For the last three decades, the outbreak events have constantly increased and became
more complex to prevent, predict and contain. Nowadays, health authorities concern
is to identify which ones are bioterrorist outbreaks. However, natural outbreaks and
biological attacks have a too intertwined nature to be considered separately and hence,
in the absence of any attack evidence, differentiate them is a delicate task requiring
complex, long and rigorous scientific investigations. Furthermore, and as demonstrated
by the COVID-19 outbreak, the effectiveness of the response to an outbreak is closely
dependent on the timeliness of the response: the effort should thus rather focus on
the development of early detection and preparation measures such as the development
of global contingency plans organising the action of all entities (civilians, militaries,
governmental and non-governmental) in a common effort. Innovative Artificial Intelligence
is becoming unavoidable to detect the crisis and to manage it, especially in the phases
of preparedness and response effectiveness. This technology largest impact will be
to complement and enhance human capabilities but cannot substitute them. The human
experts monitoring new threats and able to work with these systems will stay at the
centre of the stage.
In the last thirty years, the pace of emerging infectious disease outbreaks has significantly
increased [1]. The globalisation of international exchanges contributes to the inefficiency
of common quarantine measures to contain the disease [2]. The last Ebola outbreak
in 2014 in West Africa was regarded as a paradigm of the issues caused by emerging
infectious diseases nowadays: this extremely deadly pathogen has naturally emerged
in a large new area, and its overwhelming spread has subsequently impacted Europe
and the United States [3]. This observation was confirmed and emphasised by the coronavirus
disease pandemic (COVID-19) caused by the new human coronavirus SARS-CoV-2. The effectiveness
of the ongoing lockdown of billions of people during the COVID-19 will have to be
evaluated and compared to other strategies. Thus, outbreaks can no longer be considered
as a local and distant issue but should be regarded as a global concern [4].
Of course, in History, some outbreaks have been the starting point of biological attacks,
even long before the discovery of microbiology. In 1346, Mongols exploited the second
plague pandemic by catapulting the bodies of soldiers died from plague over the city
walls of Caffa [5]. In the same lines, in the 18th century, the distribution of infected
blankets from a smallpox hospital of English settlers probably caused the deadly smallpox
outbreaks in the Native Americans population [5]. In the 20th century, after the discovery
of microbiology, a period of extensive industrial biological weapon programs started
with scenarios of massive biological attacks against military troops. Since 2001,
the threat is considered more focused on actions against the population or vital interest
points of the nations. These biological attacks could be perpetrated by state or non-state
groups in the context of low intensity or hybrid wars and bioterrorist attacks [6].
Nowadays, when an outbreak occurs, one of the first concern of the authorities is
to separate a natural outbreak [7] from an intentional act involving a biothreat agent
[5] in order to adapt their management. Even the SARS-CoV-2 did not escape the suspicion
to have been laboratory-engineered [8,9].
Thus, this review will show that there are no easy ways to distinguish one from each
other but that they share the same consequences and hence should have a shared management.
Accordingly, group together preparation measures and response tools against both the
emergence of an unknown pathogen and an unpredictable attack will optimise the effectiveness
of the response.
NATURAL OR INDUCED OUTBREAK: HOW TO DISTINGUISH THEM?
The Biological Weapons Convention signed in 1972 outlaws the use of biological weapons
[10]. Since then, the identification of a biological attack is a major international
political and judiciary issue [11]. However, multiple nested events such as global
warming [12], natural catastrophes [13], human actions [14] and conflicts [15] affect
natural outbreaks in an unpredictable way [16]. Several authors proposed algorithms
to determine, during crisis or shortly after, if the biological event had natural
or induced causes [17-19]. Except for some criteria, like evidence of a release explicitly
referring to attacks, the great part of the arguments should be carefully analysed
before being attributed to a biological attack.
The agent specificity
The use of some spontaneously rare agents could denote a criminal origin, as it has
been the case with the use of Bacillus anthracis during the Amerithrax crisis in 2001
[20], and, to a lesser extent, with the Aum Shinrikyo sect in Japan in 1993 [21].
However, the agent is not a sufficient criterion to identify natural or induced biohazard.
For example, the Rajneesh sect used a quite common Salmonella enteric [22] to perpetrate
attacks, and some bacterial toxins are considered as a potential warfare agent precisely
because of their high prevalence [23]. In sharp contrast, recent natural outbreaks
involved top select agents like Ebola virus in West Africa in 2014-2015 [3] or Yersinia
pestis causing pulmonary plague in Madagascar in 2017 [24]. Even the emergence of
a peculiar new strain cannot be a stand-alone criterion to differentiate both events.
Indeed, even if there is no evidence of using such agents through history, natural
agents can be modified by humans to increase their transmission, lethality or drug
resistance capabilities [5]. At the same time, some natural outbreaks were caused
by naturally altered pathogens like the Escherichia coli O104:H4 in Europe in 2011,
a strain that acquired and combined unusual virulence factor and drug resistance genes
[25] or in 2003 the new human coronavirus (SARS-CoV) identified with surprise in front
of severe acute respiratory syndrome cases [26].
Photo: Two sides of the same coin (from Lionel Koch’s collection, used with permission).
The spatial and temporal distribution
If a pathogen is detected in a location where it has never been detected before, it
can constitute a hint for a biological attack suspicion. It was the case with the
Amerithrax crisis in 2001 when a Texan B. anthracis strain was found on the East Coast
of the USA [20]. However, the biggest outbreak of the Ebola virus occurred in a part
of the continent recognised as free of the disease until then [27]. One other clue
for biological attacks could be the seasonality, arguing that if an outbreak appears
during a season not compatible with the pathogen time-life, human activity could be
the cause [5]. Here too, some natural outbreaks disrupted all rules like the Influenza
virus H1N1 pandemic in 2009, which appeared in April in North America with two epidemiological
spikes [28]. It unusually emerged from infected pig populations and was followed by
a unique global spread [29].
The origins and dynamics
Multiple starting points are commonly considered a sign of a biological attack like
the five letters containing B. anthracis spores [20] as well as the several restaurants
targeted by the Rajneesh cult [22]. An attack can also occur in a single place, like
the “Shigella dysenteriae poisoning” in a laboratory in 1996 in the US, where one
unique set of pastries has been deliberately contaminated by a laboratory strain [30].
In contrast, the natural tularaemia outbreak in Kosovo in 1999-2000 reached several
districts simultaneously in a tensed geopolitical context [31] and, in 2017, the plague
outbreak in Madagascar had multiple index cases [24]. Even the assumption that an
unusual swift spread or a large share of the population rapidly affected could be
evidence for a biological attack is disputable: recent terrorist actions used non-contagious
pathogen and hence reliable epidemiological data for the intentional use of a contagious
disease do not exist [5]. By contrast, the influenza virus [28], the 2003 SARS-CoV
[26] and the SARS-CoV-2 [32] propagated very fast all around the world with more than
200 countries affected in one year for the first one and 30 countries in 5 months
for the second. For the current COVID-19 pandemic, the Centre for Systems Science
and Engineering (CSSE) of Johns Hopkins University (Baltimore, MD, USA) created a
website to visualise and track the reported cases in real-time [33]. In April 2020,
less than five months after the first alert, 185 countries declared at least one case
of infection (https://coronavirus.jhu.edu/map.html). In the same vein, the last Zika
virus natural outbreak showed an unusual spread, as it emerged in Africa, travelled
across the Pacific Ocean to finally trigger a pandemic in South America [34].
Is there any interest to identify one from the other?
Thus, to characterise an infectious phenomenon, we should merge the most advanced
technics with thorough epidemiological investigations. Results have to be interpreted
very carefully by taking into account contextual elements and technical biases to
avoid any misunderstanding [35]. The list of common-sense items is beneficial to process
data and improve awareness but should not be solely used to distinguish the origin
of an ongoing event because of a lack of reliability (
Table 1
). It should be noted that all criteria and weightings have been determined retrospectively
based on past outbreaks and bioterrorist attacks. One of these algorithms has been
reviewed in the light of new infectious events but have not yet proven its effectiveness
on a prospective basis [36]. The confusion surrounding these criteria confirms that
both phenomena have intertwined nature. Moreover, during a natural outbreak, depending
on the knowledge about its hazardousness and transmission, the infectious agent can
be secondarily regarded as a biothreat agent, like it is now the case with the US
department of justice currently considering people who intentionally spread the SARS-CoV-2
as terrorists [37]. However, these political considerations are far away from health
workers and do not consider the public health issues of quick detection and response.
Indeed, even if the substantial remaining risk in the case of an attack is the possibility
of secondary actions aiming to maximise damages to the emergency infrastructure [38],
the real challenge for global safety remains the early detection, the accurate characterisation
and the establishment of specific measures, whatever the outbreak origin [39,40].
During the COVID-19 crisis, it had been estimated that the early detection and isolation
of cases would have been more efficient to prevent infections than travel restrictions
and contact reductions [41].
Table 1
Published consensual criteria to assess an unusual outbreak and infectious events
in function of the presence or the absence of criteria
Criteria*
Present
Absent
Selected agent
Amerithrax (USA)†
Rajneesh attack (USA)†
Aum Shinrikyo attack (Japan)†
Common bacterial toxins†
Neurotoxin botulinium A (Worldwide)‡
Shigella dysenteriae (USA)†
Ebola virus (West Africa)‡
Yersinia pestis (Madagascar)‡
Emergence or altered pathogen
E coli O104:H4 (Europe)‡
All biological attacks†
SARS-CoV (Worldwide)‡
Unusual distribution
Amerithrax (USA)†
Ebola outbreak (West Africa)‡
H1N1 Influenza (Worldwide)‡
Multiple starting points
Amerithrax (USA)†
Shigella dysenteriae (USA)†
Rajneesh attack (USA)†
Aum Shinrikyo attack (Japan)†
Francisella tularensis (Kosovo)‡
Yersinia pestis (Madagascar)‡
Unusual spreading
H1N1 Influenza (Worldwide)‡
All biological attacks†
Coronavirus (Worldwide)‡
Zika virus (Worldwide)‡
*Consensual criteria.
†Induced cause.
‡Natural cause.
HOW TO EARLY DETECT THE UNEXPECTED?
The challenge of an early detection
Some diseases like influenza are internationally monitored [42] while some others
are subject to active surveillance in an outbreak context, like the Ebola virus during
the last outbreak in West Africa [43]. For such well-known diseases, the case definition
is clear and an outbreak is declared when the number of cases exceeds what has been
expected [44]. This classic and passive way of detection is efficient but has numerous
drawbacks as it requires an expensive and complex public health network and is often
activated with a certain delay. However, when it comes to a new disease or pathologies
with polymorphic or nonspecific symptoms, the case definition and the outbreak declaration
threshold are subject to debate [45]. The source of the infection can be as unpredictable
as the local outbreak of anthrax in reindeers triggered by a permafrost melting [46]
or the detection of the variola virus in ancient mummies [47].
Most of the time, the high volatility lying in the infectious process complicates
the record of the cases. For the same exposition, symptoms can differ according to
individual variables like health status or genetic factors [48] or to collective variables
involved, for example, in the chain of transmission [49] but also cultural or socio-economic
factors: the most-disadvantaged individuals will develop more severe and hence more
specific forms of the disease but will have a belated use of health care [50].
On the other hand, systematic environment monitoring for all diseases is, for now,
impossible due to technological barriers and cost challenges. Experts in biodefense
suggested that more targeted measurements in delimited spaces or during a large gathering
of people should be the priority to improve the sensitivity and specificity of the
early detection of a biological attack but, also for a natural outbreak, while reducing
the cost [51]. For example, the analysis of wastewater could be a good way to monitor
the spread of SARS-CoV-2 in the community [52]. However, these measurements should
always be paired with epidemiological investigations to avoid any misinterpretation
of the results [51].
Thus, for the moment, health workers would first notice an unusual event (disease
or an unusual number of cases) and should be able to alert public health officials
[44] as protecting themselves from contagiousness. Given the importance of early detection,
training has to be a building block in infectious diseases programs in order to promote
unusual event awareness [53]. The implementation of information technologies leaves
room for improvement in the outbreak detection process [54] as more and more stakeholders
of the health care system use informatics tools in their daily practice. Yet, considerable
efforts have been made on information technologies and electronic query of a data
set to improve the efficiency of surveillance [55]. It's an imperative prerequisite
for the implementation of an electronically assisted surveillance. Currently, data
management tools can aggregate several inputs and are already used for epidemiological
studies or trigger an alert [56].
To a connected age
Internet-based surveillance systems offer a logistically and economically appealing
extension to this traditional surveillance approach. The results are immediate and
allow access to a paucisymptomatic population or people who are not using the health
care system [57]. This methodology has been used in 2020 in China to reconstruct the
progression of the SARS-CoV-2 outbreak [58]. Despite ethical concerns and regulatory
barriers, social networks appear to be a powerful data collection tool and can also
be used as a medium to communicate sanitary messages or alerts [59]. However, here
again, these data are subject to many biases and should be carefully interpreted.
Indeed, the simple act of online documentation is just an indirect marker of disease,
and such detection system could trigger an alert just because a worldwide released
blockbuster movie increases the anxiety of population or a massive hacking produces
millions of requests.
Taken to its logical extreme, the next step of this epidemiologic watch would probably
allow the contribution of the internet of things (IoT) already used to follow chronic
illness [60] and for biomedical research [61]. A smartphone or a smartwatch is now
able to detect modifications of vital parameters like temperature or heart rate. The
capability of crossing these types of information with, for example, geo-tracking
solutions, could alert competent authorities on an ongoing infection and help them
to implement more rapidly appropriate measures and focus on a possible source of contamination.
This seems to be only the beginning of IoT possibilities as the future could be even
more connected with the development of projects like smart cities. Nowadays, China
is already using video surveillance systems to follow its citizens and detect incivilities
[62]. Likewise, criminality hot spots prediction by artificial intelligence (AI) is
no more fictional in Los Angeles [63]. These new technologies already have some applications
in epidemiology as the detection in real-time of restaurants with a higher risk to
be sources of foodborne diseases [64]. In the medical field, computers start to help
clinicians in the diagnostic of mental illness through the facial expressions and
head gesture in a video [65] but could also be used to detect an infectious disease
at the prodromal phase with potential highest efficiency than thermic portals. The
crossing data obtained from surveillance systems combined with machine learning capabilities
in prediction and diagnostic could be used to help early detection of an infectious
phenomenon in a population. This early detection could guide further specific actions
of screening to identify potential patients and even the source of the infection.
In Korea, during the COVID-19 crisis, GPS from cellular phone, credit card transaction
log and video footage had been used to monitor the patient’s contact and avoid further
transmissions [66]. However, the implementation of such surveillance systems is not
without legal and ethical issues and should be carefully considered. The privacy policy
has to be carefully examined as well as the securing of the transmission and storage
of sensitive medical data, not to mention the possible human rights abuses in non-democratic
countries [67]. These concerns have already been raised during the current COVID-19
pandemic [68] but there is still no international consensus on the use of personal
data.
Pending the advent of AI tools, many initiatives have been recently proposed to facilitate
the investigation of epidemics in the genomic era. The whole-genome sequencing already
can help to determine the origin of an outbreak and also to explain the dispersion
of the pathogen through its local evolution [69]. New tools may include online data
processing [70] up to the development of original algorithms to aggregate spatial,
temporal, epidemiological and genomic data [71]. The interactions of this technological
surveillance system with the previously described classic one are also possible at
the condition to continue to improve the data-sharing practices [72]. The use of the
Nextstrain tool [73] in the context of SARS-CoV-2 (https://nextstrain.org/ncov) perfectly
illustrates the potential of such approaches in the context of spreading epidemics
[74]. In the years to come, the epidemiological monitoring system of our societies
will probably rely on economic capacities, technical development capabilities and
societal choices, with the common objective to early detect outbreaks, regardless
of their causes (
Figure 1
).
Figure 1
Possible future detection and management system for outbreaks. In blue: the population
and the detection resources for infectious events. In red: the stakeholders of the
crisis preparation and management. In green: the response to the outbreak through
communication and specific actions.
CRISIS MANAGEMENT
Early detection for an early reaction
Even if the epidemiological monitoring is the crucial step to respond to an outbreak,
detection is useless if the resources to deal with the crisis are unavailable. Being
prepared includes but is not limited to health workers being trained to detect, react
and alert the health authorities: quick and reliable equipment has to be available
and health workers have to be used to work with them. Dedicated infrastructures have
to be prepared and ready for activation and Personal Protective Equipment (PPE), intensive
care devices and treatments have to be stockpiled. The COVID-19 crisis revealed that
the lack of simple PPE put the all health system at high risk [75]. Several authorities
(civilians or militaries, governmental or non-governmental entities) already have
some contingency plans but the compartmentalisation between different governmental
branches and the nebulous labelling of the means between natural outbreak or bioterrorist
attack dedicated sometimes prevent an accurate global appreciation [76]. As it is,
and as unfortunately still demonstrated during the COVID-19 pandemic, if an outbreak
would occur, there is a risk, even for the highly trained first aid service in the
most developed countries, to get in each other’s way. By learning how to work together,
synergies could be developed to improve health response [77]. After the failure in
the control of the last Ebola virus outbreak by the WHO, international agencies called
for better international preparation to respond to future outbreaks [78]. Thus, international
and European research networks managed to improve the speed and effectiveness of the
present deployment on a validated diagnostic workflow for SARS-CoV-2 [79]. This demonstrating
the response capacity that can be released through the coordinated action of academic
and public laboratories like PREPARE [80]. In 2020, in China, coordination by the
central authority allowed to deploy medical staff and built new hospitals in Wuhan
in a tight schedule. In Europe, crisis management strategies were different among
countries, but cooperation helped relieve overloaded Intensive Care Units in some
regions and saved lots of patients. In the meantime, other consortiums like GRACE
may also help us to prepare the possible future sanitary crisis [81].
Preparedness technologies
Developments of AI do not only help for early detection, but make available a full
set of possibilities in crisis management to the authorities. By using classic risk
analysis documentation with AI tools, it is now possible to generate predictions to
improve the resilience of a system and to mitigate the risk [82]. The preparation
phase of the crisis can also benefit from AI tools by ordering the reuse of information
from previous crises [83], improving the stockholders’ training with a serious game
approach [84], helping to design realistic plans [85] or even boosting the discovery
of new drugs [86]. Resources management can also be improved by the use of AI in terms
of network structuration [87] as well as for the mean’s allocation [88]. During the
crisis, AI can also sort information from many sensors, merge it and make it relevant
for the user responsible of the situation assessment [89], which will be helped by
a decision-support system [90] to design the best crisis response. For example, during
the COVID-19 crisis, social contact matrices had been used to project the benefit
to maintain social distancing measures [91]. Over the past ten years, epidemiological
and mathematical modelling data were essential for risk characterisation and management
during infectious disease outbreaks [92] but ironically, the rising power of AI systems
will not erase the role of human experts [93]. Indeed, intuition and emotions are
known for a long time to be part of the decision-making process [94]. During crisis
management, expert intuition developed through years of practice is described as more
realistic than pure analytical thinking [95]. Moreover, both intuition and creativity
are part of the problem-solving process [96]. Both experts and AI will have to learn
how to work together and assist each other by developing collaborative intelligence,
which will be the best way to solve complex problems (
Figure 1
). This was experienced during the COVID-19 crisis in which experts, assisted by simulations,
had to make decisions to control the spread of a virus still little known.
DIMENSIONING THE GLOBAL PREPAREDNESS
Economic impact
Inevitably, to develop an anticipation-centred view required investment. The economic
justification of such an investment was underlined for a long time (even before the
Amerithrax crisis) [97], and recently, a panel of USA experts recommended reinforcing
the biological threat characterisation research at a federal level with clear safety,
ethical and practical guidelines [98]. Splitting outbreaks into two causes is not
cost-efficient and seems absurd as dangerous pathogens to human can be used for biological
attacks but are first and foremost causing natural outbreaks [99]. However, studies
about the burden-adjusted research intensity showed that diseases with a greater impact
are still underfunded [100] in an economical context where citizens are more and more
concerned by public expenses. Indeed, if the vaccine policy implemented were economically
profitable in the USA during the 2009 Influenza pandemic [101], a similar strategy
caused substantial wastage in Australia [102]. Thus, authorities have to be very careful
to dimension their actions appropriately, even though a delayed response is severely
judged by public opinion as during the 2014 Ebola outbreak [78]. Hence, authorities
and experts should improve the global contingency plans, especially on catastrophic
biological risks, which represent a small portion of the biological threats but with
substantial potential consequence for humanity [103].
For a health care system, the preparation for a biological attack [6,104] or a natural
outbreak [78,105] is globally the same challenge. Moreover, preparedness for biological
attacks has a significant added value that helps to strengthen preparedness for natural
outbreaks, and vice versa [104]. It is therefore economically interesting to consider
the natural biological risk and the possibility of an attack as a single threat in
the preparation of the response to an infectious event with epidemic potential. The
crisis generated by the numerous deaths of COVID-19 and the lockdown of billions of
people will probably trigger a new evaluation of public policies to control outbreaks
with the opportunity that the public opinion will look at it through fresh eyes.
Misguidances and consequences
Indeed, the uncertainty associated with scientific knowledge often challenges decision
making and opens the way to the contestation of expertise [92]. Sometimes, the best
intentions can result in a health disaster, such as the deployment of a peacekeeping
force and the cholera outbreak in Haiti in 2010 [106] or the project of spreading
some modified mosquitoes to fight against malaria [107]. Technology allows us to modify
organisms specifically leading to the reconstitution of the Spanish Influenza virus
[108] or to unexpected results as a test for a new poxvirus vaccine resulted with
an ultra-virulent strain able to neutralise the immune system [109] or, during research
experiments mimicking natural phenomenon, the generation of highly-resistant B. anthracis
spores [110] and viruses acquiring airborne transmission [111]. Nowadays, these widely
used technics appear to be almost obsolete in comparison with the new capacities of
gene synthesis: a horsepox virus has been reconstructed using only internet-bought
sequences [112], and a new bacterium has been created de novo in a laboratory [113].
Currently, the possibilities of genome editing technologies like CRISPR-Cas9 seems
to be limitless [114]. Some malicious scenarios have already been imagined with a
genetically modified virus infecting mosquitoes able to perform gene modification
of crops in a field [115]. The South African « coast » project [116] that aimed at
developing a bacterial agent able to selectively kill a part of the population could
now be a terrifying technical possibility. Thus, even if applications of some of these
modified organisms may be highly beneficial, as the recycling complex wastes [117],
they are swamped in the middle of the wanderings reported by the media [118].
Due to all these miscalculations and misguidances, society lost confidence in the
authorities and national programs. It leads to society-born threats with notably the
growing emergence of highly antibiotic-resistant bacteria due to the improper antibiotic
use [119] or the re-emergence of nearly forgotten pathogens linked to the mistrust
in public health programs like vaccination programs [120]. This lack of confidence
extends to crisis management programs and can compromise outbreak management measures
the same way it happened with the Ebola outbreak in 2014 [121] or currently, with
the beginning of the management of the COVID-19 pandemic and the lockdown decision
[122]. However, during the COVID-19 pandemic, the transparency about its progress
reported in real-time, for the first time in the outbreaks’ history, lead to better
comprehension and cooperation of people [123]. Thus, every decision can have a dual
nature and should be considered carefully before being implemented (
Table 2
). That is why, while encouraging research, technologies and their application must
be controlled to avoid any misuse and major communication actions are needed to overcome
the public reluctance. Ethics in research and data publication must also be placed
at the centre of researchers' concerns.
Table 2
Duality of decisions in infectious phenomenon management.
Type of change
Positive effect
New risk
Science progress
Better understanding of infectious process
Creation of new threats
Internet screening
Weak signal detection
Data manipulation
Open data
Sharing of the knowledge
Misuses of the data
Improved surveillance systems
Early detection and characterisation
Privacy and human right violation
Use of AI
Collection and fusion data
Lose of human expertise
Increased communications
Better acceptance from the population
Fake news
AI – artificial intelligence
CONCLUSION
The intricate nature of natural outbreaks and biological attacks is too important
to consider them separately. To create an efficient way to detect and contain them,
the first step is to anticipate them by performing continuous scientific and epidemiological
monitoring. Still, the most serious and unpredictable events are referred as “Black
swan events” and despite our inability to foresee their occurrence, knowledge keeps
being the key concept to anticipate them [124]. Thus, we need to continue and intensify
networking at local, regional and global levels. Stakeholders from a broader range
of backgrounds must be involved to monitor the evolution of threats and update existing
procedures by developing concrete and immediately applicable solutions in crisis.
The biological crisis is becoming a field of expertise by itself, and it is no longer
enough to be a specialist in crisis management, infectious disease or epidemiology
to be able to understand the implications of their own decisions fully. New technologies
and AI will have more impact on crisis management, and experts will have to better
work with these tools to improve their preparedness. The evolution of threats as well
as technologies developments will require permanent adjustments in the strategies
to optimise the public health response. Communication will also be a key point of
the future strategies to promote the acceptance of financial and societal investment
by both the public authorities and the population and to avoid false information spreading.
Current COVID-19 crisis is the first pandemic to benefit from so much advanced research
and several major articles are published every day. However, SARS-CoV-2 is probably
not the deadliest virus we will ever have to fight. We must learn from this crisis
while preparing the next one.