A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics

Chen, Xin; Chughtai, Abrar Ahmad; MacIntyre, C. Raina

doi:10.7205/milmed-d-17-00090

Cited by 10 publications

(14 citation statements)

References 33 publications

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“…An advanced risk assessment tool with both high sensitivity and specificity in distinguishing unnatural from natural epidemics is important for early disease response and biosecurity. Of the few available tools designed for such differentiation, the GFT is the best known scoring system (Chen et al., ). However, the performance in detecting unnatural epidemics is poor, with low sensitivity (38%) when applied to historical outbreaks of known causes.…”

Section: Discussionmentioning

confidence: 99%

“…In 2016, we conducted a systematic review of risk analysis tools for differentiating unnatural from natural epidemics (Chen, Chughtai, & MacIntyre, ). It is found that five risk assessment tools are available, including (1) the Grunow–Finke epidemiological assessment tool (13 criteria) (Grunow & Finke, ), (2) potential epidemiological clues to a deliberate epidemic (11 criteria) (Treadwell, Koo, Kuker, & Khan, ), (3) bioterrorism risk assessment scoring (23 criteria) (Radosavljevic, Finke, & Belojevic, ), (4) and (5) two modified scoring systems (14 and 23 criteria, respectively) (Radosavljevic & Belojevic, ; Radosavljevic & Belojevic, ) based on (3).…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…Planning for bioterrorism is underpinned by the assumption that attacks will be recognised as unnatural, but no public health systems exist to differentiate the aetiology of epidemics. Tools such as the Grunow and Finke (2002) criteria are not well known in public health and have low sensitivity for detecting unnatural epidemics when tested against historical events (Chen et al 2017;MacIntyre and Engells 2016). Public health agencies do not routinely use such tools and default to the assumption that they all epidemics are natural (MacIntyre 2015).…”

Section: Challenges In Detecting Bioterrorismmentioning

confidence: 99%

“…Yet the Grunow-Finke criteria despite low sensitivity (MacIntyre and Engells 2016) remain the best available tool. Other tools have been developed but are even less known and used than the Grunow-Finke criteria (Chen et al 2017). In addition to risk analysis tools, rapid surveillance methods are required to detect unnatural epidemic signals.…”

Section: Challenges In Detecting Bioterrorismmentioning

confidence: 99%

Converging and emerging threats to health security

et al. 2017

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Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.Keywords Biosecurity · Cybersecurity · Terrorism · Disasters · Health intelligence Advances in biological sciences have occurred at a faster rate than changes in regulatory and legal frameworks for biosecurity. An equally profound change is the convergence of concepts and methods from scientific disciplines that were previously distinct and siloed. Innovations in genomics, synthetic biology, big data, computing science and many other technologies have applications in health and medicine. Equally important, these convergent trajectories are dramatically expanding the repertoire of dual-use technologies-those which can be harnessed for benefit or harm to humanity. Collectively, these trends are shaping a new landscape for systematic risk analysis, for which there are

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“…episodes, one should make sanitary-epidemiologic investigations as efficient as it's only possible [10,11]. At present, as a number of sanitary-epidemiologic institutions is being gradually reduced but a number of object under surveillance, on the contrary, is growing, issues related to staff, material, and instrumental provision call for implementing information technologies (IT) that can support managerial decision-making [11][12][13][14].…”

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confidence: 99%

Information technologies for data collection and processing when establishing determinants of epidemic processes

Bogomolov¹,

Chikova²,

Zueva³

2019

Health Risk Analysis

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Providing biological safety of population is determined by a current situation with the state sanitary-epidemiologic system and its being ready to detect, localize and eliminate infective episodes. As threats of bioterrorism attacks against people are growing and infectious diseases are becoming more widely spread, it calls for greater efficiency of sanitary-epidemiologic examinations due to optimized collection and processing of data that are necessary for decision making related to revealing basic determinants of an epidemic process, as well as causes and conditions for infection occurrence and spread. The paper dwells on a technology for automated data processing that helps to efficiently reveal all determinants of an epidemic process, and causes and conditions for infection occurrence and spread. The technology also allows automated checking of all proposed hypotheses basing on generalization of results obtained via independent research. Proposed solutions are verified within a sanitary-epidemiologic examination that focused on dysentery episode in an organized team with its members staying on a closed territory and having their meals provided for them at a canteen. The authors compared an already existing system for collection and processing of statistical data (applied to reveal basic determinants of an epidemic process) and a proposed system for data collection and processing. Obtained results supplement and develop existing theoretical and practical achievements as regards IT implementation into sanitary-epidemiologic examinations; they have considerable practical significance especially bearing in mind a future transition to electronic circulation of documents within public healthcare and medical provision. When applied, a proposed approach allows considerable reduction in time spent on generalization of data obtained via sanitary-epidemiologic examinations as well as a significant increase in validity of accomplished statistical calculations necessary to reveal factors that cause infectious agent transmission.

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A Systematic Review of Risk Analysis Tools for Differentiating Unnatural From Natural Epidemics

Cited by 10 publications

References 33 publications

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

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

Converging and emerging threats to health security

Information technologies for data collection and processing when establishing determinants of epidemic processes

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