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

Use of a risk assessment tool to determine the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is contentious. Most studies have focused on a zoonotic origin, but definitive evidence such as an intermediary animal host is lacking. We used an established risk analysis tool for differentiating natural and unnatural epidemics, the modified Grunow-Finke assessment tool (mGFT) to study the origin of SARS-COV-2. The mGFT scores 11 criteria to provide a likelihood of natural or unnatural origin. Using published literature and publicly available sources of information, we applied the mGFT to the origin of SARS-CoV-2. The mGFT scored 41/60 points (68%), with high inter-rater reliability (100%), indicating a greater likelihood of an unnatural than natural origin of SARS-CoV-2. This risk assessment cannot prove the origin of SARS-CoV-2 but shows that the possibility of a laboratory origin cannot be easily dismissed.

Surveillance of bacterial disease in wartime Ukraine

This analysis considers circulation of bacterial disease in wartime Ukraine. Anthrax, brucellosis, botulism and tularaemia are all naturally occurring in the country. The causative agents of these diseases also formed components of the biological weapons programme the Russian Federation inherited from the Soviet Union at the end of the Cold War. Differentiating between natural and unnatural outbreaks of disease in Ukraine is essential for combating disinformation and maintaining health security as the war intensifies.

Application of the Modified Grunow-Finke Risk Assessment Tool to the Sverdlovsk Anthrax Outbreak of 1979

INTRODUCTION

The modified Grunow-Finke tool (mGFT) is an improved scoring system for distinguishing unnatural outbreaks from natural ones. The 1979 Sverdlovsk anthrax outbreak was due to the inhalation of anthrax spores from a military laboratory, confirmed by Russian President Boris Yeltsin in 1992. At the time the Soviet Union insisted that the outbreak was caused by meat contaminated by diseased animals. At the time there was no available risk assessment tool capable of thoroughly examine the origin of the outbreak.

METHODS

This study aimed to retrospectively apply the mGFT to test its ability to correctly identify the origin of the Sverdlovsk anthrax outbreak of 1979 as unnatural, using data available up to 1992, before the disclosure of a laboratory leak. Data spanning from 1979 to 1992 were collected through literature reviews. Evidence related to each mGFT criterion was scored on a scale of 0 to 3 and independently reviewed by 3 assessors. These scores were then multiplied with a weighting factor and summed to obtain a maximum score. A final score exceeding 30 was indicative of an unnatural origin.

RESULTS

The mGFT results assigned a total of 47 points to the Sverdlovsk anthrax outbreak, suggesting an unnatural origin with a 78% likelihood.

CONCLUSIONS

These findings align with the confirmed unnatural origin of the outbreak, highlighting the value of tools such as the mGFT in identifying unnatural outbreaks. Such tools integrate both intelligence evidence and biological evidence in the identification of unnatural outbreaks. The use of such tools for identifying unnatural outbreaks is limited. Outbreak investigation can be improved if risk assessment tools become integral to routine public health practice and outbreak investigations.

Origin of the H1N1 (Russian influenza) pandemic of 1977-A risk assessment using the modified Grunow-Finke tool (mGFT)

In 1977, the Soviet Union (Union of Soviet Socialist Republics [USSR]) notified the World Health Organization (WHO) about an outbreak of H1N1 influenza, which later spread to many countries. The H1N1 strain of 1977 reappeared after being absent from the world for over 20 years. This pandemic simultaneously spread to several cities in the USSR and China. Many theories have been postulated to account for the emergence of this pandemic, including natural and unnatural origins. The purpose of this study was to use the modified Grunow-Finke risk assessment tool (modified Grunow-Finke tool [mGFT]) to investigate the origin of the 1977 H1N1 pandemic. Data was collected from WHO archives and published documents. The assessment of the pandemic's origin involved the utilization of a modified version of the original Grunow-Finke risk assessment tool (GFT). Using the mGFT, the final score was 37 out of 60 points (probability: 62%), indicating a high likelihood that the Russian influenza pandemic of 1977 was of unnatural origin. Several variables supported this finding, including the sudden re-emergence of a previously extinct strain, a genetic signature of laboratory modification for vaccine development, and unusual epidemiology. Inter-rater reliability was moderate to high. By applying the mGFT to the 1977 Russian influenza pandemic, we established a high probability that this pandemic was of unnatural origin. Although this is not definitive, it is consistent with the possibility that it originated from an incompletely attenuated live influenza vaccine. The mGFT is a useful risk analysis tool to evaluate the origin of epidemics.

Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.

Using a grey relational analysis in an improved Grunow-Finke assessment tool to detect unnatural epidemics

The Grunow-Finke epidemiological assessment tool (GFT) has several limitations in its ability to differentiate between natural and man-made epidemics. Our study aimed to improve the GFT and analyze historical epidemics to validate the model. Using a gray relational analysis (GRA), we improved the GFT by revising the existing standards and adding five new standards. We then removed the artificial weights and final decision threshold. Finally, by using typically unnatural epidemic events as references, we used the GRA to calculate the unnatural probability and obtain assessment results. Using the advanced tool, we conducted retrospective and case analyses to test its performance. In the validation set of 13 historical epidemics, unnatural and natural epidemics were divided into two categories near the unnatural probability of 45%, showing evident differences (p < 0.01) and an assessment accuracy close to 100%. The unnatural probabilities of the Ebola virus disease of 2013 and Middle East Respiratory Syndrome of 2012 were 30.6% and 36.1%, respectively. Our advanced epidemic assessment tool improved the accuracy of the original GFT from approximately 55% to approximately 100% and reduced the impact of human factors on these outcomes effectively.

Artificial intelligence in public health: the potential of epidemic early warning systems

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.