12:14 PM 3/28/2020 - REPRINT: Application of a Risk Analysis Tool to Middle East Respiratory Syndrome Coronavirus (MERS‐CoV) Outbreak in Saudi Arabia

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Fig. 1
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Epidemic curve of MERS‐CoV by date of symptoms onset in Saudi Arabia, 2012–2018.
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 This article appears to be very important, I reprint it to have it at hands and just in case, if it gets lost somehow, which happens somwtimes. - M.N. 
Original Research Article 

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


First published:13 March 2020
 
https://doi.org/10.1111/risa.13472









Abstract

The Grunow–Finke assessment tool (GFT) is an accepted scoring system for determining likelihood of an outbreak being unnatural in origin. Considering its high specificity but low sensitivity, a modified Grunow–Finke tool (mGFT) has been developed with improved sensitivity. The mGFT has been validated against some past disease outbreaks, but it has not been applied to ongoing outbreaks. This study is aimed to score the outbreak of Middle East respiratory syndrome coronavirus (MERS‐CoV) in Saudi Arabia using both the original GFT and mGFT. The publicly available data on human cases of MERS‐CoV infections reported in Saudi Arabia (2012–2018) were sourced from the FluTrackers, World Health Organization, Saudi Ministry of Health, and published literature associated with MERS outbreaks investigations. The risk assessment of MERS‐CoV in Saudi Arabia was analyzed using the original GFT and mGFT criteria, algorithms, and thresholds. The scoring points for each criterion were determined by three researchers to minimize the subjectivity. The results showed 40 points of total possible 54 points using the original GFT (likelihood: 74%), and 40 points of a total possible 60 points (likelihood: 67%) using the mGFT, both tools indicating a high likelihood that human MERS‐CoV in Saudi Arabia is unnatural in origin. The findings simply flag unusual patterns in this outbreak, but do not prove unnatural etiology. Proof of bioattacks can only be obtained by law enforcement and intelligence agencies. This study demonstrated the value and flexibility of the mGFT in assessing and predicting the risk for an ongoing outbreak with simple criteria.

1 INTRODUCTION

During the past decade, serious outbreaks of emerging infectious diseases have occurred in many parts of the world, including the Middle East respiratory syndrome coronavirus (MERS‐CoV) in Saudi Arabia (2012–present), Ebola in West Africa (2014–2016) (Ajisegiri, Chughtai, & MacIntyre, 2018), Zika virus in Brazil (2015–2016) (Campos, Bandeira, & Sardi, 2015), and the plague in Madagascar in 2017 (European Centre for Disease Prevention and Control, 2017). The default assumption of most outbreaks and epidemics is that they are naturally caused (MacIntyre & Engells, 2016), and public health culture and practice do not routinely consider unnatural causes such as bioterrorism or biowarfare (MacIntyre, 2015a). All category A bioterrorism agents except smallpox also occur in nature. Therefore, epidemics caused by bioterrorism, including by new or engineered pathogens, may not be recognized as unnatural (MacIntyre et al., 2017). Bioterrorism‐associated outbreaks cannot be identified unless specifically considered, and even so may be difficult to detect (MacIntyre, 2015b; MacIntyre & Engells, 2016; Treadwell, Koo, Kuker, & Khan, 2003). Therefore, in an era of accessible and expanding technology in genetic engineering and synthetic biology (MacIntyre et al., 2017), it is important to discern unnatural from natural outbreaks during outbreak investigation.
The original Grunow–Finke epidemiology assessment tool (GFT) (Grunow & Finke, 2002) is a scoring system developed in 2002 for the identification of bioterrorism events, and is the most widely used tool (Chen, Chughtai, & MacIntyre, 2017). 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.
The ongoing MERS‐CoV outbreak has persisted in the world for seven years since 2012 when it was first isolated in Bisha (Zaki, Van Boheemen, Bestebroer, Osterhaus, & Fouchier, 2012), despite having a much lower basic reproduction number (R0) than severe acute respiratory syndrome coronavirus (SARS‐CoV) which ended after an eight‐month outbreak (Gardner & MacIntyre, 2014; MacIntyre et al., 2017). As of December 31, 2018, a total of 1,901 human cases, including 732 deaths (case fatality rate: 39%), have been reported in Saudi Arabia by the World Health Organization (WHO) (WHO, 2018a), accounting for more than 80% of global cases (FluTrackers, 2018). There have been unanswered questions and various explanations of the source of MERS‐CoV, transmission modes, and unusual epidemic pattern. While camels have been identified as a host of the virus (Azhar et al., 2014; Dudas, Carvalho, Rambaut, & Bedford, 2018; Mohd, Al‐Tawfiq, & Memish, 2016), over 50% of human cases have no identified exposure to animals, let alone camels (Chen, Chughtai, Dyda, & MacIntyre, 2017). No drug is currently available against MERS‐CoV (Modjarrad, 2016). Several vaccine candidates are in development, of which the GLS‐5300 vaccine candidate was in human clinical trial (Modjarrad et al., 2019). The aim of this study was to apply the mGFT to MERS‐CoV outbreak in Saudi Arabia for risk analysis, and to evaluate the mGFT in comparison with original GFT.

2 METHODS

2.1 Data Collection

For this study, a data set of human cases of MERS‐CoV infection reported from 2012 to 2018 in Saudi Arabia was first created. The publicly available data were sourced from the “Case List of Saudi Ministry of Health (MoH)/WHO Novel Coronavirus MERS Announced Cases” on FluTrackers (FluTrackers, 2018), including demographic data (age, gender, and occupation) and location data (city). Each human case on FluTrackers’ list has the linked original reports from the WHO (WHO, 2019a) and Saudi MoH (Saudi MoH, 2015), where data of multiple risk factors including date of notification, date of symptoms onset, date of hospitalization, laboratory confirmation date, complications, death, and specified contact history (camel‐linked, sheep‐linked, community‐linked, hospital‐linked, unknown exposure) were collected to enhance the data set. Cases on the list would be excluded from the data set if there were no data of any risk factor. In addition, the published literature associated with MERS outbreaks investigations were reviewed for rationales to inform scoring the GFT.

2.2 Application of the Original GFT and mGFT

The MERS‐CoV outbreak in Saudi Arabia was scored using original GFT and mGFT, and assessment points were determined by three researchers (XC, AAC, and CRM) independently to minimize the subjectivity. The original GFT (Grunow & Finke, 2002) contains 11 nonconclusive criteria (Table I) which indirectly indicate the likelihood of biological warfare. For each criterion, a value between 0 and 3 is given based on the available data. It is then multiplied by a set weighting factor between 1 and 3 points. The sum of points is divided by the maximum number of points (54 points) for a probability which indicates the likelihood of bioterrorism: 0–33% (Unlikely), 34–66% (Doubtful), 67–94% (Likely), and 95–100% (Highly Likely).



Table I. The Original Grunow–Finke Assessment Tool

CriteriaAssessment PointsaWeighting FactorMaximum Points
1. Biorisk0,1,2,326
2. Biothreat0,1,2,339
3. Special aspects0,1,2,339
4. Geographic distribution0,1,2,313
5. Environmental concentration0,1,2,326
6. Epidemic intensity0,1,2,313
7. Transmission mode0,1,2,326
8. Time0,1,2,313
9. Unusually rapid spread0,1,2,313
10. Population limitation0,1,2,313
11. Clinical0,1,2,313
Total pointsAssessment points × weighting factor54
Likelihoodb(Total points/54) × 100
  • Note: The Grunow–Finke epidemiological assessment procedure used by the U.S. Army (Dembek et al., 2007).
  • a Assessment of a criterion: 0, Ruled out or no data; 1, Peculiarities or suspicions but both are uncertain and indistinct; 2, Obvious peculiarities or indications yet to be clarified for certain or which have not been proven unambiguously; 3, Considerable peculiarities or deviations or clear indication or proof of a biological attack.
  • b Likelihood of a biological attack: 0–33%, Unlikely; 34–66%, Doubtful; 67–94%, Likely; 95–100%, Highly likely.
The mGFT (Table II) contains 11 modified criteria (biorisk, unusual strain, geographic distribution, environmental concentration, epidemic intensity, transmission mode, time, unusually rapid spread, population limitation, clinical, and special insights). Each criterion is assessed according to the available evidence. It is then multiplied by a fixed weighting factor (1–3 points). Based on the total points of all the criteria, the outbreak is classified as naturally caused if the result is less than 30 points, otherwise it points to an unnatural cause, with varying likelihood depending on the score.



Table II. The Modified Grunow–Finke Assessment Tool

CriteriaAssessment PointsaWeighting FactorMaximum Points
1. Biorisk0,1,2,339
2. Unusual strain0,1,2,339
3. Geographic distribution0,1,2,313
4. Environmental concentration0,1,2,339
5. Epidemic intensity0,1,2,313
6. Transmission mode0,1,2,313
7. Time0,1,2,313
8. Unusually rapid spread0,1,2,313
9. Population limitation0,1,2,326
10. Clinical0,1,2,313
11. Special insights0,1,2,339
Total pointsAssessment points × weighting factor60
Likelihoodb(Total points/60) × 100
  • Note: The original Grunow–Finke assessment tool was recalibrated (Chen et al., 2018).
  • a Assessment of a criterion: 0, No data; 1, Uncertain; 2, Obvious peculiarities; 3, Clear indication or proof of a biological attack.
  • b Classification by the likelihood: <50%, Natural outbreak; ≥50%, Unnatural outbreak.

3 RESULTS

Between September 20, 2012 and December 31, 2018, there were 1,903 human cases of MERS‐CoV in Saudi Arabia listed on FluTrackers (FluTrackers, 2018), containing 1,901 cases reported by the WHO (WHO, 2018a) in that period. One hundred and thirteen cases had missing data on all risk factors and were excluded from this study. A total of 1,790 cases of MERS‐CoV including 365 fatal cases were collected in the data set (Table III). These data were analyzed for scoring the GFT and mGFT.



Table III. An Overview of Human Cases of MERS‐CoV Infections in Saudi Arabia (2012–2018)

VariablesSaudi Arabia (Number of Cases = 1,790)
Age range
 Mean
 Median
0–109 years
52 years
53 years
Sexa
 Male1,187/1,741 (68.2%)
 Female555/1,741(31.9%)
Healthcare workersb237/1,771 (13.4%)
Fatalities365/1,772 (20.6%)
Comorbidityc1,072/1,662 (64.5%)
Contact history
 Camel‐linked243/1,790 (13.6%)
 Sheep‐linked5/1,790 (0.3%)
 Hospital‐linked209/1,790 (11.7%)
 Community‐linked315/1,790 (17.6%)
 Unknown exposure920/1,790 (51.4%)
  • a Sex: unavailable data from 49 cases.
  • b Healthcare workers: unavailable data from 19 cases.
  • c Comorbidity: unavailable data from 128 cases.

3.1 The Original GFT: Risk Assessment Criteria and Rationales

3.1.1 Criterion 1: Existence of a Biological Risk

Biological risk is defined as “the presence of a political or terrorist environment from which a biological attack could originate. A biological risk arises, for example, if states, groups, or individuals have access to biological warfare agents and the necessary means of distributing them and are willing to use them. In addition, a biological risk is to be assumed if, in the area concerned, biological warfare agents are developed, produced, or stored and could be released as a result of poor plant security or if the plant itself is destroyed”(Grunow & Finke, 2002).
The Middle East region has been a war‐torn region with conflicts and disputes over politics, culture, religion, and land. During the past decade, at least 10 terrorist attacks against Middle Eastern civilians and soldiers have been documented (Johnston, 2017), including two suspicious bioterrorism events with cholera and poison involved in Yemen (Global Research, 2017) and Iraq (Johnston, 2017). In addition, MERS‐CoV was within scope for the U.S. government for dual‐use research (or gain‐of‐function) of concern, and was included in the pause on new government funding on gain‐of‐function research which also involved influenza and SARS‐CoV in 2014 (Public Health Emergency, 2014). Therefore, the biological risk of MERS‐CoV may be significant. Given the geopolitical history and context in the Middle Eastern region, this criterion was assessed as 2 points.

3.1.2 Criterion 2: Existence of a Biological Threat

It is defined as “If in the environment of a biological risk, individual states, groups, or persons openly threaten to use biological warfare agents or if a specific interest in their use can be assumed”(Grunow & Finke, 2002).
There was no openly biological threat to Saudi Arabia, but it is regarded that some countries have offensive biological warfare capabilities. Its neighboring countries, such as Israel, refused to sign the Biological Warfare Convention (BWC), and might have maintained biological weapons stocks since 1998 (The Washington Post, 2017). Syria and Egypt have not ratified the BWC (Arms Control Association, 2018) either. During a 2014 raid on the Islamic State, it is alleged that some Syrian rebels recovered a laptop which contained instructions for the Islamic State on the usage of biological weapons and the way to protect themselves from exposure (Hummel, 2016). As the biological risk of MERS‐CoV may be significant (2 points), it is likely for terrorist groups to produce and use biological warfare agents, especially when the tensions between Saudi Arabia and neighbors became serious. For this reason, a value of 2 was allocated to this criterion.

3.1.3 Criterion 3: Special Aspects of the Biological Agent

It is defined as “Given the current state of research and technology, it cannot be ruled out that a potential aggressor has genetically manipulated certain characteristics of pathogens or toxins prior to their use as biological warfare agents. This can include measures which make detection and identification more difficult or which increase the virulence, environmental stability and resistance to prophylactic and therapeutic measures” (Grunow & Finke, 2002).
  1. The MERS‐CoV is classified as a bioterrorism category C agent by the Center of Disease Control (CDC, 2018), which includes newly emerged infections, and is the “third highest priority agent that could be engineered for mass dissemination because of availability, ease of production, and dissemination, and potential for high morbidity and mortality rates and major health impact” (CDC, 2018);
  2. The origin of MERS‐CoV is unknown. It was first documented in humans in the Middle East in 2012 (FluTrackers, 2012), resulting in 1903 laboratory‐confirmed human cases worldwide as of December 31, 2018 (FluTrackers, 2018). It is now known that MERS‐CoV strains have circulated in animal hosts such as bats and dromedary camels in the Middle East and East Africa for decades (Cui, Eden, Holmes, & Wang, 2013; Mohd et al., 2016) before the first human case that was identified in 2012 (Chen, Chughtai, Dyda et al., 2017). If naturally occurring, presumably a genetic mutation enabled the species jump to humans in 2012. The phylogenetic evolution of MERS‐CoV prior to and after the species jump from animal to human shows the high rate of evolution (1.42 × 10−3 substitutions/site per year) (Kim, Park, Bae, & Park, 2019). However, the exact routes of transmission of MERS‐CoV in animals and humans are still puzzling.
  3. In a nosocomial outbreak in Al Ahsa (April–May 2013), a total of 23 cases, with a classic epidemic curve, were confirmed. A single branching chain of person‐to‐person transmission was assumed. However, the phylogenetic analysis (Assiri et al., 2013; Cotten et al., 2013) demonstrated that multiple genetic strains were infecting patients during this outbreak. At least five of 13 transmissions within the hospital could not be explained by person‐to‐person transmission (MacIntyre, 2014). This evidence of multiple independent introductions of infection and several genetically related but distinct strains in a single hospital outbreak is unusual. Whether multiple people had the same animal exposure in the community prior to presenting to hospital, no evidence of this was found or described.
Taking the peculiarities described above into consideration, 3 points have been allocated to this criterion.

3.1.4 Criterion 4: Peculiarities of the Geographic Distribution of Disease

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).
The Middle East country, particularly the Kingdom of Saudi Arabia, has the greatest disease burden of MERS‐CoV. Since it was first identified in the Middle East in 2012, it has continued to cause human infection for seven years. Saudi Arabia, where all 20 cities have been affected, has more than 80% of global MERS‐CoV cases. While the disease has spread to other 26 countries, most of these are isolated cases of MERS‐CoV imported through travel to Saudi Arabia or the Middle East (FluTrackers, 2018). South Korea was the only country that had an MERS‐CoV outbreak outside of the Arabian Peninsula, with the index patient presenting to multiple health facilities who failed to diagnose MERS‐CoV (MacIntyre, 2014).
Comparing this pattern to SARS‐CoV, a similar novel coronavirus which also spread globally, the epidemiology is different. After the importation of SARS cases, satellite epidemics occurred in a consistent epidemic pattern in a number of countries (MacIntyre, 2014), including the mainland China, Hong Kong, Taiwan, Singapore, Vietnam, and Canada (WHO, 2018b). However, MERS‐CoV has been limited largely to the Middle East, with a notable absence of satellite epidemics, except for South Korea (MacIntyre, 2014). A modeling study also showed that with the estimated R0 of MERS‐CoV, satellite epidemics should have occurred in other countries following mass gatherings such as the Hajj pilgrimage in Mecca (Gardner et al., 2014). Yet no such satellite epidemics have arisen following Hajj or Umrah pilgrimages. In addition, based on frequency of air travel to Saudi Arabia, the country at the highest risk of an importation of MERS‐CoV, India, has not seen a case, yet a country which is not in the top 10 at‐risk countries, South Korea, has seen a large epidemic (Gardner, Chughtai, & MacIntyre, 2016). Given the narrow geographic distribution of MERS‐CoV in Saudi Arabia and the other factors above, this criterion was assessed as 3 points.

3.1.5 Criterion 5: High Concentration of the Biological Agent in the Environment

It is defined as “If a biological agent is released artificially, e.g. 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).
In Saudi Arabia, no environmental investigation for MERS‐CoV has been conducted, or if it has been conducted, no public data are available. According to the environmental investigation in the MERS‐CoV outbreak units in South Korea (Kim et al., 2016), virus was found from ambient air samples obtained from each patient's room and its affiliated restroom. Furthermore, swabs from patients’ pillows, telephone buttons, televisions, and the floor were positive for the virus in the rest rooms after daily cleaning and disinfection of the rooms. While there is no publicly available environmental data from Saudi Arabia, the possibility of environmental concentration of MERS‐CoV there could not be ruled out. Given this uncertainty, only 1 point was allocated to this criterion.

3.1.6 Criterion 6: Peculiarities of the Intensity and Dynamics of the Epidemic

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).
The epidemic pattern of MERS‐CoV is not as intensive as SARS‐CoV, which is also caused by a coronavirus, and had sickened over 8,000 people in 2003 (Lu, Wang, & Gao, 2015). In Saudi Arabia, MERS‐CoV has resulted in a total of 1,901 human cases between 2012 and 2018 (WHO, 2018a). In addition, MERS‐CoV did not feature a classic epidemic peak in the first year after emergence as did SARS (Wallinga & Teunis, 2004). While there has been a large surge in cases in Saudi Arabia in some years, with some clustered cases and nosocomial outbreaks, the pattern remains largely sporadic rather than epidemic (MacIntyre, 2014). Taking the above into consideration, 2 points were allocated to this criterion.

3.1.7 Criterion 7: Peculiarities of the Transmission Mode of the Biological Agent

This criterion is defined as “Different pathogens prefer different paths of infection. 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).
There are two presumed transmission routes of MERS‐CoV:
  1. Zoonotic transmission: Exposure to animals, such as close contact with camels and consumption with camel milk or meat, is a risk factor of MERS‐CoV infection. The exact routes of zoonotic transmission of MERS‐CoV remain unknown. Although camels appear to be the host, it is found that only 13.6% of cases had confirmed contact history with animals (Table III). Serosurveys of camels and humans also show low risk of infection in humans with close camel contact (Hemida et al., 2015).
  2. Human‐to‐human transmission: Human‐to‐human transmission is documented in nosocomial and household outbreaks. In Saudi Arabia, around 30% of MERS‐CoV infections were acquired in healthcare facilities and the community after contact with confirmed cases (Table III).
In Saudi Arabia, the MERS‐CoV shows unusual transmission patterns. Of the laboratory‐confirmed human cases in Saudi Arabia, over 50% did not report any exposure before illness (Table III). In Al Ahsa nosocomial outbreak in 2013, five of 13 transmissions within the hospitals could not be explained by person‐to‐person transmission, and the evidence of animal exposure before presenting to hospital was not found (MacIntyre, 2014). Given these unusual aspects of the transmission epidemiology, this criterion was given 3 points.

3.1.8 Criterion 8: Peculiarities of the Time of the Epidemic

The criterion is defined as “Epidemics of certain infectious diseases occur in increased numbers during certain seasons, either because they are dependent on the weather, or they occur after certain intervals in time. If the temporal context of an epidemic is not ‘typical’ and an epidemic does not conform with accepted temporal patterns, then there is some basis to the claim that an epidemic has artificial causes” (Chen et al., 2018).
The epidemic curve of MERS‐CoV cases in Saudi Arabia was generated using the available data (Fig. 1). It shows that the number of cases was substantially different each year from 2012 to 2018, and the lack of seasonality was striking, with inconsistent timing and size of the peak each year, and a mixture of sporadic and epidemic patterns. Apart from that, MERS‐CoV, with R0 (reproductive number) close to 1 (the epidemic threshold) (MacIntyre, 2014), has paradoxically persisted over seven years, much longer than SARS, which had a R0 > 2 and was controlled within eight months (MacIntyre, 2014). In South Korea, the MERS‐CoV outbreak ended in two months after the first cases reported. The ongoing introduction of infection to humans in Saudi Arabia, without evidence of camel contact in most cases, is unusual. For these reasons, 3 points were allocated to this criterion.
image
Fig. 1
Open in figure viewerPowerPoint
Epidemic curve of MERS‐CoV by date of symptoms onset in Saudi Arabia, 2012–2018. 










3.1.9 Criterion 9: Unusually Rapid Spread of the Epidemic

It is defined as “The speed at which some epidemic spreads is determined by the virulence, resistance and concentration of the pathogen, the contagiousness of the disease and the intensity of the transmission process, on the one hand, and on the susceptibility and disposition of the exposed population, on the other” (Chen et al., 2018).
In Saudi Arabia, sporadic MERS‐CoV cases have been reported consistently since 2012 (Chen, Chughtai, Dyda et al., 2017), but there were some instances of rapid spread, such as nosocomial outbreaks (Assiri et al., 2013; Oboho et al., 2015). Generally, the spread of MERS‐CoV in Saudi Arabia has not occurred as rapidly as the SARS‐CoV outbreak of 2003, which had 8,098 human cases within eight months (Lu et al., 2015). Thus the criterion was assessed as 1 point.

3.1.10 Criterion 10: Limitation of the Epidemic to a Specific Population

This criterion is defined as “Biological attacks can be directed against large heterogeneous population groups and military contingents or against selected target groups” (Chen et al., 2018).
MERS‐CoV from Saudi Arabia has spread to 26 countries, however, none had long‐term seeding of the disease at the destination country. South Korea has been the only country which had a nosocomial outbreak, resulting in 186 human cases but ended within eight weeks, with no further cases since (Chen, Chughtai, Dyda et al., 2017). MERS‐CoV has primarily circulated in the population of Saudi Arabia, for a period of seven years. Around 80% of global MERS‐CoV human cases occurred in Saudi Arabia, where the epidemic is still ongoing. The majority of Saudi cases (77%) were local residents, and 23% were expatriates. MERS‐CoV infection occurred in any age group, but middle‐aged adults (50s) were most commonly affected, particularly males (male:female ratio ≈ 2:1) (Table III).
Given the high prevalence and apparent limitation of MERS‐CoV in Saudi Arabia population, it could be concluded that the disease is largely limited to a specific population. Therefore, this criterion was assessed as 3 points.

3.1.11 Criterion 11: Peculiarities of the Clinical Manifestation

The assessment is based on “If the clinical manifestations of the disease are to be expected. If a pathogen is to be efficiently ingested, biological agents would most likely to be released as aerosols or be used to contaminate food and water. As a result, we could expect primarily pulmonary manifestations or typhoidal types of symptoms if an aerosol attack had occurred, and mainly intestinal manifestations if the food‐borne pathogen had been ingested” (Chen et al., 2018).
The disease spectrum of MERS‐CoV infection ranges from asymptomatic or mild respiratory symptoms to rapidly progressive severe respiratory failure and death. Typical symptoms include fever, cough, and shortness of breath. Pneumonia is common, and gastrointestinal symptoms, such as diarrhea, have been reported occasionally. Complications, such as pneumonia and kidney failure, are common in severe MERS‐CoV cases (WHO, 2017). In Saudi Arabia, most cases have severe disease (WHO, 2017). The case fatality rate is 39%, substantially higher than SARS (12%) (Gardner & MacIntyre, 2014; MacIntyre, 2014), and 64.5% of cases have comorbidities (Table III), such as chronic heart, lung, and kidney diseases, diabetes, and cancer. This criterion was assigned with 1 point because there was no striking abnormality in the expected clinical manifestation, except for a high case fatality rate.

3.2 The mGFT: Risk Assessment Criteria and Rationales

3.2.1 Criterion 1: Existence of a Biological Risk

Assessment points were the same as the original GFT criterion, which had the same definition.

3.2.2 Criterion 2: Unusual strain

This is a revised criterion of “special aspects of the biological agent.” The updated definition is “In unnatural outbreaks, the strains may be atypical, rare, antiquated, new emerging, with mutations or different origins, genetically edited created by synthetic biotechnology. It may demonstrate increased virulence, unusual environmental stability, resistance to prophylactic and therapeutic measures, or difficulty in detection and identification” (Chen et al., 2018). The assessment was also 3 points given the same rationales of the criterion “special aspects of the biological agent” in the original GFT.

3.2.3 Criteria 3‐10

The seven criteria were assessed as the same points as the original GFT because of no changes made on these criteria and definitions.

3.2.4 Criterion 11: Special Insights

The criterion is defined as “Any suspicious circumstances identified prior to the outbreak, during the period of outbreak or post‐outbreak, which would point to an unnatural outbreak. Examples could be a suspicion raised by someone, or an anonymous message or mail, or other intelligence not included in the criteria above” (Chen et al., 2018).
MERS‐CoV may trigger an epidemic through a single traveller, as seen in the large outbreak in South Korea in 2015. However, in mass gatherings such as the annual Hajj pilgrimage in Saudi Arabia (week 43 in 2012, week 41 in 2013, week 40 in 2014, and week 39 in 2015), no significant increase of incidence has been documented (Fig. 1), which is an unusual phenomenon in the presence of sustained, ongoing human infections in Saudi Arabia. Due to lack of other insights, this criterion was assessed as 1 point.

3.3 Final Score

Using the scoring system of original GFT (Table IV), the result showed 40 points out of 54 points, indicating a likely unnatural outbreak (likelihood 74%). For the assessment by the recalibrated GFT, the result showed 40 points out of 60 points (likelihood 67%) (Table V), also indicating likelihood that MERS‐CoV outbreak in Saudi Arabia may be unnatural.



Table IV. Original GFT: Risk Assessment of MERS‐CoV Outbreak in Saudi Arabia, 2012–2018

CriteriaAssessment PointsWeighting FactorOutcome/Maximum points
1. Biorisk224/6
2. Biothreat236/9
3. Special Aspects339/9
4. Geographic distribution313/3
5. Environmental concentration122/6
6. Epidemic intensity212/3
7. Transmission mode326/6
8. Time313/3
9. Unusually rapid spread111/3
10. Population limitation313/3
11. Clinical111/3
Total pointsAssessment points × weighting factor40/54
Likelihooda74%—Likely
  • a Likelihood of a biological attack = total points/54 × 100; 0–33%, unlikely; 34–66%, doubtful; 67–94%, likely; 95–100%, highly likely.



Table V. mGFT: Risk Assessment of MERS‐CoV Outbreak in Saudi Arabia, 2012–2018

CriteriaAssessment PointsaWeighting FactorOutcome/Maximum points
1. Biorisk236/9
2. Unusual strain339/9
3. Geographic distribution313/3
4. Environmental concentration133/9
5. Epidemic intensity212/3
6. Transmission mode313/3
7. Time313/3
8. Unusually rapid spread111/3
9. Population limitation326/6
10. Clinical111/3
11. Special insight133/9
Total pointsAssessment points × weighting factor40/60
Likelihoodb67%—Unnatural Outbreak
  • aAssessment of a criterion: 0, No data; 1, Uncertain; 2, Obvious peculiarities; 3, Clear indication or proof of biological attack.
  • bClassification by likelihood: <50%, Natural outbreak; ≥50%, Unnatural outbreak.

4 DISCUSSION

Based on available data and published evidence, the use of both the original GFT and mGFT suggested likelihood of MERS‐CoV being an unnatural outbreak in Saudi Arabia. The findings are consistent with a previous qualitative assessment, which showed a discrepant epidemiology of MERS‐CoV (MacIntyre, 2014). This study does not comprise proof of bioterrorism, but could be helpful to further investigate the origin of human MERS‐CoV infections. Epidemiology and risk analysis can only flag unusual patterns, but proof of bioattacks is the remit of law enforcement and intelligence agencies (MacIntyre, 2015a).
Historically, many unnatural outbreaks have not been identified as such at the time. For example, the large salmonellosis outbreak in Oregon in 1984, which was caused by Rajneesh cult who deliberately released salmonella in salad bars in 10 local restaurants, was wrongly judged as a natural outbreak by public health experts (Török et al., 1997). Public health authorities would never have known that this was bioterrorism, except for a confession by Bhagwan Shree Rajneesh six months later, and the accidental discovery by the Federal Bureau of Investigation (FBI) of their laboratory containing the exact outbreak strain a full 12 months later (Török et al., 1997). Another example is Operation Seaspray, where bacteria were sprayed over San Francisco in open air testing by the U.S. military in 1950 (Wheat, Zuckerman, & Rantz, 1951). Treating doctors noted an unusual resultant illness in exposed people, but health professionals never identified the illness to be unnatural in origin, and the cause was not known until 20 years later, when it was disclosed (US Army, 1977). Unless the question of natural or unnatural origin is asked, unnatural outbreaks cannot be identified. Public health training, practice, and culture defaults to the assumption that every outbreak is natural in origin, and does not routinely include risk assessments for bioterrorism (MacIntyre, 2015a). Even if the questions were asked, except for smallpox which was eradicated in 1980 (WHO, 2019b), all known and new emerging infections cannot easily be discerned as natural or unnatural. Routine use of tools such as the GFT could assist in this differentiation and may be more relevant in an era of accessible biotechnology.
The risk of unnatural epidemics is high in the era of synthetic biology and genetic engineering technology. An extinct poxvirus, closely related to smallpox, was synthetically created in Canadian laboratory in 2017 using the mail‐ordered DNA (Koblentz, 2017). There are more than 150 private companies providing genetic sequences to biomedical laboratories worldwide, including self‐regulated do‐it‐yourself (DIY) laboratories (Global Engage, 2017). The technological capability is present to give rise to unnatural epidemics, compounded by the risk of laboratory accidents and insider threats (MacIntyre et al., 2017). The question, therefore, is whether risk analysis tools such as the GFT should be routinely used in public health when investigating outbreaks.
Interestingly, in this study, the original GFT scored MERS‐CoV higher than the mGFT, but it does not indicate that the latter is less sensitive than the original GFT. The two scores are not directly comparable due to changed weights, different thresholds, and total points. The significance is that this study shows the value and flexibility of the mGFT in assessing and predicting the risk for an ongoing outbreak with simple criteria.
To conclude, the risk analysis of MERS‐CoV outbreak in Saudi Arabia flagged likelihood of an unnatural origin using both the original GFT and mGFT. Further investigation of this possibility is a matter for intelligence and law enforcement agencies in Saudi Arabia. In the era of rapidly expanding and accessible technology for synthetic biology and genetic engineering, as well as stated intent of terrorist groups to use biological attacks (Hummel, 2016), use of the highly sensitive risk assessment tool, the mGFT, could be integrated into routine outbreak investigation by public health authorities.

ACKNOWLEDGMENTS

This research was supported by the Australian National Health and Medical Research Council (NHMRC)–Centre for Research Excellence (CRE), Integrated systems for epidemic response (ISER) grant (APP1107393).

    REFERERNCES

    Comments

    1. Theresa williamsApril 29, 2020 at 2:16 PM

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