Download Ebola in West Africa: lessons we may have learned

International Journal of Epidemiology, 2016, 5–12
doi: 10.1093/ije/dyv324
Advance Access Publication Date: 7 January 2016
Editorial
Editorial
Ebola in West Africa: lessons we may have
learned
Tom Koch
Department of Geography (Medical), University of British Columbia, 1984 West Mall, Vancouver, BC
V6T 1Z2, Canada. E-mail: [email protected]
Received 28 June 2015; Revised 29 October 2015; Accepted 11 November 2015
Introduction
We missed it
An outbreak of professional and popular articles followed
upon the West African Ebola epidemic which began
December 2013 in Meliandou, an isolated village in
Guinea. If they teach nothing else, it is that a complex of
events combine to permit a local infectious outbreak to assume regional if not, in the end, pandemic status. These
contributions may be separated into several distinct categories. The first includes a host of reports describing
Ebola’s virology and its history. They range from clinical
reviews of what has been learned about the virus since its
discovery in 1976,1 to focused reports on treatment issues
in the field.2 A second set has focused on the ‘zoonotic
niche’, the biogeographical environment that promoted
Ebola in local reservoirs, especially those including bat species populations,3 as a source of both the most recent and
perhaps previous outbreaks as well. 4,5 A third category
has added socioeconomic and geopolitical factors to that
biographical field. Popularly, this literature considered the
presumed failures of the World Health Organization6,7
and the budget cuts that had decimated its staff.8 But that
was only part of a history of more general failure in the
creation of regional and global programmes of health and
development.9 To that must be added a local and regional
anthropology documenting the local distrust of official
intervention by regional officials who seemed, at first, to
first ignore and then blame affected populations.10 All
these elements—anthropological, biogeographical, economic, political and social—contributed to what became
the most deadly and dangerous Ebola epidemic in history.
Valuable as this work has been, it does not address a fundamental but critically practical question: how did many
of the best minds in infectious disease, epidemiology and
disaster medicine miss the early spread of the Filovirus
from a remote village in Guinea until its presence became
regionally epidemic? In early May 2014, experts from the
Centers for Disease Control and regional politicians alike
assumed the outbreak was over and the disease contained.11 ‘We were happy’, the Liberian health minister,
Dr Bernice Dahn, later remembered. ‘We were not actually
expecting Ebola to come back and overwhelm us.’ Soon,
however, it did.
Dr Margaret Chan, Director General of the World
Health Organization, blamed the virus itself: ‘Old diseases
in new contexts consistently spring new surprises’.12 But
this epidemic, like the 24 previous Ebola outbreaks documented since 1976, began in the same context as its predecessors, a relatively isolated rural African village. What
made this case different? And why was its early expansion
unremarked until too late?
Epidemiology and firefighting
As Murdoch and Briggs put it, rather poetically,
‘[Infectious] disease spreads rapidly through the susceptible
population, similar to a forest fire fed by a glut of fuel, and
then dissipates as the fuel is spent. However, there are always glowing embers in the forest.’13 Although their focus
was measles rather than Ebola, the analogy is true of all
C The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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infectious diseases. Adopting the forest fire fighting metaphor not only permits a better understanding of infectious
disease dynamics but also the means by which they can be
identified and controlled.
The first lesson from firefighting is that all outbreaks
have the potential for expansion. When addressing a localized burn, the possibility of spread is assumed. A local fire
may seem to have been dampened, but the likelihood it is
quiescent rather than extinct requires vigilant regional attention. A localized outbreak may seem to be over but, as
epidemiologists in West Africa discovered, it may proceed
‘underground’, seeking a new fuel for its expansion. The
lesson is that every infectious disease event has the potential to become a regional epidemic, even when previous
outbreaks have been locally contained. Attention must be
paid simultaneously from the start to both scales, local and
regional.
kilometres away might be causally related to those
occurring weeks later in their home villages. The potential
for early containment was lost as a result.
In retrospect it’s easy to see how this occurred. ‘You
need medical people doing outreach and epidemiologists
investigating the cases. You need huge teams of contact
tracers, health promoters’.17 Contact tracers are epidemiology’s fire spotters, spatially locating the homes of affected persons and thus the ‘hot spots’ where cases cluster.
The result is analogous to the forester’s sighting of active
burns. Ideally, a patient or a member of the patient’s family is interviewed about the patient’s travel patterns during
a suspected incubation period (in the case of Ebola approximately 2 weeks) before symptom onset. These data
help create the environment of an outbreak and its potential for expansion in the same way as firefighters see a
brush fire in the context of a fire.
What happened?
A geographical approach
It is now generally accepted that the 2014 epidemic began
in 2013 with the infection of a two-year-old boy in the village of Meliandou in Guinea’s Guéckédo Prefecture.14
Local, national and international health officials assumed
at first that, like its predecessors, this outbreak would be a
static and thus controllable, localized disease event.
Because nobody anticipated the possibility of a dynamic
and expansive epidemic, programmes of triage management dictating immediate attention to regional disease containment protocols were not immediately implemented.15
As Médecins Sans Frontières’s Dr Armand Sprecher
later told one reporter, what nobody realized was that, like
most of us, ‘People in West Africa just get around. They
come in contact with the virus in Location A, then by the
time they get sick, they’re in Location B, which is 50 km
away. And if they’re unlucky enough to die in Location B,
maybe they get taken home to Location C to get buried,
where they’ll infect more people.’16 Because the outbreak
began among a rural people, the reflexive assumption was
that they were isolated and the virus thus would be naturally contained. As a result the epidemiological links between Locations A, B and C were unexplored.
Nobody realized until too late that the index case
occurred among a primarily Kissi speaking, indigenous
rural population whose members regularly visited similar
villages across porous, unmanned national borders
(Guinea, Liberia, Sierra Leone). Assuming a localized outbreak, disease experts did not anticipate the travel patterns
that existed between a set of Kissi villages on the one hand
and, on the other, villagers’ regular travel to and from the
large, coastal cities of all three countries. For their part, villagers did not perceive until too late that deaths in villages
This geographical approach to disease outbreaks has a
long history.17 Since the late 17th century, clinicians, researchers and officials have sought to contain epidemics,
organize patient treatments and understand infectious disease as a spatial problem. We see the traces of this perspective, I have elsewhere argued, in the maps that have
documented containment and treatment programmes
while investigating the cause of this or that epidemic expansion, from late 16th century plague to 20th century
West Nile Virus.18 This tradition serves today as a means
of understanding both the recent Ebola outbreak and,
I suggest, the means by which future epidemics might be
better contained.
The central thesis is that infectious diseases have a spatial structure that can be best understood based upon the
locational incidence of disease occurrence in an environment whose individual constituents promote or retard the
progress of an infectious disease.19 In recent years this spatial epidemiology, as some now call it,20 has come to rely
on a ready library of digital map resources—digital and
print versions—to which patient data can be added.
Those resources were unavailable to epidemiologists
and infectious disease specialists in Guinea in the first
months of the outbreak. Not only was the local health infrastructure minimal, but data on the spatial architecture
of the region—its maps and census data—were almost
non-existent. Clinic records published to date indicate that
patients were typically identified by family name, date and
village names. Even those coarse data were sometimes
problematic. In Guéckédo Prefecture, for example, 14 separate patient sites carried the same village name.21 Contact
checkers thus first had to identify the correct village and
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Figure 1. A hand-drawn map of Guéckédo Prefecture was the cartographic baseline from which Ebola volunteers began to create a mapped database
of the evolving epidemic. Courtesy: Médicins sans Frontières (Geneva).
then travel to it across rural roads that were at best minimally maintained. Once identified, it was often difficult to
locate homes because affected villages lacked well -codified
street numbering systems.
In March 2014, Médicins sans Frontières Switzerland
dispatched a GIS mapping specialist to support epidemiologists and physicians working in the affected region.10
As the outbreak expanded, the Humanitarian Open
Mapping Project (HOMP) sought to assist by developing
digital maps of affected villages and cities. HOMP is a ‘volunteer geographical information programme’ with over a
million volunteers who collectively map sites by tracing
streets (lines), polygons (local buildings) and landmarks
(points) from satellite images of target locations.22 HOMP
began in response to the massive cholera outbreak that followed upon a 2010 earthquake in Haiti. ‘If there was a
point source for cholera in Haiti, we wouldn’t have known
where it was’, MSF’s Ivan Gayton recently explained.23
‘We needed a map—to be able to correlate the alerts we
hear and our patient origins to something on the ground.’
But because that mapping is village- or town specific—
each village is its own map—the result is not easily employed in a more regional consideration of patient travel
patterns between villages. Thus potential viral transmission
pathways between towns and villages were unreported.
The result served as a description of individual outbreak
sites but not as a predictive tool of disease expansion. The
relation between communities in the tri-border region, and
between those villages and larger cities, thus was literally
unseen. The result was a classical problem of ‘not seeing
the forest for the trees’.
As a result, aggressive quarantine programmes were not
quickly enacted to isolate both villages where Ebola was
active and those at risk from villagers exposed but asymptomatic. Issues of quarantine were, for some, ethically
problematic24 and certainly national governments were reluctant to impose restrictions on their citizens.25 But a spatially grounded ‘scale of ethics’ focusing on both origin
and destination data would argue for a comprehensive programme of localized quarantine as part of an aggressive
epidemic engagement.26
Practicalities
GPS/GIS/Satellite locations
Firefighting technology has been revolutionized in recent
decades through the introduction of GPS (geographical
positioning system) devices that, relying on satellites, can
fix the latitude and longitude of any place on the globe to
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Figure 2. In response to a suspected dengue fever outbreak, students with GPS devices mapped the roads and homes of a village in India to construct
a digital map of infected (lighter = red) and uninfected (darker = blue) homes. Pratt 2003, courtesy: ArcUser Magazine.
within several metres.27 Locational data on an evolving
fire site are recorded and then transmitted to supervisors
responsible for posting its locations on a digital, computergenerated map in which data can be rapidly updated over
time. To this evolving fire map other environmental event
classes (prevailing winds and regional ground cover, for
example) potentially promoting the fire’s growth can be
added. The result permits the use of predictive models of
future risk based on evolving data.28
Even without well-articulated maps, however, the
mapped data can be instructive. There are a variety of GPS
receivers available today and their accuracy in various
conditions—including under a heavy forest canopy—has
been evaluated.29 All provide latitude and longitude data.
Some can record travel routes, adding travel distance and
time to the field of readily mappable data. Effectively, they
map the roads that a contact seeker travels. In response to
a 2001 outbreak of dengue fever in India, Dr Jay
Devasundaram enlisted local students to map, using handheld GPS devices, both the roads in the affected villages
and the homes on its streets (Figure 2).30 Based on this
work, sample populations were tested for Dengue fever.
The results served the immediate needs of the local health
authority responsible for treatment, and with a 2 x 2
contingency table based on the mapped data, planning for
patient care needs. As importantly, the results argued for
the introduction of mosquito control measures to inhibit
the spread of further disease.
This is not to argue that HOMP maps were superfluous,
only that their predictive value was limited. An approach
like Devasundaram’s has the advantage of rapidity, a first
responder’s first response to identifying the precise location
of a disease event in areas where neither codified street
data nor the maps that store them are available. Its data,
logged as coordinates, can be easily transferred to other
maps later. Employing community members in the mapping also served anthropologically, involving community
members in the disease response, teaching them about an
expanding viral event and its local effects. In areas where
there is distrust for foreign or official health workers, this
can be critical.
Intake mapping
Mapping has been recognized for some time as a cultural
universal.31 People with even minimal map literacy are
able to locate themselves in village or town maps and on
satellite images of those environments. This is true not
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Figure 3. presents Google Earth (left) and OMP maps of Quetzaltenango, Guatemala (right) where I once worked. Both permit rapid scaling to provide
a regional perspective. Note the Google Earth map has a tracing function enabled as a tool.
only of urban populations but also among non-urban,
agrarian people as well.32 Satellite imaging programmes
(Bing Maps, Google Maps etc.) are the basis for HOMP
map projects that typically permit roads to be highlighted
and the precise location of a place on the map (latitudelongitude) to be recorded (Figure 3).
Where map or satellite imagery is available it’s relatively easy to point to local landmarks (a church, hospital
or soccer field, for example). ‘Is your home over here, off
the Avenida and near the church, or over there, near the
soccer field behind the town square?’ Using a map to pinpoint the precise location makes it possible, with programmes, to both trace routes in and around the village (‘I
went from home to the market to the church’) and assign
coordinates to those locations and pathways. The use of
this kind of imagery during or immediately after patient intake can provide a simple method for the rapid accumulation of spatial data on the extent of an outbreak in a rural
location and the potential for infection within the community. If maps capable of accepting street-level locations
(‘1354 Avenida Leon’) become available, the GPS-created
data can be easily added to it.
patterns and thus the potential for regional disease expansion. If a destination village described is not known to the
interviewer, the map serves a locational function: ‘Was it
this Bendou, here, or perhaps the one near Mala?’; or, perhaps, ‘When you say you went to Mama last month was it
the one to the west, closer to Nongoa (in Guinea) or the
one south of Guéckédo nearer Sama (in Liberia)?’
The results are obviously consequential. If 15 now
symptomatic patients from Meliandanou (Guinea) all went
to an Ebola-related funeral in Sama (Liberia) a fortnight
previously, then that town is at risk. By identifying villages
at risk, planners may better anticipate future patient loads.
And, with a location/destination map, it becomes easier to
explain an epidemic to local persons. The map becomes a
discussion piece that people in distant villages can understand, tracing the route and the times it takes to travel during the incubation period of a disease. ‘Abdul, whose
mother recently died, was here 14 days ago. The disease
that affected her may have travelled with him even though
he seemed healthy. Now he is in hospital. So we want to
make sure the disease does not spread and if anyone here
gets sick that we can help’(Figure 4).
Origin/destination mapping
Ecological mapping
Because attention has primarily focused on mapping disease
incidence, the technology’s potential for diffusion mapping
has been overlooked. Satellite images and most digital map
programmes permit rapid scaling from local to regional settings. It is thus a simple matter to use a smaller scale to identify a greater geography. Using maps at this scale in patient
interviews permits a relatively rapid identification of patient
travel patterns during the pre-symptomatic phase of disease
incubation: ‘During the 2 weeks prior to becoming sick,
what other places did you visit?’
This is a potentially invaluable, if so far untested, approach that would rapidly characterize local travel
Ecological mapping’s goal in spatial epidemiology is to
identify the environmental conditions that promote disease
reservoirs. By mapping specific animal habitats, the potential for specific disease vectors to prosper can be assessed.
This mapping of broad environmental conditions is of a
different order of complexity and sophistication to mapping of local villages or village regions to identify the location of individual cases. It is the broad ‘forest’—literally
and figuratively—in which specific disease events may
occur. It thus serves as a parallel but distinct investigative
track in determining the potential of an infectious disease
to diffuse across a broad area.
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International Journal of Epidemiology, 2016, Vol. 45, No. 1
Figure 4. These Google maps of the Guéckédo region identify villages in the tri-national area and permit easy location of sites for diffusion mapping
based on patient travel during a pre-symptomatic, incubation phase: [http://www.nationsonline.org/oneworld/google_map_Conakry.htm].
Again, there is a long history to this kind of mapping.
Denis Burkitt undertook a ‘tumour safari’ in the early
1960s to discover the biogeographical region in which
what came to be known as Burkitt’s lymphoma was common.33 In that work, he identified by an altitude band and
a climatic range where the characteristic tumour was evident. Others mapped the biogeography of a range of diseases, including cholera and malaria.
Throughout the recent epidemic and into 2015, researchers attempted to identify both the origin of the original outbreak and its natural reservoirs. The suspicion
was that one or another native fruit bat, probably Eidolon
helvum, was the source of the original infection. The concern was that the presence of this or similar species might
contribute to new but separate infections spontaneously
occurring elsewhere.34 Similar concerns have been raised
about bats as reservoirs and thus potential vectors for a
range of diseases, including Marburg Haemorrhagic
Fever,35 Middle East Respiratory Syndrome (MIRS) and
Severe Acute Respiratory Syndrome (SARS).36
The assumption was that the original infection occurred
when 2-year-old Emile Ouamouno either ate food contaminated by bat dung (perhaps fruit) or, alternatively, ate
bat meat. In the affected area fruit bat is a preferred ‘bush
meat’ and a crucial source of protein. Typically it is
smoked or used in a soup or stew.37 Bat hunters are frequently scratched by their quarry and typically come into
contact with bat blood in their work.
The possibility of broad scale, zoonotic contamination
required the mapping of bat environments, and known
habitats in those environments, as potential viral reservoirs
that might encourage future outbreaks. Although the result
did not serve to limit the extent of the 2014 Ebola outbreak, it did identify a set of broadly biogeographical conditions that may promote future outbreaks of the virus.
Others have focused on deforestation and other environmental contributors to bacterial and viral evolution and
migration.38
Discussion
It is important to note, in passing, that this map-based approach is increasingly common in a range of emergency
situations. Following the earthquake in Nepal in 2015, for
example, it was unclear what the general and medical
needs were in affected towns and villages. Supplies were
sometimes routed to villages that could not be easily
reached by road or sent to villages where the supplies were
not needed. Using the resources of Humanitarian Open
Street Map and Digital Globe satellite data,39 volunteers
created Quakemap.org,40 a crowd-sourced mapping program to correlate reports of earthquakes in individual villages. Continually updated maps were regularly published
online as new data were reported by ground volunteers.
The goals were several, including identifying those villages most affected by the disaster; locating missing persons; and allocating relief supplies where they were most
needed. As one early responder said, ‘As soon as we got
there we realized that the situation was very different than
we had expected. There were already enough medical supplies in Sankhu, and we ended up having to bring the supplies back to Kathmandu. At that moment I realized that
to help the survivors it is essential to know what is needed
where.’41
None of this is to ignore, except for reasons of space,
the economic, cultural and geopolitical characteristics that
were contributors to the 2014 Ebola epidemic. As Daniel
Bausch put it, in a memorable phrase, the epidemic
occurred ‘where economy meets ecology’.42 Poverty and
war are always breeding grounds for infectious disease. So,
International Journal of Epidemiology, 2016, Vol. 45, No. 1
too, is the presence of the minimal national health infrastructures of impoverished countries. Deforestation promotes the evolution and transfer of microbes from
previously stable ecological niches to new areas where the
disease was previously unknown. There is, in other words,
a human ecology that promotes disease just as there is a
natural ecology likely to encourage the proliferation of
zoonotic host reservoirs. A thorough investigation of those
factors and their mapping as a predictor of disease incidence and diffusion must await, however, another paper.
The purpose of this paper has been to explore the recent
epidemic in the hopes that future disease outbreaks in rural
areas with minimal resources can be better and more rapidly assessed. None of this should be read as a criticism of
the efforts of local medical professionals or their foreign
colleagues who struggled to treat Ebola patients during the
recent epidemic. Personnel from all three infected nations,
Médicins sans Frontières, the International Red Cross, the
U.S. Centers for Disease Control and the World Health
Organization, among others, laboured heroically—often at
great personal risk—to restrict the original outbreak and
treat those affected by it.
The goal here has been to learn from their efforts, identifying procedures which might, in the future, help prevent
a similar disease progression. If there has been an undue
emphasis on mapping as a potential corrective, it is because
the use or non-use of maps provides a convenient means of
assessing issues that arose in the battle against Ebola in
West Africa.
In the end, the hope is that this review is not simply educational but practical. Just as forest fires are an annual, recurrent event in many parts of North America, so too
bacterial and viral outbreaks are recurrent events across
the entire timeline of human history.43 And, as Ebola researcher Kenneth Cameron told the Washington Post in
2015: ‘It’s only a question of how destructive the next one
will be’.44 A prompt response to an emerging outbreak is
necessary if its potential for diffusion is to be contained.
The intent here has been to consider the means by which
disease mapping may contribute to that goal.
Acknowledgements
The author wishes to thank peer reviewers and journal editors for
their comments on this manuscript.
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