions on the movement of nursery plants to be reviewed.
He says the statistical surveillance information is being used in conjunction with a geographic information system (GIS) database and predictive modelling to develop a risk map for the spread of the insect. “The risk map is a much more powerful tool than the surveillance data because it provides a visual representation of changing insect or disease presence over time; it gives a sense of movement or change, which can be otherwise difficult to assess.”
In WA, Nichole Hammond has considered both active and passive surveillance systems for the exotic fungal pathogen Tilletia indica, which causes Karnal bunt of wheat, rated an ‘extreme’ threat to the grains industry if it ever gets through Australia’s quarantine defence.
The Department of Agriculture and Food, WA (DAFWA) has run an active surveillance for the past 10 years, collecting grain samples from the bulk grain handler the CBH Group. Each round of DAFWA surveillance tested approximately 200 grain samples and no Karnal bunt has been detected.
Passive surveillance systems involve the constant scanning of crops by farmers, farm workers and agronomists for anything ‘out of the ordinary’. Ms Hammond says passive surveillance has historically been used to support claims of freedom from plant pests, but its effectiveness has not previously been formally evaluated.
Her research analysed the community detection and reporting processes – the likelihood that growers would detect a possible infection and report it, based on existing surveillance practices for reporting pests and diseases.
She found that the active and passive surveillance systems provided greater than 90 per cent confidence that WA is free of Karnal bunt. This was based on 10 years’ worth of harvest data and the level of infection expected, if the disease was present in WA.
Ms Hammond’s analysis identified how sensitive each stage of the surveillance and identification process was. It highlighted where these could be improved, such as providing more information to growers and agronomists about signs and symptoms of exotic grains pests to improve confidence in on-farm detection.
Photo caption: Nichole Hammond recently submitted her thesis
Photo acknowledgement: GRDC Ground Cover
Article written by: Catherine Norwood
