Recalibration of the Grunow–Finke Assessment Tool to Improve Performance in Detecting Unnatural Epidemics

Abstract

Successful identification of unnatural epidemics relies on a sensitive risk assessment tool designed for the differentiation between unnatural and natural epidemics. The Grunow–Finke tool (GFT), which has been the most widely used, however, has low sensitivity in such differentiation. We aimed to recalibrate the GFT and improve the performance in detection of unnatural epidemics. The comparator was the original GFT and its application in 11 historical outbreaks, including eight confirmed unnatural outbreaks and three natural outbreaks. Three steps were involved: (i) removing criteria, (ii) changing weighting factors, and (iii) adding and refining criteria. We created a series of alternative models to examine the changes on the parameter likelihood of unnatural outbreaks until we found a model that correctly identified all the unnatural outbreaks and natural ones. Finally, the recalibrated GFT was tested and validated with data from an unnatural and natural outbreak, respectively. A total of 238 models were tested. Through the removal of criteria, increasing or decreasing weighting factors of other criteria, adding a new criterion titled “special insights,” and setting a new threshold for likelihood, we increased the sensitivity of the GFT from 38% to 100%, and retained the specificity at 100% in detecting unnatural epidemics. Using test data from an unnatural and a natural outbreak, the recalibrated GFT correctly classified their etiology. The recalibrated GFT could be integrated into routine outbreak investigation by public health institutions and agencies responsible for biosecurity.

Application of a Risk Analysis Tool to Middle East Respiratory Syndrome Coronavirus (MERS‐CoV) Outbreak in Saudi Arabia

“…This criterion is defined as "It is unusual from an epidemiologic perspective, if the disease, is identified in a region concerned for the first time ever or again after a long period of time" (Chen et al, 2018).…”

“…as an aerosol, we can expect to find it in unusually high concentrations in the air, soil and drinking or surface water over a large area. High concentrations of pathogens predominantly exist in selected areas, although the pathogens might also be found in the case of natural contamination" (Chen et al, 2018).…”

“…The intensity and dynamics of an epidemic are "characterized by the percentage of cases of a disease per unit of time or the total number of cases" (Chen et al, 2018).…”

“…In general, natural epidemics will feature paths of transmission which are typical for the pathogen and its natural hosts. Deviations from natural paths of infection could indicate that biological agents have been deliberately disseminated" (Chen et al, 2018).…”

“…When applied to 11 historically natural and unnatural disease outbreaks by the United States (U.S.) Army (Dembek, Pavlin, & Kortepeter, 2007), it had high specificity (100%) but relatively low sensitivity (38%) for detecting bioterrorism (MacIntyre & Engells, 2016). The GFT was recalibrated in 2017 by changing criteria, weights, and threshold to achieve 100% sensitivity in detection of unnatural outbreaks, and the modified GFT (mGFT) was validated against historical data (Chen, Chughtai, & MacIntyre, 2018). However, it has not been applied to newly emerged or ongoing epidemics.…”