Disease Attribute Intelligence System

Aims

To adapt the Disease Attribute Intelligence System (DAISY) tool for monitoring emerging disease risk for the six HAIFA indicator diseases (campylobacteriosis, cryptosporidiosis, meningococcal infectious disease, influenza, and Ross River and dengue fevers); and to investigate the utility of using DAISY as a predictive tool for the impact of extreme weather events on infectious diseases.

Summary

Many current risk assessment tools are based on static elements that are inherent to the infectious disease. In an emerging infectious disease situation, climatic events and regional or local vulnerabilities are changeable and the level of risk is often difficult to determine. What is required is a tool that takes into account changing elements of risk e.g. immigration changes, and incorporates new knowledge into a continuous reassessment of emerging disease risk. We previously developed such a tool, DAISY, and adapted it for the six HAIFA indicator diseases (campylobacteriosis, cryptosporidiosis, meningococcal infectious disease, influenza, and Ross River and dengue fevers).

The original DAISY tool consisted of 25 risk attributes with five risk levels within each attribute. Using online information, DAISY profiled the daily risk of avian influenza spread into Europe during 2005 and 2006. DAISY was sensitive to the spread of avian influenza into Europe and the risk level for major agency interventions was noted. In addition, DAISY was used to risk rank 45 biological agents benchmarked against four standard lists of hazardous substances. Threat scores aligned well with the four standard lists and country-specific benchmarking enabled risk stratification of agents into preparedness categories. As a tool, DAISY was able to routinely and consistently assess levels of risk from day to day, according to attributes that apply across a wide range of biological agents, from both infectious disease and biowarfare perspectives. Thus, DAISY provides a consistent methodology for risk ranking infectious disease and other agents of concern. For this reason DAISY was used as part of the original risk ranking process to help identify campylobacteriosis, cryptosporidiosis, meningococcal infectious disease, and influenza to study for HAIFA (see the HAIFA modelling report [PDF, 6 MB] ).

Different regions within a country have region to region variation in surveillance, disease detection and disease control capabilities. DAISY therefore can be used to provide a picture of intra country risk variation. Using DAISY we monitored down to Territorial Land Authority level across New Zealand the changing risk for the six HAIFA indicator diseases on a monthly basis from January 2008 to November 2009 using surveillance and outbreak data from ESR’s EpiSurv system. The figure to the right  and the video files below, made from DAISY’s outputs, depict the changing risk across New Zealand. Investigations into the utility of using DAISY as a predictive tool for the impact of extreme weather events on infectious diseases were not fruitful.

Resources

Disease Attribute Intelligence System Tool [PDF, 367 KB]

Campylobacteriosis and cryptosporidiosis video [8.7MB AVI]

Meningococcal infectious disease and influenza video [11.4MB AVI]

Ross River and dengue fevers video [8.39MB AVI]

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