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Epidemiology & Infection

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ARTICLE CONTENTS

* Abstract
* Introduction
* Methods
* Results
* Discussion
* Conclusion
* Recommendations
* Data availability statement
* Footnotes
* References

IMPLEMENTING EPIDEMIC INTELLIGENCE IN THE WHO AFRICAN REGION FOR EARLY DETECTION
AND RESPONSE TO ACUTE PUBLIC HEALTH EVENTS

Published online by Cambridge University Press: 14 May 2021

George Sie Williams [Opens in a new window] ,
Benido Impouma [Opens in a new window] ,
Franck Mboussou ,
Theresa Min-Hyung Lee [Opens in a new window] ,
Opeayo Ogundiran ,
Charles Okot ,
Tatiana Metcalf [Opens in a new window] ,
Mary Stephen ,
Senait Tekeste Fekadu and
Caitlin M. Wolfe
...Show all authors
Show author details

--------------------------------------------------------------------------------

George Sie Williams* Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Benido Impouma Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Institute of Global Health, University of Geneva, Geneva, Switzerland
Franck Mboussou Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Theresa Min-Hyung Lee Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Opeayo Ogundiran Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Charles Okot Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Tatiana Metcalf Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Mary Stephen Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Senait Tekeste Fekadu Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Caitlin M. Wolfe Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo College
of Public Health, University of South Florida, Tampa, Florida, USA
Bridget Farham Affiliation:
World Health Organization Regional Office for Africa, Brazzaville, Congo
Cristina Hofer Affiliation:
Infectious Diseases Department, Universidad Federal do Rio de Janeiro Medical
School, Rio de Janeiro, Brazil
Bertil Wicht Affiliation:
University of Lausanne, Lausanne, Switzerland Graph Network, Geneva, Switzerland
Claudia Codeço Tores Affiliation:
Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
Antoine Flahault Affiliation:
Institute of Global Health, University of Geneva, Geneva, Switzerland
Olivia Keiser Affiliation:
Institute of Global Health, University of Geneva, Geneva, Switzerland
*
Author for correspondence: George Sie Williams, E-mail: gwilliams@who.int

--------------------------------------------------------------------------------

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Article contents
* Abstract
* Introduction
* Methods
* Results
* Discussion
* Conclusion
* Recommendations
* Data availability statement
* Footnotes
* References

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ABSTRACT

Epidemic intelligence activities are undertaken by the WHO Regional Office for
Africa to support member states in early detection and response to outbreaks to
prevent the international spread of diseases. We reviewed epidemic intelligence
activities conducted by the organisation from 2017 to 2020, processes used, key
results and how lessons learned can be used to strengthen preparedness, early
detection and rapid response to outbreaks that may constitute a public health
event of international concern. A total of 415 outbreaks were detected and
notified to WHO, using both indicator-based and event-based surveillance. Media
monitoring contributed to the initial detection of a quarter of all events
reported. The most frequent outbreaks detected were vaccine-preventable
diseases, followed by food-and-water-borne diseases, vector-borne diseases and
viral haemorrhagic fevers. Rapid risk assessments generated evidence and
provided the basis for WHO to trigger operational processes to provide rapid
support to member states to respond to outbreaks with a potential for
international spread. This is crucial in assisting member states in their
obligations under the International Health Regulations (IHR) (2005). Member
states in the region require scaled-up support, particularly in preventing
recurrent outbreaks of infectious diseases and enhancing their event-based
surveillance capacities with automated tools and processes.

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KEYWORDS

AFROepidemic intelligenceevent-based surveillanceindicator-based
surveillanceoutbreakrapid risk assessments

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Type Original Paper
Information
Epidemiology & Infection , Volume 149 , 2021 , e261
DOI: https://doi.org/10.1017/S095026882100114X [Opens in a new window]
Creative Commons
This is an Open Access article, distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivatives licence
(http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits
non-commercial re-use, distribution, and reproduction in any medium, provided
the original work is unaltered and is properly cited. The written permission of
Cambridge University Press must be obtained for commercial re-use or in order to
create a derivative work.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

INTRODUCTION

The increasing burden of public health crises resulting from epidemics and
humanitarian emergencies continue to pose challenges, particularly in countries
with weak health systems [Reference Warsame, Blanchet and Checchi1, 2]. Of
particular concern are infectious disease outbreaks, which continue to threaten
regional and international public health security [Reference Lee3, Reference
McCloskey4]. These outbreaks are driven by mass population movements, extensive
transport networks, climate change and other ecological and environmental
factors that facilitate the emergence and global spread of infectious pathogens
[Reference Morganstein, Ursano, Fullerton, Weisaeth and Raphael5, Reference
McMichael6]. In the African region, where more than 100 infectious disease
outbreaks are detected and responded to annually [7], the risk is even higher
due to the limited capacities of most countries to detect and respond to these
events [Reference Talisuna8]. Limited efficacy and availability of proven public
health preventive interventions as a result of the challenges faced by disease
control programmes particularly in sub-Saharan Africa add to this risk
[Reference Songane9]. Outbreaks of severe acute respiratory syndrome (SARS),
Ebola virus disease (EVD) in West Africa and the Democratic Republic of Congo
and the ongoing coronavirus disease 2019 (COVID-19) pandemic have shown that
these events not only cost lives but also disrupt trade and economic activities,
costing millions or billions of dollars [Reference Joo10, Reference
Elmahdawy11]. This underscores the urgent need for tools and processes that are
used to facilitate early detection and response to these public health events to
mitigate their impact.

In addition to indicator-based surveillance, the International Health
Regulations (IHR) 2005, emphasise the need for event-based surveillance to be
used as part of holistic epidemic intelligence activities [Reference Merianos
and Peiris12, Reference Fischer, Kornblet and Katz13]. Epidemic intelligence
refers to the collection, processing and analysis of information on potential
health-related hazards with the goal of early detection of outbreaks and
health-related emergencies [Reference Paquet14]. The two main components of
epidemic intelligence are indicator-based surveillance, which relies on
information from structured or official sources such as health facility reports
or other pre-identified reporting sites, and event-based surveillance, which
includes information obtained from unstructured sources such as the media [15,
16]. The latter is responsible for most initial reports on epidemics [Reference
Heymann and Rodier17].

In furtherance of compliance with IHR 2005, several guidance documents,
frameworks and tools have informed the implementation and scale-up of epidemic
intelligence activities in the African region. These include the WHO Emergency
Response Framework (ERF) [18], Integrated Disease Surveillance and Response
(IDSR) strategy [19], the Global Health Security Agenda (GHSA) [Reference
Wolicki20] and the Early Warning and Response Network (EWARN) [Reference
Cordes21]. Additionally, the internet or social media has rapidly enhanced
epidemic intelligence monitoring of public health events, providing an
exceptional medium for early detection of outbreaks [Reference Yan, Chughtai and
Macintyre22]. Three internet-based platforms that have been used for epidemic
intelligence in the African region include Early Alerting and Reporting (EAR),
the Hazard Detection and Risk Assessment System (HDRAS) and more recently the
Epidemic Intelligence from Open Sources (EIOS). The EAR detects and monitors
information about chemical, biological, radiological and nuclear threats (CBRN)
as well as pandemic influenza [Reference Barboza23], and the HDRAS is a platform
for detection and monitoring of acute public health events [Reference
Bernard24]. EAR has been found to be a feasible model for detecting intentional
release of biological agents while HYDRAS has been deployed to detect outbreak
in mass gathering events [Reference Riccardo25][, Reference Enderlein and
Regmi26]. The EIOS encapsulates the experiences of EAR and HDRAS and serves as a
medium to ‘identify, verify, assess, analyze, interpret and communicate relevant
information on public health events for appropriate, timely and effective
action’ [27].

The aim of this study is to review the epidemic intelligence processes adopted
by WHO Regional Office for Africa (AFRO) to support the 47 Member States in the
region, its ability to detect, report and provide evidence-based assessments for
early outbreak response, as well as lessons learned. We specifically analysed
the ability of the epidemic intelligence activities to detect true outbreaks,
the frequency of outbreak detection and the results of evidence generated from
outbreak risk assessments. We emphasise the need for timely and reliable event
detection activities and describe how lessons learned can inform and guide
future use and scale-up of support to member states in the African region to
strengthen preparedness for, early detection of and rapid response to outbreaks
that may constitute a public health event of international concern (PHEIC).

METHODS

We performed a retrospective analysis of epidemic intelligence activities
undertaken at AFRO over a four-year period – 2017−2020 – focusing on the
processes, key outputs and contribution to the early detection and response to
infectious disease outbreaks across the WHO African region. The WHO African
region is composed of 47 member states, with all but Algeria located in
sub-Saharan Africa [28].

EPIDEMIC INTELLIGENCE AT AFRO

Epidemic intelligence activities were implemented in the context of the IDSR
strategy widely adopted in the African region for early detection of outbreaks
[Reference Kasolo29], consisting of indicator-based surveillance (IBS),
event-based surveillance (EBS) and rapid risk assessments (RRA). AFRO relied on
formal notification of outbreaks by member states using indicator-based
surveillance while the organisation implemented event-based surveillance through
monitoring of the media from internet-based sources and rumours (Fig. 1). A
standard operating procedure (SOP) on detection, verification and risk
assessment of acute public health events in the African region was adopted to
guide a dedicated team of epidemiologists in the daily implementation of
epidemic intelligence activities [30].

Fig. 1. Steps in the epidemic intelligence process at the WHO Regional Office
for Africa adapted from WHO AFRO manual on detection, verification, and risk
assessment of acute public health event in the WHO African region.

The IDSR strategy identifies a list of priority diseases with defined thresholds
for determining alert and epidemic levels to trigger public health response. The
implementation of indicator-based surveillance relied on analysis and
interpretation of routine surveillance data obtained from formal reporting sites
(mostly health facilities) in the respective member states. These data were
shared weekly with AFRO for collation, analysis and monitoring of epidemic
thresholds of priority diseases across the region. The outbreaks detected
through indicator-based surveillance were officially notified to WHO by member
states using the IHR (2005) Annex 2 decision instrument [31].

Event-based surveillance was performed mainly through media monitoring using
internet-based epidemic intelligence platforms. The two platforms used during
the studied period were HDRAS and EIOS, with the latter succeeding the former
since June 2018. These platforms pooled epidemic intelligence information from
several expert systems, including Medisys [32], Global Public Health
Intelligence Network (GPHIN) [33], HealthMap [34] and ProMED [35], among others.
They used automated methods for scraping, filtering and processing news from
media institutions and other relevant online sources, which were categorised
based on key terms such as outbreaks, epidemics, disease name, cluster of
symptoms or deaths, disaster, etc. On the platforms, customised boards with
sources from 42 languages filtered articles only of concern to member states in
the African region.

A designated member of the epidemic intelligence team triaged media articles
populating these platforms daily to detect any signal concerning an acute public
health event. A signal was considered as any information or rumour relating to
an event not yet officially reported to WHO that had caused or posed a potential
risk to human health. The triage process involved filtering, screening and
selecting signals for monitoring or further verification. During filtering, the
designated epidemiologist identified and screened out any duplicate information
analysed that was not automatically de-duplicated by the epidemic intelligence
platforms, and discarded any information not relevant for the purpose of early
warning. The potential signals were further screened by gathering additional
information from relevant sources to ensure that they differed from known events
in terms of disease manifestation, location, number of cases, suspected causal
agent and the circumstances under which they occurred. The final step of triage
involved selection of signals from a screened list of potential signals
presented by the designated epidemiologist. This was determined through
discussion with a team of epidemiologists who used baseline epidemiological
data, reliability of the source, indications of severity, risk of international
spread or the potential to cause large-scale outbreak as the basis for
selection. The selected signals were then shared with the respective member
states via WHO Country Offices for verification or continued to be monitored for
any change in the situation. A confirmed signal was a signal found to be true
after investigation or verification by the member state and WHO, while a signal
was considered discarded if not verified during the investigation. Confirmed
signals that met the requirements of the IHR 2005 Annex 2A decision-making
instrument for notification to WHO were deemed as new public health events.
Other signals concerning the spread of an already notified outbreak to new
geographic areas were monitored, used to gauge the need for a rapid risk
assessment and inform public health response actions.

RISK ASSESSMENT OF ACUTE PUBLIC HEALTH EVENTS

The WHO guide on rapid assessment of acute public health events [36] was used by
AFRO to conduct rapid risk assessments (RRA). These were conducted for
‘large-scale events potentially exceeding the response capacity of the affected
country, for events with international implications, and those requiring a WHO
response’ [18]. They assessed the likelihood and consequences associated with
three main risk determinants: the potential risk to human health, its potential
to spread, and the risk of insufficient control capacities. RRA were
systematically undertaken internally by WHO staff to reflect its independent
assessment of an event and evidence for decision making. The overall public
health risk was categorised as either ‘very high’, ‘high’, ‘moderate’ or ‘low’
at the global, regional and national levels based on the findings from the RRA.
An outbreak was assessed as low risk if it required routine response through the
disease control programme while a moderate risk was assigned if the outbreak
required some roles and responsibilities in addition to routine response. A
high-risk outbreak was an outbreak needing a range of additional control
measures including the need to establish command and control structures. A very
high risk was the highest level of risk assigned and denoted that the
implementation of control measures with serious consequences was highly likely.

DATA SOURCES AND COLLECTION

Data on signals triaged, events detected and RRA performed during the studied
period were recorded in the public health event (PHE) database maintained at
AFRO as well as the WHO Events Management System (EMS) [37]. The PHE database
and the EMS were the primary sources of data for the study.

DATA MEASUREMENT AND ANALYSIS

We selected only infectious disease outbreaks reported during the studied period
for our analysis and categorised them based on their modes of transmission, a
similar type of pathogen or pathogenesis, or requiring a similar type of public
health intervention (e.g. vaccination). We performed a descriptive analysis of
the infectious disease outbreaks detected through the various modes of epidemic
intelligence activities. We analysed the frequency of these outbreaks and their
geographic distribution across the African region.

We also analysed the proportion of outbreaks detected and positive predictive
values (PPV) of the media monitoring implemented through the internet-based
epidemic intelligence platforms for the top 16 infectious disease outbreaks
reported. An outbreak was considered as detected if a signal concerning the
outbreak was found via the epidemic intelligence platforms before they were
officially reported to WHO. This metric measured the ability of the epidemic
intelligence platforms to detect an outbreak and was calculated as the total
number of outbreaks detected divided by the total number of outbreaks for a
disease. The results were expressed as a percentage. The PPV measured whether a
signal selected through the triage process could or could not be confirmed and
was calculated as the number of confirmed signals divided by the total signals
selected, expressed as a percentage. The frequency and risk level categorisation
were analysed for the top 11 frequent infectious disease outbreaks with
finalised RRA.

R version 4.0.3 [38] was used for statistical analysis and ESRI 2017 ArcGIS Pro
2.1.0 [39] for mapping of the data.

RESULTS

A total of 415 substantiated infectious disease outbreaks were detected through
epidemic intelligence activities and reported to WHO from 2017 to 2019, with 25%
(n = 104) initially detected through event-based surveillance and 75% (n = 311)
through indicator-based surveillance (Table 1). During the studied period, about
100 infectious disease outbreaks were reported each year, although the number of
new outbreaks decreased slightly over time. The most frequent outbreak
categories were vaccine-preventable diseases (n = 108), followed by
food-and-water-borne (n = 88) and vector-borne diseases (n = 77) (Table 1). The
most frequent diseases causing outbreaks were cholera (n = 62), COVID-19
(n = 47), measles (n = 37), dengue fever (n = 29) and Crimean-Congo haemorrhagic
fever (CCHF) (n = 26). Cholera (40.3%; n = 25) had the most outbreaks initially
detected by event-based surveillance, followed by measles (27.0%; n = 10),
COVID-19 (17.0%; n = 8), dengue fever (27.6%; n = 8) and CCHF (19.2%; n = 5).
The types of surveillance used for the initial detection of each outbreak
reported are seen in Table 1.

Table 1. Infectious disease outbreaks and conditions detected by epidemic
intelligence activities (event-based and indicator-based surveillance) and
reported to WHO in the African region, 2017–2020

Figure 2 shows a spot map of the geographic distribution of infectious disease
outbreaks reported to WHO during the studied period, with at least one outbreak
reported from each member state in the African region. Cholera and measles were
the most frequent infectious disease outbreaks reported from 2017 to 2019.
Additionally, circulating vaccine-derived poliovirus type 2 (cVDPV2) outbreaks
emerged among the three most frequent across the region in 2019, while COVID-19
was the most frequent in 2020, with SARS-CoV-2 detected in all the member states
of the WHO African region (Fig. 2).

Fig. 2. A spot map of the geographical distribution of infectious disease
outbreaks reported to WHO in the African region, 2017–2020.

Over 2.8 million unverified media articles were published on the epidemic
intelligence platforms from 2017 to 2020 (Fig. 3). Of these, 20 405 media
signals were screened with 924 selected and sent to member states for
verification. Of the selected media signals, 780 (84.4%) were either confirmed
or remained under monitoring while the rest were either discarded or
unverifiable. From the confirmed media signals, 104 new public health events
were detected (Fig. 3).

Fig. 3. Results of event-based surveillance (media monitoring) undertaken at WHO
Regional Office for Africa leading to detection of new infectious disease public
health events in the African region, 2017–2020.

Of the 358 outbreaks caused by the 16 most frequent diseases reported to WHO
from 2017 to 2020, 24% (n = 86) were initially detected via the epidemic
intelligence platforms before official notification to WHO (Table 2). Detection
ranged from 4.3% for yellow fever to 53.8% for anthrax and malaria. The most
frequent disease outbreak detected was cholera (n = 25) followed by measles
(n = 8). Table 2 further showed that of 1018 media signals selected for the top
16 diseases reported, 33% were confirmed with positive predictive values ranging
from 8.5% for COVID-19 to 57.6% for measles. EVD (n = 100) had the highest
number of signals confirmed (Table 2).

Table 2. Percent of events detected and positive predictive values of the most
frequent events identified through epidemic intelligence in the WHO African
region, 2017–2020

a Detected events are events detected via epidemic intelligence platforms before
they were officially reported to WHO.

b Per cent of events detected via the epidemic intelligence platforms before
being officially reported to WHO.

c PPV, positive predictive value, is defined as the proportion of selected media
signals that were confirmed or monitored after verification.

A total of 178 RRA were conducted for infectious disease outbreaks during the
studied period with cholera (24.2%; n = 43) being the most frequent, followed by
EVD (13.5%; n = 24), yellow fever (10.1%; n = 18), dengue fever (6.1%; n = 11)
and Lassa fever (5.6%; n = 10) (Fig. 4). Further, RRA found most outbreaks to
have high risk (47.7%; n = 83) at national level, moderate risk (42.5%; n = 74)
at regional level and low risk (92.9%; n = 169) at global level (Fig. 4). Only
outbreaks of cholera and EVD reached very high risk at both national and
regional levels. All outbreaks were assessed as low risk at the global level
except for COVID-19, which was considered very high due to the ongoing pandemic.

Fig. 4. National, regional, and global levels risk characterization for the top
eleven infectious disease outbreaks with frequent rapid risk assessments, WHO
African region, 2017–2020 (N = 178).

DISCUSSION

Epidemic intelligence activities have detected and verified a large number of
infectious disease outbreaks in the WHO African region, highlighting the
frequency of outbreaks of cholera and measles over the whole study period. In
2019 outbreaks of cVDPV2 increased in frequency, while in 2020 the global
pandemic of COVID-19 was reflected in the frequency of its detection and wide
geographical spread across the region. Our results have shown that epidemic
intelligence activities have contributed to the detection and reporting of
infectious diseases across the region.

Although IDSR specifies the use of both indicator and event-based surveillance,
we found that the member states mainly relied on formal reporting sites for the
detection of outbreaks, thus the high number initially detected through
indicator-based surveillance. According to Fall et al., implementation of
event-based surveillance was inadequate despite the wide adoption and scale-up
of IDSR across the African region [Reference Fall40]. The initial detection of a
quarter of all outbreaks through event-based surveillance from media sources
showed that this mode of epidemic intelligence is a useful complement in the
early detection of outbreaks. This point has been reinforced in the third
edition of the IDSR technical guidelines, recently adopted by the WHO African
region [41].

Although vaccines are known to be one of the most efficient, high-impact public
health measures to prevent diseases, immunisation rates across most of the WHO
African region have been sub-optimal, mainly challenged by issues related to
funding, stock-outs, logistics and accessibility, among others [Reference
Mihigo42]. Our finding of high frequency and recurrence of vaccine-preventable
disease outbreaks in the region during the studied period could be attributed to
these challenges. Food-and-water-borne disease outbreaks have also remained
prevalent in the region, with cholera being the most frequently detected and
reported. Poor water, sanitation and hygiene (WASH) infrastructures are the most
cited key factors driving the recurrence of food-and-water-borne disease
outbreaks, however, the role of environmental reservoirs as well as
anthropogenic and hydroclimatic drivers have also been highlighted [Reference
Gwenzi and Sanganyado43].

Additionally, the changing environment, particularly changes in the
socio-ecological systems and climate, have undoubtedly influenced the recurrence
of outbreaks of some emerging and reemerging diseases, which we categorised as
vector-borne and viral haemorrhagic fevers [Reference Campeau44]. There are
recurrent outbreaks of these diseases, such as dengue fever, Rift Valley fever,
CCHF, Lassa fever and EVD. Also, the emergence of several outbreaks of cVDPV2 in
2019 and the spread of SARS-CoV-2 globally in 2020 in all members states of the
WHO African region highlights the effects of novel diseases and conditions on
human health and development in the region.

With the increasing threat of emerging and reemerging infectious diseases, the
role of epidemic intelligence remains crucial for preparedness and response to
these events. Integrated surveillance systems such as IDSR, when implemented
holistically, are central to early detection of outbreaks and, most importantly,
can help mitigate the devastating social and economic consequences of epidemics.
The ability of media monitoring to detect several outbreaks not only confirm the
invaluable contribution of internet-based sources to early outbreak detection,
but also emphasise how adopting standardise and thorough processes for triaging
rumours can reduce noise and improve the PPV of media monitoring.

Moreover, the epidemic intelligence activities undertaken at AFRO may have
contributed to the early detection of outbreaks and indirectly influenced the
timeliness of WHO response actions to support member states in mounting public
health response to outbreaks with potential for international spread. Impouma et
al. found that the timeliness of detection and control of outbreaks across the
African region improved from 2017 to 2019, attributed to enhanced epidemic
intelligence, among other factors [Reference Impouma45]. For example, in 2017
WHO received initial information on a cluster of cases presenting with EVD-like
symptoms in the Democratic Republic of the Congo three days before the event was
confirmed and officially notified by the country. A verification request from
WHO to the member state triggered field level investigation and within 24 h of
confirmation and official notification, an RRA was finalised, which provided the
basis for further operational actions. The rapid response mounted by the member
state with support from WHO and partners cannot be discounted among factors that
led to an eventual control of the outbreak within two months.

The WHO emergency response framework requires evidence for decision making when
supporting member states’ response to epidemics [18]. The RRA provided the
evidence base with which to inform the scale of WHO support in order to
implement appropriate and timely public health response measures. The high
frequency of RRA for some outbreaks such as cholera, EVD, dengue fever and Lassa
fever highlighted the potential of these outbreaks to cause significant impact
to human health. Most outbreaks were assessed as high risk at national level,
indicating that these outbreaks could quickly overwhelm national capacities and
spread to new sub-national areas, with high morbidity and mortality. While most
outbreaks had moderate risk at the regional level, outbreaks of cholera and EVD
were found to have high and very high risk at regional levels indicating their
potential to cross national boundaries and affect other member states in the
region, resulting in strain on their health systems. COVID-19 was assessed as
very high risk at the global level in light of the ongoing pandemic and its
impact across the globe.

Implementing epidemic intelligence activities in the African region has not been
without challenges. First, event-based surveillance has not been fully scaled-up
across the African region, partly due to the additional logistical, human and
financial resources that are required for its implementation. While media
monitoring undertaken at AFRO have been useful, in-country strengthening of
event-based surveillance activities, for example, community-based surveillance
and media monitoring, would have further enhanced the timeliness of outbreak
detection and response at sub-national levels. Secondly, the internet-based
epidemic intelligence platforms generated a high volume of noise, resulting in
hundreds or thousands of articles, making triaging laborious. Additionally,
while articles from formal media sources have been invaluable, the increasing
popularity and use of social media platforms such as Facebook and Twitter in the
region could have contributed much more to the pool of epidemic intelligence
information if they were included as sources of information on the epidemic
intelligence platforms. Thirdly, for some events such as EVD and COVID-19, the
heightened awareness, concerns and increased rumours associated with outbreaks
meant that many more signals needed to be investigated to rule out new flare-ups
or events in order to ensure that the population was protected from any eventual
spread of these diseases. The importance of increasing sensitivity to capture
all possible signals resulted in lower positive predictive values and increased
human and financial resources needed to investigate additional signals. There
was a higher importance to acquire sufficient sensitivity at the cost of lower
PPV.

There are four main limitations of our analysis. First, our list of outbreaks
may not be exhaustive since we considered only those required for notification
to WHO as per IHR 2005 Annex 2A decision instrument. Second, the detection
metrics is not an ideal measurement for sensitivity. The relatively low
detection measured for most of the top 16 infectious disease outbreaks were due
to the exclusion of signals appearing on the epidemic intelligence platforms
after the outbreaks were officially reported to WHO through other channels. This
means that the detection metrics cannot be used as a proxy for sensitivity of
the epidemic intelligence platforms, that is, the ability of the platforms to
detect outbreaks irrespective of whether first reported to WHO or not. Third,
several signals remained unverified as of the time of our analysis and therefore
could not be classified as confirmed or discarded. This may have led to
underestimation of the positive predictive values reported. Finally, we did not
systematically capture data on the number of duplicate articles filtered during
the triaging process, hence, we were unable to exclude duplicates from the total
number of unique media articles populating the platforms. While the epidemic
intelligence platforms automated the de-duplication of some of the articles that
were nearly identical in text, there were articles which provided duplicative
information which were not filtered out.

CONCLUSION

Despite these challenges and limitations, epidemic intelligence activities have
proven useful to the early detection and response to outbreaks in the African
region. The adoption of standardised processes and tools for epidemic
intelligence, including rapid risk assessments, not only enhanced outbreak
detection in the region but also provided an objective means of monitoring the
evolution of outbreaks and generating evidence to guide WHO in the deployment of
its resources and technical expertise. The implementation of media monitoring
using internet-based platforms in addition to routine indicator-based
surveillance, shows that in an increasingly digital world, there is growing
reliance on unofficial sources for first reports about outbreaks.

RECOMMENDATIONS

We recommend that AFRO continues to invest in strengthening epidemic
intelligence as part of preparedness for and response to events of public health
concern through building countries’ capacities to implement event-based
surveillance including the scale-up of the use of platforms such as EIOS in the
African region. Expanding sources on the current epidemic intelligence platforms
to include social media would further increase the chances of detecting events
early and should be prioritised. There is also a need to strengthen routine
disease prevention programmes by addressing factors that limit their
effectiveness to prevent recurrent outbreaks.

ACKNOWLEDGEMENTS

The authors would like to thank all colleagues, particularly WHO staff, partners
and Ministries of Health, whose tireless efforts have shaped the epidemic
intelligence landscape across the African region and save many more lives from
the scourge of epidemics.

DATA AVAILABILITY STATEMENT

The authors confirm that the datasets used for this study are available on
request.

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FOOTNOTES

These authors contributed equally to the work.

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Table 1. Infectious disease outbreaks and conditions detected by epidemic
intelligence activities (event-based and indicator-based surveillance) and
reported to WHO in the African region, 2017–2020

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Fig. 2. A spot map of the geographical distribution of infectious disease
outbreaks reported to WHO in the African region, 2017–2020.

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Table 2. Percent of events detected and positive predictive values of the most
frequent events identified through epidemic intelligence in the WHO African
region, 2017–2020

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Fig. 4. National, regional, and global levels risk characterization for the top
eleven infectious disease outbreaks with frequent rapid risk assessments, WHO
African region, 2017–2020 (N = 178).

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