Access to this full-text is provided by Springer Nature.
Content available from One Health Outlook
This content is subject to copyright. Terms and conditions apply.
Mremietal. One Health Outlook (2021) 3:22
https://doi.org/10.1186/s42522-021-00052-9
REVIEW
Twenty years ofintegrated disease
surveillance andresponse inSub-Saharan Africa:
challenges andopportunities foreective
management ofinfectious disease epidemics
Irene R. Mremi1,2,3* , Janeth George1,2 , Susan F. Rumisha3,4 , Calvin Sindato2,5 , Sharadhuli I. Kimera1 and
Leonard E. G. Mboera2
Abstract
Introduction: This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and
Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in
information technology, big data analytics techniques and wealth of data sources.
Methods: HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review
followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols.
Results: A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion
criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3–4 countries. Twenty-six
studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the
studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS),
which collects data from health care facilities, has remained the primary source of IDSR data. However, the system
is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integra-
tion of data from other sources. Under-use of advanced and big data analytical technologies in performing disease
surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based
decision-making.
Conclusions: This review indicates that most countries in SSA rely mainly on traditional indicator-based disease
surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA
countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources
of health information to provide support to effective detection and prompt response to public health threats.
Keywords: Disease surveillance, Data source, Performance, Big data, One Health, Sub-Saharan Africa
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
Open Access
One Health Outlook
*Correspondence: irene.mremi@sacids.org
1 Department of Veterinary Medicine and Public Health, Sokoine
University of Agriculture, Morogoro, Tanzania
Full list of author information is available at the end of the article
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 2 of 15
Mremietal. One Health Outlook (2021) 3:22
Introduction
Despite scientific development to strengthen the health
system to protect and promote human health, Sub-Saha-
ran Africa (SSA) continues to be confronted by long-
standing, emerging, and remerging infectious disease
threats [1, 2]. e region vulnerability to infectious dis-
ease epidemics is driven by favourable climatic and eco-
logical conditions for harbouring pathogens and their
vectors in an environment with high human and animal
interactions [3, 4]. Migration of wild animals and birds,
frequent uncontrolled movements of people, commodi-
ties, animals and animal products across the national
and international borders pose additional threats to
the spread of infectious diseases [5]. Unfortunately, the
region has a relatively low capacity for risk management
of disease epidemics, mainly due to inadequate resources
for early detection, identification, and prompt response
[6, 7]. e failure in the early detection and response to
epidemics in SSA is attributed to several factors, includ-
ing deficiency in the development and implementation of
surveillance and response systems against infectious dis-
ease outbreaks [8].
Before 1998, most countries in Africa implemented
surveillance systems through vertical programmes of
specific diseases of national and /or international prior-
ity. ese included malaria, HIV/AIDS, tuberculosis and
vaccine-preventable diseases. Epidemiological data were
collected mainly at the health care facility level and in
outreach health service settings [9, 10]. is situation
led to fragmented and inefficient disease monitoring sys-
tems in many aspects, including resource allocation, flow
and use of information and country capacity to detect
and respond [9]. In response to an increased frequency
of emerging and re-emerging diseases causing high mor-
bidity and mortality in Africa, in 1998, the World Health
Organisation (WHO) Regional Committee for Africa
adopted a strategy called Integrated Disease Surveillance
[9, 11]. e intent was to create and implement a com-
prehensive, integrated, action-oriented, district-focused
public health surveillance for African countries [9]. In
2001 the strategy was renamed Integrated Disease Sur-
veillance and Response (IDSR) to emphasise the critical
linkage between surveillance and public health action
and response [12].
IDSR functions are categorised into core and support
functions. e core functions include identification of
cases, investigation and confirmation, registration, case
notification/reporting, data analysis and interpretation,
response to the situation, communication and provision
of two-way feedback, evaluation of the interventions,
and preparation for emergency occurrences. e sup-
port functions include guidelines, laboratory capacity,
supervision, training, resources and coordination at all
health system levels [13]. e IDSR organisation struc-
ture allows surveillance information to flow from the low
levels (community and facility) where data is generated
through the district and national levels up to the World
Health Organization. e IDSR implementation lever-
ages the purpose and scope of the International Health
Regulations 2005 [11].
During the past 20years, the IDSR framework has been
used in 94% (44/47) of the countries in the WHO Afri-
can region to enhance capacity for surveillance for prior-
ity diseases, conditions, and events [14–16]. In most of
these countries, the strategy has been implemented for
about two decades, and the priority disease list required
for reporting has been revised and increased [17]. Hav-
ing a large number of diseases monitored by the public
surveillance system creates implementation challenges.
Low laboratory diagnostic capacity, low utilisation of the
primary healthcare system and limited analytical skills
and capacities in managing large and complex data result
in unconfirmed and incomplete data and minimal utili-
sation of the data generated by the conventional system.
Besides, the African continent has recently experienced
major epidemics, including Ebola virus disease, dengue
fever, cholera, yellow fever and coronavirus disease 2019,
which spread faster and further due to high global con-
nectivity, inadequate detection and risk management,
and might easily be missed by the routine monitoring
systems.
Over the years, the IDSR has relied heavily on the
routine health management information system
(HMIS) implemented at the facility and district lev-
els of the health systems [16]. However, technology
advancement and new communication platforms such
as social and news media are growing in Africa, bring-
ing more opportunities to incorporate digital data into
surveillance information to complement passive facil-
ity-based surveillance. Since its adoption IDSR effec-
tiveness and performance in SSA have been assessed,
focusing on its functions. However, assessments on
how the challenges and opportunities coming with
IDSR evolved, how the technology expansion and the
availability of other data sources relevant for surveil-
lance have been embraced in monitoring, detecting
and managing epidemics have not been documented
with certainty [11, 14]. This systematic review aimed
to analyse the performance of the IDSR strategy in
Sub-Saharan Africa and how its implementation
has embraced advancement in information technol-
ogy, big data analytics techniques and wealth of data
sources, as well as the One Health approach. The
gaps, challenges and opportunities identified are used
to propose appropriate strategies to improve surveil-
lance in the region.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 3 of 15
Mremietal. One Health Outlook (2021) 3:22
Methods
Search strategy andselection criteria
is review was guided by the following overarching
question: Does IDSR generate information that drives
early detection of and response to infectious disease out-
breaks? Specific questions were: (i) Has IDSR improved
health data quality and utilisation during its 20years of
implementation in SSA?; (ii) What are the challenges
and opportunities for IDSR to improve early detection
and prompt response to infectious diseases in SSA?
e review followed the Preferred Reporting Items for
Systematic Reviews and Meta-Analysis Protocols 2015
checklist [18]. ree databases, namely HINARI, Pub-
Med, and advanced Google Scholar, were searched using
Boolean operators. e search terms were Integrated
Disease Surveillance and Response, Integrated Disease
Surveillance, Health Management Information Systems,
District Health Information System and Sub Saharan
Africa or individual member country. e search was
limited to studies published in the English language
between January 1998 and December 2020. An addi-
tional search was conducted using the Google search
engine on the World Wide Web and hand-searching
from the reference list of the screened articles. Other
sources were the World Health Organization (WHO),
the United States Centres for Disease Control and Pre-
vention, Africa Centre for Disease Control and Preven-
tion and ministries websites of individual Sub-Saharan
African countries.
e review involved two-stage screening, title/
abstract screening and full-paper screening. e inclu-
sion criteria were: the study must involve at least one of
the SSA countries, clearly describe the evaluation of the
IDSR system, focuses on at least one of the IDSR func-
tions and/or systems attributes. e review excluded
studies with abstracts without full text, not in English,
reviews and newsletters. Two of the authors (IRM and
LEGM) extracted eligible articles independently, and
any disagreements between them on inclusion or exclu-
sion were resolved by discussion and consensus. e
linked descriptive search requests that were developed
and search results from each database are presented in
Table1. Further exclusion of the article was performed
during the data collection process after its full-text
review. e extracted data related to the IDSR core
and support functions’ performance, challenges associ-
ated with its implementation and improvement oppor-
tunities were summarised using the thematic analysis
method.
Results
Literature selection
A total of 1,809 articles were initially identified using
the key search descriptors. A large number of articles
(1,311) were irrelevant or duplicate and were excluded.
e 498 remaining abstracts were screened further, and
412 were excluded based on the inclusion/exclusion
criteria. Of the remaining 86, full-text articles were
screened, and 45 studies met the inclusion criteria and
hence, were selected for detailed reviews (Fig.1). Of the
45 studies, 35 were country-specific, seven covered the
SSA region, and three covered 3–4 countries. Of the
47 countries in Sub-Saharan Africa, country-specific
studies were available for 20 (42.6%) countries. A total
of 26 studies assessed the IDSR core functions, while
43 the support functions and 24 focused on either core
or support functions. Twenty-four studies addressed
both the core and support functions. Most of the stud-
ies involved Tanzania (9), followed by Ghana (6) and
Uganda (5) (Table2).
Table 1 Search strategy and the number of articles included for screening
Database Search strategy Total results No. Article
included for
screening
No.
Article
exclude
PubMed (((Integrated disease surveillance and response) AND (Sub Saharan Africa)) OR
(IDSR[Title/Abstract])) OR (Health management information system [Title/Abstract])
AND ((ffrft [Filter]) AND (journalarticle [Filter]) AND (fft [Filter]) AND (english [Filter]) AND
(1998:2020[pdat]))
344 96 248
Hinari ((Integrated disease surveillance and response) OR (TitleCombined:(IDSR))) AND
((TitleCombined:(sub Saharan Africa)) OR (Health management information system)) 1,052 302 750
Google scholar ((("Integrated disease surveillance and response" OR "IDSR" OR "Integrated disease
surveillance") AND ("Health management information system" OR "district health infor-
mation system")) AND "Sub Saharan Africa")
369 82 287
Other source Integrated disease surveillance and response OR Integrated disease surveillance OR
IDSR AND Sub Saharan Africa
Health management information system OR district health information system
44 18 26
Total 1,809 498 1,311
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 4 of 15
Mremietal. One Health Outlook (2021) 3:22
Performance ofIDSR strategy
e adoption and implementation of the IDSR strategy
during the past 20years have shown some improvements
in several countries’ disease surveillance activities. ese
include the integration of the surveillance functions of
the categorical (or vertical) disease control programmes;
implementation of standard surveillance, laboratory and
response guidelines; improved timeliness and complete-
ness of surveillance data, as well as increased national-
level review and use of surveillance data for the response
[14, 15]. However, most efforts to improve IDSR in SSA
focused on the support functions rather than core func-
tions. e successes of the desire for integration of the
disease surveillance strategies in SSA have been docu-
mented in several countries, including Ghana, Ethiopia,
Botswana, Kenya, Liberia, Sierra Leone, Uganda [56].
ese include the efficient utilisation of the vertical pro-
gramme surveillance mechanisms that provided func-
tional infrastructure and trained personnel [56, 57].
IDSR core functions
Improvements in IDSR system attributes such as com-
pleteness and timeliness of data reporting have been
observed in Uganda, Malawi and Ghana [24, 35, 49,
51]. By the end of 2017, 68% of the countries in the
WHO Africa Region had achieved the timeliness and
completeness threshold of at least 80% of the report-
ing facilities. ere was an improvement in timeliness
of monthly and weekly reporting from 59 and 40% in
2012 to 93 and 68% in 2016, respectively [14]. During
the same period of time, completeness of monthly and
weekly reporting improved from 69 to 100% and 56 to
78%, respectively [14]. However, over the years, routine
HMIS has remained the primary data source for IDSR
in SSA. e routine HMIS in several SSA countries is
characterised by persistent incompleteness and other
data quality issues [58–60]. Studies in Ghana, Malawi,
Mozambique, Nigeria and Tanzania have reported
that case registration at a health care facility is also a
Fig. 1 PRISMA flow diagram for article selection
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 5 of 15
Mremietal. One Health Outlook (2021) 3:22
challenge. ere is a failure in comprehensively enter-
ing the appropriate patient information in the registers,
and in some cases, diagnoses are either not recorded
or wrongly recorded [60–64]. Moreover, high levels
of mismatch between the register records and report
forms and electronic District Health Information Sys-
tem-2 (DHIS-2) have been observed [60]. In Ethiopia
and Liberia, IDSR data generated through HMIS were
under-utilised due to poor data management and anal-
ysis skills [19, 22, 32]. A high level of mismatch between
the HMIS registers’ entries, tally sheets and the DHIS-2
database has also been reported in some countries in
Africa [60, 65]. us, despite some progress in recent
years, the core IDSR data source is still weak and inac-
curately reflects what is generated from the primary
healthcare facility levels [60].
Studies in Ghana, Tanzania, and Zambia have reported
that several health facilities lack copies of the IDSR
Technical Guidelines for Standard Case definitions;
and that laboratories are ill-equipped to provide con-
firmation of any suspected priority notifiable infectious
disease [10, 25, 60]. Lack of capacity for timely clinical
screening, referral, diagnosis, notification, treatment and
containment of suspected cases has been documented in
Africa [66, 67]. Coordination of case definition report-
ing protocols across programmes was identified as a
necessary step towards improving IDSR completeness
and timely reporting in Uganda [52]. Moreover, since
most primary level health care facilities lack diagnos-
tic capabilities, the generated data rely on a syndromic
approach, with low specificity [68]. Syndromic surveil-
lance remains more useful at the community level for
early detection and reporting of disease signals, which
should be immediately verified and responded to by the
primary health care facilities. Health care utilisation in
many low-income countries is limited, and that only a
proportion of people have access to conventional health-
care facilities. e utilisation frequency is higher among
urban than rural populations. Several SSA countries
have reported a frequency of between 40.0 and 87.3% of
their population seeking care from conventional health
care facilities [69–72].
IDSR support functions
In terms of IDSR support functions, of the 47 countries
in the WHO Africa Region, 94% were implementing the
IDSR strategy, and 45 (85%) have initiated training at the
sub-national level [14]. irty-three (70%) of the coun-
tries were using the electronic IDSR (eIDSR) system,
and over two thirds (68%) had a feedback mechanism for
sharing national surveillance data [14]. e introduction
of the eIDSR using short message service for reporting
Table 2 Articles on IDSR core and support functions in Sub-
Saharan Africa
Key: ✔=Article available; X= Article not available
Core functions included case detection, case conrmation; case registration;
case reporting; data management; data analysis, outbreak preparedness,
outbreak response, and feedback
Support functions included guidelines, laboratory capacity, supervision;
training; resources (nancial, human, material/equipment) and coordination
Study country/ region Core Support Reference
1. Africa X ✔[14]
2. Africa ✔ ✔ [11]
3. Africa ✔ ✔ [19]
4. Africa X ✔[20]
5. Africa X ✔[16]
6. Africa X ✔[21]
7. Africa ✔ ✔ [8]
8. Côte d’Ivoire, Guinea-Bissau, Senegal, Mali X ✔[22]
9. Democratic Republic of the Congo X ✔[23]
10. Ethiopia ✔ ✔[24]
11. Ethiopia ✔ ✔ [25]
12. Ghana ✔ ✔ [26]
13. Ghana ✔ ✔[27]
14. Ghana ✔ X [28]
15. Ghana X ✔[29]
16. Guinea X ✔[30]
17. Guinea ✔ ✔[31]
18. Kenya X ✔[32]
19. Kenya X ✔[33]
20. Liberia ✔ ✔[34]
21. Madagascar X ✔[35]
22. Malawi X ✔[36]
23. Nigeria ✔ ✔[37]
24. Nigeria X ✔[38]
25. Nigeria ✔ X [39]
26. Nigeria ✔ ✔[40]
27. Rwanda ✔ ✔ [41]
28. Sierra Leone X ✔ [42]
29. Sudan ✔ ✔[43]
30. Tanzania ✔ ✔ [44]
31. Tanzania ✔ ✔[45]
32. Tanzania ✔ ✔[46]
33. Tanzania ✔ ✔ [47]
34. Tanzania ✔ ✔ [48]
35. Tanzania X ✔[49]
36. Tanzania ✔ ✔[50]
37. Tanzania, Ghana X ✔[51]
38. Tanzania, Ghana, Uganda, Zimbabwe X ✔[15]
39. Uganda X ✔[52]
40. Uganda ✔ ✔ [53]
41. Uganda ✔ ✔[54]
42. Uganda ✔ ✔ [55]
43. Zambia X ✔[56]
44. Zambia ✔ ✔ [57]
45. Zambia ✔ ✔ [10]
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 6 of 15
Mremietal. One Health Outlook (2021) 3:22
weekly epidemiological data has proved to be a power-
ful tool that empowers health workers and addresses
many of the barriers associated with paper-based report-
ing [38, 47, 52]. At the same time, the development of
generic data analysis has guided enhanced data qual-
ity and management in Zimbabwe [73]. In terms of key
performance indicators, there was a substantial increase
in the number of countries that had adopted the IDSR
guidelines and conducted training of healthcare workers
at all levels [14].
Discussion
Challenges ofIDSR
is review indicates that in most countries, data gener-
ated through the routine HMIS, which is the key source
of IDSR, are rarely assessed for their quality, analysed
and used to support decision-making [74]. Several stud-
ies in SSA have revealed weaknesses in case identifica-
tion and recording at the primary healthcare facilities
associated with several factors including limited skills
among health workers due lack of training and refresher
courses, patient-load versus human resource availability,
low motivation and inadequate HMIS-related resources
[15, 24, 44, 45, 49, 60]. e quality of the data remains
a challenge, with incomplete and inconsistent data fre-
quently being reported at different levels of the surveil-
lance system. Moreover, HMIS data are considered to
mainly reflect the population seeking care from health
care facilities.
In Ethiopia, Liberia and Tanzania, assessments of the
HMIS have identified some data quality issues and lack
of use of the generated data [32, 43, 60, 75]. In a study
in Ethiopia, though the surveillance system was found to
be simple, useful, flexible, acceptable and representative,
it lacked regular data analysis and feedback [22]. More-
over, studies in Kenya and Nigeria have indicated gaps
between knowledge and practice of disease surveillance
among health care workers [76, 77]. Incomplete data fil-
ing and inadequate organisation have been reported as an
inbuilt shortcoming at all levels of IDSR in SSA [25, 26,
78]. Routine data analysis is still insufficient at facility and
district levels in most countries, mainly due to the lack
of clear guidelines for analysing data, shortage of skilled
personnel, poor understanding of the use of surveillance
data in planning, and inadequate infrastructure, includ-
ing warehouses, computers, databases, data mining sys-
tems and analytical software [43, 44, 46, 51, 60].
A few countries (Burkina Faso, Ghana, Liberia,
Uganda) have reported analysing and used routine HMIS
data at sub-national levels [32, 74]. In both Liberia [32]
and Tanzania [43], it was found that analysis and data use
have not been given adequate attention. In addition to
poor data management and analysis skills, some studies
have reported under-utilisation of IDSR data at all lev-
els due to poor data management and analysis skills [32,
42, 43, 60]. e culture of data analysis was lacking, and
the relevance of surveillance data for decision making at
sub-national levels was grossly underestimated. e use
of paper-based reporting was likely to lead to severe limi-
tations in the transmission of the data from the point of
generation to a higher level mainly because of the ineffi-
cient report review and approval processes, manual rout-
ing of reports and running out of recording and reporting
forms [25, 46]. Despite significant investment in early
outbreak detection in SSA, there is very little evidence
that even high HMIS data utilisation will influence early
detection [79].
For the integrated system to be efficient, it requires
strong coordination and communication, a clear organi-
sation structure, adequate resources [80, 81], and reliable
data sources. Integration may range from interconnec-
tivity, which requires a simple transfer of files with basic
applications, to complex convergent integration, which
involves merging technology with processes, knowl-
edge, and human performance. IDSR strategy strives
for the concurrent integration route, but most coun-
tries have not achieved total integration. Implementa-
tion of the strategy is partially done [14, 35], and there is
more focus on technical aspects than organisational and
human resource issues hence impair the performance of
the systems [49, 82]. Nevertheless, some countries such
as Uganda have rectified those systemic challenges and
reported improvement in the implementation [50].
Opportunities forimproving IDSR
Health information systems
In SSA, several government ministries, agencies, and aca-
demic and research institutions are involved in managing
different aspects of the health information systems. e
ministries of health run the routine HMIS as the major
source of information for decision making and planning.
National Statistical Offices are responsible for most of
the nation-wide household demographic and health sur-
veys as well as population census [74]. Other key health-
related information systems include civil registration,
demographic surveillance sites and research outputs [83].
Demographic surveillance sites function in several coun-
tries, but the data generated are not integrated into the
national health information system because of concerns
about representativeness [74]. Besides, health research
and academic institutions are increasingly generating
evidence on human and animal health that could be used
for disease surveillance purposes. However, most of the
findings are mainly used for estimating national disease
distribution rather than for planning national disease
control programmes [84].
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 7 of 15
Mremietal. One Health Outlook (2021) 3:22
A warning of an impending epidemic can help rel-
evant authorities and communities to prepare and take
immediate actions to reduce morbidities and mortali-
ties. Many of the epidemic diseases are highly sensitive
to long-term changes in climate and short-term fluctua-
tions in weather. Meteorological data are made available
daily by the National Meteorological Agencies, yet they
are rarely used to monitor the occurrence of diseases.
Meteorological data can be combined with geospatially
referenced data, population densities or road networks
to generate estimates of environmental indicators rel-
evant to infectious diseases [68]. However, such informa-
tion is not available for planning, disease surveillance and
outbreak management. It is recommended that the SSA
governments consider establishing national platforms for
infectious disease epidemics early warning systems and
develop action plans for their operationalization, includ-
ing resource mobilization and engagement with key
stakeholders.
It is critical for a good and efficient surveillance system
to incorporate other sources such as mortality data from
demographic surveys, environmental data, vital statistics
and civil registration, antimicrobial resistance, systematic
surveys, meteorological data and research data. In most
countries, despite an enormous amount of data gener-
ated by these systems, they run in parallel and indepen-
dently, not well-coordinated, and sharing of information
between them is minimal. Each of the existing systems
operates its data collection and utilization framework.
Moreover, much of the information is generated outside
the health sectors – making it not readily available for
disease surveillance purposes. It is a fact that the inno-
vations, including the use of big data and artificial intel-
ligence, could transform infectious disease surveillance
and response and complement the existing traditional
disease surveillance systems and improve detection and
response to epidemics [68].
Laboratories play an important role in the prompt diag-
nosis of infectious diseases. e findings of this review
have shown that IDSR is challenged by inadequate diag-
nostic capacities at all levels of the health system, espe-
cially in terms of staff levels, skill sets and infrastructure.
It is critical, therefore, that countries support the efforts
to strengthen laboratory capacities for the detection of
a wide range of pathogens in relation to the IDSR prior-
ity diseases. Moreover, laboratory networking should be
encouraged and should involve both national, regional
and research reference laboratories. To address the gaps
in knowledge, it is important to strengthen the labora-
tory management information systems (LIMS), recruit
adequate staff who are well trained and motivated as well
as the need for periodic support supervision of the sur-
veillance activities. e plan by the African Centres for
Disease Control and Prevention to establish and opera-
tionalize a Regional Integrated Surveillance and Labora-
tory Network is commended. is network is expected
to coordinate and connect the continent’s analytical, sur-
veillance, and emergency-response assets [85].
Digital disease surveillance
An effective epidemic intelligence should contain both
indicator-based and event-based surveillance. Globally,
with the use of information technologies, an event-based
surveillance approach is being promoted to complement
the traditional “indicator-based” surveillance approach
as part of the components of epidemic intelligence [86].
ere have been growing interests in event-based inter-
net bio-surveillance systems also referred to as digital
disease surveillance (DDS) in recent years. DDS is the
use of data generated outside the public health system
for disease surveillance [86]. It involves the aggrega-
tion and analysis of data available on the internet, such
as search engines, social media and mobile phones, and
not directly associated with patient illnesses or medical
encounters. It has been shown that digital approaches
in surveillance improve the timeliness and depth of sur-
veillance information in high-income countries [86, 87].
Recently, DDS has been used in responding to COVID-
19 through case detection, contact tracing and isolation,
and quarantine in several countries, including Taiwan,
New Zealand and ailand [88, 89]. In about 30 coun-
tries, algorithmic contact tracing through the use of a
cell phone app or operating system has been deployed in
response to the COVID-19 pandemic [89].
ere is growing interest in using digital surveillance
approaches to improve monitoring and control of infec-
tious disease outbreaks [86]. However, such applications
are scarce in Africa, and few studies have shown a direct
connection between DDS and public health actions. So
far, DDS has demonstrated its potential in early detection
and response to Ebola and COVID-19 epidemics [90–95].
In a recent systematic review of the mobile-based infec-
tious disease outbreak management systems (SORMAS)
[93, 95, 96] was identified as having capacities to fully
integrate data from case management, contact tracing,
laboratory work and surveillance components. Currently,
the Africa Centre’s for Disease Control and Prevention
is implementing a pilot Programme in Ghana, Liberia,
Madagascar, Nigeria, Sierra Leone and South Africa to
develop digital surveillance indicators and online disease
dashboards based on social media to inform infectious
disease surveillance [97]. Moreover, there are ongoing
efforts to create real-time data sharing platforms for dis-
ease surveillance using mobile technologies to allow cen-
tralized data management and use [96]. is is expected
to strengthen real-time surveillance of infectious diseases
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 8 of 15
Mremietal. One Health Outlook (2021) 3:22
in the continent, guide interventions, and build capacity
in big data approaches for outbreak prediction, analysis
and prevention.
With the proliferation of information technologies and
increased ownership of mobile phones in SSA, there are
large amounts of data on social media blogs, chatrooms,
and local news reports that may provide governments
and other stakeholders’ clues about disease outbreaks
time and place daily. Such data are essential raw mate-
rials for DDS. Advancements in information technology
and information sharing give rise to infodemiology –
defined as the science of distribution and determinants
of information in an electronic medium, specifically the
internet [90]. To date, Program for Monitoring Emerg-
ing Diseases (ProMED-mail) [98] and HealthMap [99]
are among the several leading efforts in digital surveil-
lance. e World Health Organization routinely uses
HealthMap, ProMED and similar systems to monitor
infectious disease outbreaks and inform public health
officials and the general public [100]. e key advantages
of DDS include speed and volume, which may increas-
ingly help health officials to spot outbreaks quickly and
cheaply [96].
Community event‑based surveillance
Community-based surveillance (CBS) is defined as the
systematic detection and reporting of events of pub-
lic health significance within a community by commu-
nity members [101]. Community engagement has long
been an essential part of both human and animal health
[102–105]. CBS has played a significant role in smallpox,
guinea worm and polio eradication programmes [103].
Recently, CBS was reported as an important component
in response to the West African Ebola virus disease out-
break of 2014–2016, where community health workers
and volunteers worked together in early detection and
timely reporting to the health system [106]. With CBS,
public engagement is being transformed through partici-
patory surveillance systems that enable the community
to directly report disease events via information technol-
ogy and communication tools [107]. Several CBS systems
have been described and demonstrated their accuracy
and sensitivity, their ability to provide more timely dis-
ease activity measures, and their usefulness in identifying
risk groups, assessing the burden of illness and inform-
ing disease transmission models [108, 109]. CBS can pro-
vide early warning for emerging events by engaging the
communities to detect potential public health events and
connecting individuals to health services [3, 110, 111]. In
a study in Ivory Coast, following the implementation of
CBS, 5 to eightfold increases in reporting of suspected
measles and yellow fever clusters have been reported
[110]. ese findings suggest that CBS can be used to
strengthen the detection and reporting capabilities for
several suspect priority diseases and events.
e WHO Technical Guidelines on IDSR [13, 17] high-
light the need for CBS. is is because most of the health
problems and events happen at the community level,
thereby placing the community as the primary sensor of
the disease signals. us, putting a surveillance mecha-
nism to obtain information at the community level is an
added advantage to capture diseases and public health
events at their early stages to allow effective preparedness
and response, thereby managing disease outbreaks at the
source. Despite the relevance of the inclusion of commu-
nity information in surveillance, by the end of 2017, only
32 (68%) of the 44 countries in the WHO Africa region
had commenced CBS, and 35 (74%) had event-based sur-
veillance [14]. However, there is only one report from
Sierra Leone that indicates data collected from the two
approaches are integrated into the national IDSR system
[110]. In some countries, the CBS Programme are still
operating as pilot or research projects [112, 113], and
most cover a limited geographical area and are mainly for
specific disease programmes in rural settings [110].
One health surveillance
As part of an effective global response to diseases trans-
mitted between animals and humans [114], there have
been calls for integrating surveillance of zoonotic dis-
eases in human and animal populations. e driving
force is that about three-quarters of humans’ emerging
infectious diseases have animal origin [115]. One health
(OH) concept promotes the multi-sectoral collaboration
between human, animal, and environmental health dis-
ciplines and sectors in addressing complex health issues
[114]. Several African countries have carried out their
prioritisation exercises on the zoonoses. Among the dis-
eases that were ranked high include anthrax, brucello-
sis, viral haemorrhagic fevers, zoonotic avian influenza,
human African trypanosomiasis, rabies and plague [116–
120]. With this approach, OH surveillance is strongly
encouraged at all levels to efficiently manage and coor-
dinate health events involving humans, animals and their
environment [16]. However, there are issues that need to
be considered and addressed in the adoption of OH sur-
veillance. ese include the need to define the charac-
teristics of OH surveillance and identify the appropriate
mechanisms for inter-sectoral and multi-disciplinary col-
laboration [81, 116].
In 2019, the Tripartite organisations – the Food and
Agriculture Organization of the United Nations, the
World Organisation for Animal Health, and the World
Health Organization – developed the Tripartite Zoon-
oses Guide (TZG). e aim is to help the countries
develop a capacity to address zoonoses in a coordinated
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 15
Mremietal. One Health Outlook (2021) 3:22
manner, linking to existing international policies and
frameworks and supporting efforts for global health
security. e TZG includes three operational tools to
support national authorities: (i) the Multi-sectoral Coor-
dination Mechanism, (ii) the Joint Risk Assessment, and
(iii) the Surveillance and Information Sharing operation
tools [121].
Towards multi‑sectoral andmulti‑indicator surveillance
e emerging and re-emerging infectious diseases in
Africa underline the urgent need to integrate public
health surveillance systems [122]. As infectious disease
threats increase in SSA, effective ways of predicting
outbreaks and planning for outbreak responses become
increasingly important. However, an epidemic intel-
ligence that encompasses early warning functions for
infectious diseases of humans and animals in SSA is
almost non-existent. erefore, we propose the devel-
opment and adoption of a national platform for public
health surveillance that is a multi-sectoral, multi-dis-
ease and multi-indicator epidemic intelligence system
(Fig.2). e system is envisaged to consolidate infor-
mation from existing surveillance systems to define
composite surveillance indicators with intelligence to
trigger and guide unified responses to public health
threats across sectors and diseases that share com-
mon risks. In One Health perspective, such a system
may reduce the hurdle of monitoring enormous sec-
tor-specific and single-disease indicators, strengthen
multi-sectoral collaboration, improve data quality and
ultimately IDSR performance. For its operationalisa-
tion, a multi-sectoral coordination mechanism with
representatives from the sectoral ministries should be
established with timely-defined rotational leadership
between the sectors responsible for human, animal and
environmental health.
e importance of using both formal and informal
data sources for timely and accurate infectious disease
outbreak surveillance has been emphasized [86]. Evi-
dence-based outbreak preparedness provides ground
to streamline and concentrate efforts towards diseases
that have been documented to circulate. Among other
things, outbreak preparedness entails predicting pos-
sible epidemics with regards to the possible location of
involvement, the risk and vulnerability of the popula-
tion, the extent of the outbreak, its spread and socio-
economic consequences. erefore, for any effective
outbreak preparedness plan, information on prior risks
is crucial in setting robust outbreak management and
response plans. Research findings for decades have dis-
played mapping of exposure patterns and the burden of
infectious diseases that can cause outbreaks in the com-
munity [5].
Modern technologies such as artificial intelligence
and machine learning are widely applied in analysing a
significant volume of data to assess the status and fore-
cast future dynamics of diseases [123, 124]. A number of
prediction models have been developed to provide event
prediction, special ecological niche, diagnostic or clinical,
spread or response information. e prediction models
are valuable for disease prevention and saving disability-
adjusted life years [125]. ey also save valuable financial
resources due to the high costs and resource utilisation
associated with traditional surveillance systems. ese
emerging technologies are likely to become powerful
means of facilitating the collection of more accurate and
timely information, leading to information-based evi-
dence. e techniques are expected to allow decision-
makers to identify areas where the model predicts a
particular risk category with certainty to effectively target
limited resources to those areas most at risk for a given
season.
Conclusion
is review indicates that most countries in SSA rely
mainly on traditional indicator-based disease surveil-
lance utilising data from healthcare facilities with
limited use of data from other sources. However,
the traditional indicator-based disease surveillance
approaches face several challenges, including data
quality and inefficient early warning systems, because
they are less sensitive than event-based surveillance
approaches. ey most often miss information from
populations who do not access health care or do so
through informal channels, thus unable to detect new,
potentially high-impact disease outbreaks. Moreover,
there is a dearth of information on IDSR data qual-
ity, analyses that utilise advanced methodologies and
use in the detection and response of infectious disease
outbreaks in the region. Over the years, data-use and
data-process have not been given adequate attention.
is analysis indicates that future efforts to address
disease surveillance systems should consider data qual-
ity, multi-source data analysis and triangulation, data
use and data integration. Capacity building for health
workers at the national and sub-national levels in data
management is critical.
is review highlights the untapped opportunities
for integrating community-based, digital surveillance
through a one health approach that could improve
public health surveillance in SSA. It is high time that
the region explores and adopts the integration of sev-
eral surveillance programmes into hybrid systems that
combine traditional surveillance data with data from
the public health laboratories, community, research
settings, search queries, social media posts, and
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 10 of 15
Mremietal. One Health Outlook (2021) 3:22
crowdsourcing. Improved performance requires the
merging of current gains, strong collaboration from all
stakeholders, supervision and regular evaluation of the
surveillance system to identify and address challenges
as they emerge. e introduction of innovative ways to
further strengthen the surveillance and response system
in SSA countries is critical to enhancing early detection
and reporting of suspected cases of priority diseases,
conditions and events.
To address the challenges of the IDSR system, there
is a need to develop an electronic platform that will
combine data from multiple relevant databases such
as HMIS, research programmes, laboratory manage-
ment information systems (LMIS), population-based
Fig. 2 National Platform for a Multi-Sectoral, Multi-Disease and Multi-Indicator (3Ms) Epidemic Intelligence System
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 11 of 15
Mremietal. One Health Outlook (2021) 3:22
surveys, digital disease surveillance, sentinel surveil-
lance, OH surveillance and community-based sur-
veillance initiatives to allow their interoperability.
The aim is to make optimal use of community, facil-
ity and research-based epidemiological information
in preparing the community to act before a health
emergency happens, as well as to provide high-quality
evidence to guide policy development and resource
allocation at the national level. With this platform, a
continuing analysis and review of scientific publica-
tions, social media, routine health data and demo-
graphic statistics can be established to feed different
decision-making units. Composite and multi-sourced
indicators that comprise information from various
sources can be generated, analysed and monitored.
The goal is to make data readily available and help
speed up dissecting the information and putting pro-
grammes in place to detect and promptly respond to
epidemics. The platform will foster improved utilisa-
tion of surveillance data for action and avoid delays in
response to emergencies by linking health indicators
with other information such as climate data that can
add value to inform health risks accurately. A multi-
sectoral approach should be used to pursue a common
strategic goal of developing a workforce that can sup-
port public health surveillance and response.
Abbreviations
CBS: Community-based Surveillance; CDC: Centres for Disease Control and
Prevention; DDS: Digital disease surveillance; DHIS: District Health Information
System; HIS: Health Information Systems; HMIS: Health Management Informa-
tion System; ICT: Information and Communication Technology; IDS: Integrated
Disease Surveillance; IDSR: Integrated Disease Surveillance and Response;
OH: One Health; PRISMA-P: Preferred Reporting Items for Systematic Reviews
and Meta-Analysis Protocols; SMS: Short Message Service; SSA: Sub-Saharan
Africa; USSD: Unstructured Supplementary Service Data; WHO: World Health
Organization.
Acknowledgements
The authors would like to thank Sokoine University of Agriculture’s One Health
Sciences Community of Practice members for their support and contribution
during the earlier development of the manuscript.
Authors’ contributions
Both authors made substantial contributions to the conception and design
of the review. IRM led data acquisition and analysis, with SFR, CS and LEGM
responsible for the interpretation. IRM and JG drafted the work, SIK and LEGM
supervised all stages of the review and contributed to the manuscript’s writ-
ing. IRM, LEGM and SFR provided substantial revisions. Both authors revised
and approved the final version of this paper.
Funding
This research has not received any project-specific funding. IRM and JG are
PhD students supported by the World Bank and Government of the United
Republic of Tanzania Scholarship through SACIDS Foundation for One
Health.
Availability of data and materials
All data relevant to the study are included in the article. No data are stored in a
repository. No unpublished data are available following this review.
Declarations
Ethics approval and consent to participate
Not applicable.Patients and the public were not involved in the design or
conduct of the study.
Consent for publication
Not required.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Veterinary Medicine and Public Health, Sokoine University
of Agriculture, Morogoro, Tanzania. 2 SACIDS Foundation for One Health,
Sokoine University of Agriculture, Morogoro, Tanzania. 3 National Institute
for Medical Research, Dar es Salaam, Tanzania. 4 Malaria Atlas Project, Geospa-
tial Health and Development, Telethon Kids Institute, West Perth, Australia.
5 National Institute for Medical Research, Tabora Research Centre, Tabora,
Tanzania.
Received: 31 March 2021 Accepted: 18 August 2021
References
1. Fenollar F, Mediannikov O. Emerging infectious diseases in Africa in the
21st century. New Microbes New Infect. 2018;26:S10–8. https:// doi. org/
10. 1016/j. nmni. 2018. 09. 004.
2. Talisuna AO, Okiro EA, Yahaya AA, Stephen M, Bonkoungou B, Musa EO,
et al. Spatial and temporal distribution of infectious disease epidemics,
disasters and other potential public health emergencies in the World
Health Organisation Africa region, 2016–2018. Glob Health. 2020;16:9.
https:// doi. org/ 10. 1186/ s12992- 019- 050-4.
3. Perry H, Dhillon R, Liu A, Chitnis K, Panjabi R, Palazuelos D, et al.
Community health worker programmes after the 2013–2016 Ebola
outbreak. Bull World Health Organ. 2016;94(7):551-553.9. https:// doi.
org/ 10. 2471/ BLT. 15. 164020.
4. Maganga GD, Kapetshi J, Berthet N, KebelaIlunga B, Kabange F, et al.
Ebola virus disease in the Democratic Republic of Congo. New Engl J
Med. 2014;371(22):2083–91. https:// doi. org/ 10. 1056/ NEJMo a1411 099.
5. Saker L, Lee K, Cannito B, Gilmore A, Campbell-Lendrum D. Globaliza-
tion and infectious diseases: A review of the linkages. UNDP/World
Bank/WHO Special Programme for Research and Training in Tropical
Diseases (TDR), TDR/STR/SEB/ST/04.2. 2004. https:// www. who. int/ tdr/
publi catio ns/ docum ents/.
6. Rugarabamu S, Mboera LEG, Rweyemamu MM, Mwanyika G, Lutwama
J, et al. Forty-two years of responding to Ebola virus outbreaks in Sub-
Saharan Africa: a review. BMJ Glob Health. 2020;5:e001955. https:// doi.
org/ 10. 1136/ bmjgh- 2019- 001955.
7. Rweyemamu M, Otim-Nape W, Serwadda D. Foresight. Infectious
diseases: preparing for the future Africa. London: Office of Science and
Innovation; 2006.
8. Tambo E, Ugwu EC, Ngogang JY. Need of surveillance response systems
to combat Ebola outbreaks and other emerging infectious diseases in
African countries. Infect Dis Poverty. 2014;3(1):29. https:// doi. org/ 10.
1186/ 2049- 9957-3- 29.
9. World Health Organization: Regional Office for Africa: Resolution AFR/
RC 48/R2 of 2 1998.
10. Mandyata CB, Olowski LK, Mutale W. Challenges of implementing the
integrated disease surveillance and response strategy in Zambia: A
health worker perspective. BMC Public Health. 2017;17(1):1–12. https://
doi. org/ 10. 1186/ s12889- 017- 4791-9.
11. Kasolo F, Yoti Z, Bakyaita N, Gaturuku P, Katz R, Fischer JE, et al. IDSR as a
platform for implementing IHR in African countries. Biosecur Bioterror.
2013;11(3):163–9. https:// doi. org/ 10. 1089/ bsp. 2013. 0032.
12. Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V, CDC Work-
ing Group. Framework for evaluating public health surveillance
systems for early detection of outbreaks. Recommendations from the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 12 of 15
Mremietal. One Health Outlook (2021) 3:22
Centre for Disease Control and Prevention Working Group. MMWR.
2004;53:1–11.
13. WHO. Technical guidelines for integrated disease surveillance and
response in the African Region. Geneva and Atlanta: WHO and CDC;
2010. Available from: https:// afro. who. int/ sites/ defau lt/ files/ 2017- 06/
IDSR_ Techn ica_ Guide lines_ Final_ 2010_0. pdf.
14. Fall IS, Rajatonirina S, Yahaya AA, Zabulon Y, Nsubuga P, Nanyunja M,
et al. Integrated Disease Surveillance and Response (IDSR) strat-
egy: current status, challenges and perspectives for the future in
Africa. BMJ Glob Health. 2019;4(4):e001427. https:// doi. org/ 10. 1136/
bmjgh- 2019- 001427.
15. Nsubuga P, Brown WG, Groseclose SL, Ahadzie L, Talisuna P,
Mmbuji P, et al. Implementing integrated disease surveillance and
response: Four African countries’ experience, 1998–2005. Glob
Public Health. 2010;5(4):364–80. https:// doi. org/ 10. 1080/ 17441
69090 33349 43.
16. Perry HN, McDonnell SM, Alemu W, Nsubuga P, Chungong S, Otten
MW Jr, et al. Planning an integrated disease surveillance and response
system: a matrix of skills and activities. BMC Med. 2007;5:24. https:// doi.
org/ 10. 1186/ 1741- 7015-5- 24.
17. WHO. Technical guidelines for integrated disease surveillance and
response in the World Health Organization African Region. 3rd ed.
Geneva: World Health Organization; 2019.
18. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al.
Preferred reporting items for systematic review and meta-analysis
protocols (PRISMA-P ) 2015 statement. Syst Rev. 2015;4:1–9. https:// doi.
org/ 10. 1186/ 2046- 4053-4-1.
19. Sow I, Alemu W, Nanyunja M, Duale S, Perry HN, Gaturuku P. Trained
district health personnel and the performance of integrated disease
surveillance in the WHO African Region. East Afr J Public Health.
2010;7(1):16–9. https:// doi. org/ 10. 4314/ eajph. v7i1. 64671.
20. Cáceres VM, Sidibe S, Andre M, Traicoff D, Lambert S, King ME, et al.
Surveillance training for ebola preparedness in Côte d’Ivoire, Guinea-
Bissau, Senegal, and Mali. Emerg Infect Dis. 2017;23(13). https:// doi. org/
10. 3201/ eid23 13. 170299.
21. Mandja BM, Bompangue D, Handschumacher P, Ganzalez J-P, Salem
G, Muyembe J-J, et al. The score of integrated disease surveillance and
response adequacy (SIA): a pragmatic score for comparing weekly
reported diseases based on a systematic review. BMC Public Health.
2019;19:624. https:// doi. org/ 10. 1186/ s12889- 019- 6954-3.
22. Alemu T, Gutema H, Legesse S, Nigussie T, Yenew Y, Gashe K. Evaluation
of public health surveillance system performance in Dangila district,
Northwest Ethiopia: a concurrent embedded mixed quantitative/
qualitative facility-based cross-sectional study. BMC Public Health.
2019;19:1343. https:// doi. org/ 10. 1186/ s12889- 019- 7724-y.
23. Begashaw B, Tesfaye T. Assessment of integrated disease surveillance
and response implementation in special health facilities of Dawuro
Zone. J Anesthesiol. 2016;4(3):11–5. https:// doi. org/ 10. 11648/j. ja. 20160
403. 11.
24. Adokiya MN, Awoonor-Williams JK, Barau IY, Beiersmann C, Mueller O.
Evaluation of the integrated disease surveillance and response system
for infectious diseases control in northern Ghana. BMC Public Health.
2015;15(1):1–11. https:// doi. org/ 10. 1186/ s12889- 015- 1397-y.
25. Adokiya MN, Awoonor-Williams JK, Beiersmann C, Müller O. The inte-
grated disease surveillance and response system in northern Ghana:
challenges to the core and support functions. BMC Health Serv Res.
2015;15:288. https:// doi. org/ 10. 1186/ s12913- 015- 0960-7.
26. Adokiya MN, Awoonor-Williams JK, Beiersmann C, Müller O. Evaluation
of the reporting completeness and timeliness of the integrated disease
surveillance and response system in northern Ghana. Ghana Med J.
2016;50(1):3–8. https:// doi. org/ 10. 4314/ gmj. v50i1.1.
27. Issah K, Nartey K, Amoah R, Bachan EG, Aleeba J, Yeetey E, et al. Assess-
ment of the usefulness of integrated disease surveillance and response
on suspected Ebola cases in the Brong Ahafo Region. Ghana Infect Dis
Poverty. 2015;4:17.
28. Hemingway-Foday JJ, Souare O, Diallo BI, Bah M, Kaba AK, et al. Improv-
ing integrated disease surveillance and response capacity in Guinea,
2015–2018. Online J Public Health Inform. 2019;11(1). https:// doi. org/
10. 5210/ ojphi. v11i1. 9837.
29. Collins D, Rhea S, Diallo BI, Bah MB, Yattara F, Keleba RG, et al. Surveil-
lance system assessment in Guinea: Training needed to strengthen
data quality and analysis, 2016. PLoS ONE. 2020;15(6). https:// doi. org/
10. 1371/ journ al. pone. 02347 96.
30. Mwatondo AJ, Ng’ang’a Z, Maina C, Makayotto L, Mwangi M, Njeru I,
et al. Factors associated with adequate weekly reporting for disease
surveillance data among health facilities in Nairobi County, Kenya, 2013.
Pan Afr Med J. 2016;23:165. https:// doi. org/ 10. 11604/ pamj. 2016. 23. 165.
8758.
31. Nakiire L, Masiira B, Kihembo C, Katushabe E, Natseri N, Nabukenya
I, et al. Healthcare workers’ experiences regarding scaling up of
training on integrated disease surveillance and response (IDSR) in
Uganda, 2016: cross sectional qualitative study. BMC Health Serv Res.
2019;19:117. https:// doi. org/ 10. 1186/ s12913- 019- 3923-6.
32. Nagbe T, Yealue K, Yeabah T, Rude JM, Fallah M, Skrip L, Agbo C,
et al. Integrated disease surveillance and response implementation
in Liberia, findings from a data quality audit, 2017. Pan Afr Med J.
2019;33(Supp 2):10. https:// doi. org/ 10. 11604/ pamj. supp. 2019. 33.2.
17608.
33. Randriamiarana R, Raminosoa G, Vonjitsara N, Randrianasolo R, Halm A,
et al. Evaluation of the reinforced integrated disease surveillance and
response strategy using short message service data transmission in
two southern regions of Madagascar, 2014–15. BMC Health Serv Res.
2018;18(1):265. https:// doi. org/ 10. 1186/ s12913- 018- 3081-2.
34. Wu JT-S, Kagoli M, Kaasbøll JJ, Bjune GA. Integrated Disease Surveil-
lance and Response (IDSR) in Malawi: Implementation gaps and
challenges for timely alert. PLoS ONE. 2018;13(11). https:// doi. org/ 10.
1371/ journ al. pone. 02008 58.
35. Ibrahim LM, Stephen M, Okudo I, Kitgakka SM, Mamadu IN, Njai
IF, et al. A rapid assessment of the implementation of integrated
disease surveillance and response system in Northeast Nigeria,
2017. BMC Public Health. 2020;20(1):1–8. https:// doi. org/ 10. 1186/
s12889- 020- 08707-4.
36. Jinadu KA, Adebiyi AO, Sekoni OO, Bamgboye EA. Integrated disease
surveillance and response strategy for epidemic prone diseases
at the primary health care (PHC) level in Oyo State, Nigeria: What
do health care workers know and feel? Pan Afr Med J. 2018;31:19.
https:// doi. org/ 10. 11604/ pamj. 2018. 31. 19. 15828.
37. Nnebue CC, Onwasigwwe CN, Adogu PO, Adinma ED. Challenges of
data collection and disease notification in Anambra State, Nigeria.
Trop J Med Res. 2014;17(1):1–6. https:// doi. org/ 10. 4103/ 1119- 0388.
130173.
38. Motilewa O, Akwaowo CD, Ekanem AM. Assessment of implementa-
tion of integrated disease surveillance and response in Akwaibom
State Nigeria. Ibom Med J. 2015;8(1):24–5.
39. Thierry N, Kabeja A, Asiimwe A, Binagwaho A, Koama JB, Johnson P,
et al. A national electronic system for disease surveillance in Rwanda
(eIDSR): Lessons learned from a successful implementation. Online
J Public Health Inform. 2014;6(1). https:// doi. org/ 10. 5210/ ojphi. v6i1.
5014.
40. Njuguna C, Jambai A, Chimbaru A, Nordstrom A, Conteh R, Latt A,
et al. Revitalization of integrated disease surveillance and response
in Sierra Leone post Ebola virus disease outbreak. BMC Public Health.
2019;19:364. https:// doi. org/ 10. 1186/ s12889- 019- 6636-1.
41. Sahal N, Reintjes R, Eltayeb EM, Aro AR. Assessment of core activities
and supportive functions for the communicable diseases surveillance
system in Khartoum state, Sudan, 2005–2007. E Mediterr Health J.
2010;16(12):1204–10. https:// doi. org/ 10. 26719/ 2010. 16. 12. 1204.
42. Mboera LEG, Rumisha SF, Magesa SM, Kitua AY. (2001) Utilisation of
health management information system in disease surveillance in
Tanzania. Tanzania Health Res Bull. 2001;3(2):15–8. https:// doi. org/ 10.
4314/ thrb. v3i2. 14213.
43. Mboera LEG, Rumisha SF, Mlacha T, Mayala BK, Bwana VM, Shayo EH.
Malaria surveillance and use of evidence in planning and decision mak-
ing in Kilosa District, Tanzania. Tanzania J Health Res. 2017;19(3). https://
doi. org/ 10. 4314/ thrb. v19i3.7.
44. Mghamba JM, Mboera LEG, Krekamoo W, Senkoro KP, Rumisha SF,
Shayo EH, et al. Challenges of implementing integrated disease surveil-
lance and response strategy using the current health management
information system in Tanzania. Tanzania Health Res Bull. 2004;6:57–63.
https:// doi. org/ 10. 4314/ thrb. v6i2. 14243.
45. Nsubuga P, Eseko N, Wuhib T, Ndayimirije N, Chungong S, McNabb
S. Structure and performance of infectious disease surveillance and
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 15
Mremietal. One Health Outlook (2021) 3:22
response, United Republic of Tanzania, 1998. Bull World Health Organ.
2002;80:196–203. https:// doi. org/ 10. 1590/ S0042- 96862 00200 03000 05.
46. Rumisha SF, Mboera LEG, Senkoro SF, Gueye D, Mmbuji PL. Monitor-
ing and evaluation of Integrated disease surveillance and response
in selected districts in Tanzania. Tanzania Health Res Bull. 2007;9:1–11.
https:// doi. org/ 10. 4314/ thrb. v9i1. 14285.
47. Pascoe L, Lungo J, Kaasbøll J, Kileleni I. Collecting integrated disease
surveillance and response data through mobile phones. ISTAfrica 2012
Conference Proceedings. Paul Cunningham and Miriam Cunningham
(Eds). IIMC Int Inf Manage Corporation. 2012;34–2.
48. Mboera LEG, Sindato C, Mremi IR, George J, Ngolongolo R, Rumisha SF,
et al. Socio-ecological systems analysis and health system readiness
in responding to dengue epidemics in Ilala and Kinondoni districts,
Tanzania. Front Pubic Health. 2021; (in press).
49. Franco LM, Setzer J, Banke K. Improving performance of IDSR at district
and facility levels: experiences in Tanzania and Ghana in making IDSR
operational; 2006.
50. Kihembo C, Masiira B, Nakiire L, Katushabe E, Natseri N, Nabukenya I,
et al. The design and implementation of the re-vitalised integrated dis-
ease surveillance and response (IDSR) in Uganda, 2013–2016. BMC Pub-
lic Health. 2018;18(1):1–11. https:// doi. org/ 10. 1186/ s12889- 018- 5755-4.
51. Lukwago L, Nanyunja M, Ndayimirije N, Wamala J, Malimbo M, Mbabazi
W, et al. The implementation of integrated disease surveillance and
response in Uganda: a review of progress and challenges between
2001 and 2007. Health Policy Plan. 2012;28:30–40. https:// doi. org/ 10.
1093/ heapol/ czs022.
52. Masiira B, Nakiiere L, Kihembo C, Katushabe E, Natseri N, Nabukenya I,
et al. Evaluation of integrated disease surveillance and response (IDSR)
core and support functions after the revitalisation of IDSR in Uganda
from 2012 to 2016. BMC Public Health. 2019;19:46. https:// doi. org/ 10.
1186/ s12889- 018- 6336-2.
53. Wamala JF, Okot C, Makumbi I, Natseri N, Kisakye A, Nanyunja M, et al.
Assessment of core capacities for the International Health Regulations
(IHR [2005]) – Uganda, 2009. BMC Public Health. 2010;10(Suppl 1):S9
https:// www. biome dcent ral. com/ 1471- 2458/ 10/ S1/ 59.
54. Haakonde T, Lingenda G, Munsaje F, Chishimba K. Assessment of factors
affecting the implementation of the integrated disease surveillance
and response in public health care facilities - the case of Rufunsa
District, Zambia. Divers Equal Health Care. 2018;15(1):15–22. https:// doi.
org/ 10. 21767/ 2049- 5471. 10001 23.
55. Kooma EK. Assessment of the integrated disease surveillance and
response implementation in selected health facilities of Southern
Province of Zambia. Int J Adv Res. 2019;7(4):961–76. https:// doi. org/ 10.
21474/ IJAR01/ 8914.
56. Wolfe CM, Hamblion EL, Dzotsi EK, Mboussou F, Eckerle I, Flahault
A, et al. Systematic review of Integrated Disease Surveillance and
Response (IDSR) implementation in the African region. PLoS ONE.
2021;16(2):e0245457. https:// doi. org/ 10. 1371/ journ al. pone. 02454 57.
57. Mwengee W, Okeibunor J, Poy A, Shaba K, Kinuani LM, Minkoulou E,
et al. Polio eradication initiative: Contribution to improved com-
municable diseases surveillance in WHO African region. Vaccine.
2016;34(43):5170–4. https:// doi. org/ 10. 1016/j. vacci ne. 2016. 05. 060.
58. Maïga A, Jiwani SS, Mutua MK, Porth TA, Taylor CM, Asiki G, et al. Count-
down to 2030 collaboration for Eastern and Southern Africa. BMJ Glob
Health. 2019;4. https:// doi. org/ 10. 1136/ bmjgh- 2019- 001849.
59. Ahanhanzo YG, Ouendo E-M, Kpozèhouen A, Levêque A, Makoutodé M,
Dramaix-Wilmet M. Data quality assessment in the routine health infor-
mation system: an application of the lot quality assurance sampling in
Benin. Health Policy Plan. 2015;30(7):837–43. https:// doi. org/ 10. 1093/
heapol/ czu067.
60. Rumisha SF, Lyimo EP, Mremi IR, Tungu PK, Mwingira VS, Mbata D,
et al. Data quality of the routine health management information
system at the primary healthcare facility and district levels in Tanza-
nia. BMC Med Inform Decis. 2020;20:340. https:// doi. org/ 10. 1186/
s12911- 020- 01366-w.
61. Gimbel S, Micek M, Lambdin B, Lara J, Karagianis M, Cuembelo F, et al.
An assessment of routine primary care health information system
data quality in Sofala Province, Mozambique. Popul Health Metr.
2011;9(1):12. https:// doi. org/ 10. 1186/ 1478- 7954-9- 12.
62. Amoakoh-Coleman M, Kayode GA, Brown-Davies C, Agyepong IA,
Grobbee DE, Klipstein-Grobusch K, et al. Completeness and accuracy
of data transfer of routine maternal health services data in the greater
Accra region. BMC Res Notes. 2015;8(1):114. https:// doi. org/ 10. 1186/
s13104- 015- 1058-3.
63. Kasambara A, Kumwenda S, Kalulu K, Lungu K, Beattie T, Masangwi
S, et al. Assessment of implementation of the health management
information system at the district level in southern Malawi. Malawi Med
J. 2017;29(3):240–6. https:// doi. org/ 10. 4314/ mmj. v29i3.3.
64. Bhattacharya AA, Umar N, Audu A, Felix H, Allen E, Schellenberg JRM,
et al. Quality of routine facility data for monitoring priority maternal and
newborn indicators in DHIS2: a case study from Gombe State, Nigeria.
PLoS ONE. 2019;14(1). https:// doi. org/ 10. 1371/ journ al. pone. 02112 65.
65. Dehnavieh R, Haghdoost A, Khosravi A, Hoseinabadi F, Rahimi H, et al.
The District Health Information System (DHIS2): a literature review and
metasynthesis of its strengths and operational challenges based on
the experiences of 11 countries. Health Inf Manag J. 2019;48(2):62–75.
https:// doi. org/ 10. 1177/ 18333 58318 777713.
66. Nkengasong JN, Nsubuga P, Nwanyanwu O, et al. Laboratory systems
and services are critical in the global health: time to end the neglect?
Am J Clin Pathol. 2010;134(3):368–73. https:// doi. org/ 10. 1309/ AJCPM
PSINQ 9BRMU6.
67. Onyebujoh PC, Thirumala AK, Ndihokubwayo J-B. Integrating laboratory
networks, surveillance systems and public health institutes in Africa. Afr
J Lab Med. 2016;5(3):a431. https:// doi. org/ 10. 4102/ ajlm. v5i3. 431.
68. Buckee CO, Cardenas MIE, Corpuz J, Ghosh A, Haque F, Karim J, et al.
Productive disruption: opportunities and challenges for innovation
in infectious disease surveillance. BMJ Glob Health. 2017;3:e000538.
https:// doi. org/ 10. 1136/ bmjgh- 2017- 00053.
69. Simba DO, Kakoko DC, Warsame M, Premji Z, Gomez MF, Tomson
G, et al. Understanding caretakers’ dilemma in deciding whether
or not to adhere with referral advice after pre-referral treatment
with rectal artesunate. Malar J. 2010;9:123. https:// doi. org/ 10. 1186/
1475- 2875-9- 123.
70. Ngugi AK, Agoi F, Mahoney MR, Lakhani A, Mang’ong’o D, Nderitu E,
et al. Utilization of health services in a resource-limited rural area in
Kenya: Prevalence and associated household-level factors. PLoS ONE.
2017;12(2):e0172728. https:// doi. org/ 10. 1371/ journ al. pone. 01727 28.
71. Zyaambo C, Siziya S, Fylkesnes K. Health status and socio-economic
factors associated with health facility utilization in rural and urban areas
in Zambia. BMC Health Serv Res. 2012;12:389. https:// doi. org/ 10. 1186/
1472- 6963- 12- 389.
72. Assefa T, Belachew T, Tegegn A, Deribew A. Mothers’ health care
seeking behaviour for childhood illnesses in Derra District, North-
shoa Zone, Oromia Regional State, Ethiopia. Ethiop J Health Sci.
2008;18(3):87–94.
73. Nsubuga P, Nwanyanwu O, Nkengasong JN, Mukanga D, Trostle M.
Strengthening public health surveillance and response using the
health systems strengthening agenda in developing countries. BMC
Public Health. 2010;10:S5. https:// doi. org/ 10. 1186/ 1471- 2458- 10- S1- S5.
74. Mbondji PE, Kabede D, Soumbey-Alley EW, Zielinski C, Kouvividila W,
Lusamba-Dikassa P-S. Health information systems in Africa: descriptive
analysis of data sources, information products and health statistics. J
Roy Soc Med. 2014;107(1S):34–45. https:// doi. org/ 10. 1177/ 01410 76814
53.
75. Teklegiorgis K, Tadesse K, Mirutse G, Terefe W. Level of data quality from
Health Management Information Systems in a resources limited setting
and its associated factors, eastern Ethiopia. S Afr J Inf Manag. 2016;18(1).
https:// doi. org/ 10. 4102/ sajim. v18i1. 612.
76. Awunor NS, Omuemu VO, Adam VY. Knowledge and practice of
disease surveillance and notification among resident doctors in a
tertiary health institution in Benin City: implications for health systems
strengthening. J Comm Med Prim Health Care. 2014;26(2):107–15.
77. Toda M, Zurovac D, Njeru I, Kareko D, Mwau M, Morita K. Health worker
knowledge of integrated disease surveillance and response stand-
ard case definitions: a cross-sectional survey at rural health facilities
in Kenya. BMC Public Health. 2018;18:146. https:// doi. org/ 10. 1186/
s12889- 018- 5028-2.
78. Mutale W, Chintu N, Amoroso C, Awoonor-Williams K, Phillips J, Baynes
C, et al. Improving health information systems for decision making
across five sub-Saharan African countries: implementation strategies
from African Health Initiative. BMC Health Serv Res. 2013;13(Suppl.
2):59. https:// doi. org/ 10. 1186/ 1472- 6963- 13- S2- S9.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 15
Mremietal. One Health Outlook (2021) 3:22
79. Steele L, Orefuwa E, Dickmann P. Drivers of earlier infectious dis-
ease outbreak detection: a systematic literature review. Int J Inf Dis.
2016;53:15–20. https:// doi. org/ 10. 1016/j. ijid. 2016. 10. 0005.
80. Smith M, Madon S, Anifalaje A, Lazaro-Malecela M, Michael E. Integrated
health information systems in Tanzania: Experience and challenges.
Electr J Inform Syst Devel Count. 2008;33(1):1–21. https:// doi. org/ 10.
1002/j. 1681- 4835. 2008. tb002 27.x.
81. George J, Häsler B, Mremi IR, Sindato C, Mboera LEG, Rweyemamu MM,
et al. Integration mechanisms in human and animal health surveillance
systems: challenges and opportunities in addressing global health
security threats: A systematic review. One Health Outlook. 2020;2:11.
https:// doi. org/ 10. 1186/ s42522- 020- 00017-4.
82. Ilesanmi OS, Babasola OM. Clinician sensitization on integrated disease
surveillance and response in Federal Medical Centre Owo, Ondo State,
Nigeria, 2016. Public Health Indon. 2017;3(2).
83. Mathers CD, Fat M, Inoue M, Rao C, Lopez A. Counting the dead and
what they died from: an assessment of the global status of cause of
death data. Bull World Health Organ. 2005;2005(83):171–7.
84. Hay SI, George DB, Moyes CL, Brownstein JS. Big data opportunities for
global infectious disease surveillance. PLoS Med. 10:e1001413. https://
doi. org/ 10. 1371/ journ al. pmed. 10014 13.
85. Disease Surveillance, Emergency Preparedness, and Outbreak Response
in Eastern and Southern Africa: a situational assessment and five-year
action plan for the Africa CDC Strengthening Regional Public Health
Institutions and Capacity for Surveillance and Response Program 18
March 2021. https:// docum ents1. world bank. org/ curat ed/ en/ 30527
16160/.
86. O’Shea J. Digital disease detection: A systematic review of event-based
internet biosurveillance systems. Int J Med Inform. 2017;101:15–22.
87. Chretien J-P, Lewis SH. Electronic public health surveillance in develop-
ing settings: meeting summary. BMC Proceed. 2008;2(Suppl 3):S1.
https:// doi. org/ 10. 1186/ 1753- 6561-2- s3- s1.
88. Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan. Big
data analytics, new technology, and proactive testing. JAMA.
2020;323(14):1341–2. https:// doi. org/ 10. 1001/ jama. 2020. 3151.
89. Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner D,
et al. Quantifying SARS-CoV-2 transmission suggests epidemic control
with digital contact tracing. Science. 2020;368(6491):eabb6936. https://
doi. org/ 10. 1126/ scien ce. abb69 36.
90. Eysenbach G. SARS and population health technology. J Medical Int
Res. 2003;5:e14. https:// doi. org/ 10. 2196/ jmir.5. 2. e14.
91. Tom-Aba D, Nguku P, Arinze C, Krause G. Assessing the concepts and
designs of 58 mobile apps for the management of the 2014–2015
West Africa Ebola outbreak: systematic review. JMIR Public Health Surv.
2018;4:e68. https:// doi. org/ 10. 2196/ publi cheal th. 9015.
92. Alwashmi MF. The use of digital health in the detection and manage-
ment of COVID-19. Int J Env Res Public Health. 2020;17(8):2906. https://
doi. org/ 10. 3390/ ijerp h1708 2906.
93. Fähnrich C, Denecke K, Adeoye OO, Benzler J, Claus H, Kirchner G, Mall
S, Richter R, Schapranow MP, Schwarz NG, Tom-Aba D, Uflacker M, Pog-
gensee G, Krause G. Surveillance and Outbreak Response Management
System (SORMAS) to support the control of the Ebola virus disease
outbreak in West Africa. Euro Surveill. 2015;20(12):2109071. https:// doi.
org/ 10. 2807/ 1560- 7917. es2015. 20. 12. 21071.
94. Perscheid C, Benzler J, Hermann C, Janke M, Moyer D, Laedtke T, et al. Ebola
outbreak containment: real-time task and resource coordination with
SORMAS. Front ICT. 2018;5:7. https:// doi. org/ 10. 3389/ fict. 2018. 00007.
95. Adeoye O, Tom-Aba D, Ameh C, Ojo O, Ilori E, Gidado S, et al. Imple-
menting Surveillance and Outbreak Response Management and Analy-
sis System (SORMAS) for public health in west Africa- lessons learnt and
future direction. Int J Trop Dis Health. 2017;22(2):1–17. https:// doi. org/
10. 9734/ ijtdh/ 2017/ 31584.
96. Yavlinsky A, Lule SA, Burns R, Zumla A, McHugh TD, Ntoumi F, et al.
Mobile-based and open-source case detection and infectious disease
outbreak management systems: a review. Wellcome Open Res.
2020;5:37.
97. African Union. 2020. https:// afric acdc. org/ progr amme/ surve illan ce-
disea se- intel ligen ce/ digit al- disea se- surve illan ce/.
98. Morse SS, Rosenberg BH, Woodall J. ProMED global monitoring of
emerging diseases: design for a demonstration program. Health Policy.
1996;38:135–53. https:// doi. org/ 10. 1016/ 0168- 8510(96) 00863-9.
99. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global
infectious disease monitoring through automated classification
and visualization of internet media reports. J Am Med Inform Assn.
2008;15(2):150–7. https:// doi. org/ 10. 1197/ jamia. M2544.
100. Ratnayake R, Crowe SJ, Jasperse J, Privette G, Stone E, Miller L, et al.
Assessment of community event-based surveillance for Ebola virus dis-
ease, Sierra Leone, 2015. Emerg Inf Dis. 2016;22(8):1431-1437.4. https://
doi. org/ 10. 3201/ eid22 08. 160205.
101. Technical Contribution to the June 2018 WHO meeting. A definition for
community-based surveillance and a way forward: results of the WHO
global technical meeting, France, 26 to 28 June 2018. Euro Surveill.
2019;24(2):1800681. https:// doi. org/ 10. 2807/ 1560. 7917. ES. 2019. 24.2.
18006 81.
102. Kuehnel A, Keating P, Polonsky J, Haskew C, Schenkel K, de Waroux
OL, et al. Event-based surveillance at health facility and community
level in low-income and middle-income countries: a systematic
review. BMJ Glob Health. 2019;4(1):e001878. https:// doi. org/ 10. 1136/
bmjgh- 2019- 001878.
103. Ndiaye SM, Quick L, Sanda O, Niandou S. The value of community
participation in disease surveillance: a case study from Niger. Health
Promot Int. 2003;18:89–98. https:// doi. org/ 10. 1093/ heapro/ 18.2. 89.
104. Azhar M, Lubis AS, Siregar ES, Alders RG, Brum E, McGrane J, et al.
Participatory disease surveillance and response in Indonesia:
strengthening veterinary services and empowering communi-
ties to prevent and control highly pathogenic avian influenza.
Avian Dis. 2010;54(1 Suppl):749–53. https:// doi. org/ 10. 1637/
8713- 031809- Reg.1.
105. Marquet RL, Bartelds AIM, Van Noort SP, Koppeschaar CE, et al. Internet-
based monitoring of influenza-like illness (ILI) in the general population
of the Netherlands during the 2003–2004 influenza season. BMC Public
Health. 2006;6:242. https:// doi. org/ 10. 1186/ 1471- 2458-6- 242.
106. International Federation of Red Cross and Red Crescent Societies (IFRC).
Community-based surveillance: guiding principles. Geneva: IFRC; 2017.
Available from: https:// media. ifrc. org/ ifrc/ docum ent/ commu nity-
based- surve illan ce- guidi ng- princ iples/.
107. Wójcik OP, Brownstein JS, Chunara R, Johansson MA. Public health for
the people: participatory infectious disease surveillance in the digital
age. Emerg Themes Epidemiol. 2014;11:7. https:// doi. org/ 10. 1186/
1742- 7622- 11-7.
108. Brooks-Pollock E, Tilston N, Edmunds WJ, Eames KTD. Using an online
survey of healthcare-seeking behaviour to estimate the magnitude and
severity of the 2009 H1N1v influenza epidemic in England. BMC Infect
Dis. 2011;11:68. https:// doi. org/ 10. 1186/ 1471- 2334- 11- 68.
109. Stone E, Miller L, Jasperse J, Privette G, Beltran JCD, Jambai A, et al. Com-
munity event-based surveillance for Ebola virus disease in Sierra Leone:
implementation of a national-level system during a crisis. PLoS Curr.
2016;8:ecurrents.outbreaks.d119c71125b5cce312b9700d744c56d8.
110. Clara A, Ndiaye SM, Joseph B, Nzogu MA, Coulibaly D, Alroy KA,
et al. Community-Based Surveillance in Côte d’Ivoire. Health Secur.
2020;18(S1):23–33. https:// doi. org/ 10. 1089/ hs. 2019. 0062.
111. Clara A, Do TT, Dao ATP, Tran PD, Dang TQ, Tran QD, et al. Event-
based surveillance at community and healthcare facilities, Vietnam,
2016–2017. Emerg Inf Dis. 2018;24(9):1649-1658.10. https:// doi. org/ 10.
3201/ eid24 09. 171851.
112. Karimuribo ED, Mutagahywa E, Sindato C, Mboera L, Mwabukusi M,
Kariuki Njenga M, et al. A smartphone app AfyaData for innovative
one health disease surveillance from community to national levels in
Africa: intervention in disease surveillance. JMIR Pub Health Surveill.
2017;3(4):e94. https:// doi. org/ 10. 2196/ publi cheal th. 7373 http:// publi
cheal th. jmir. org/ 2017/4/ e94/.
113. N’Guessan S, Attiey HB, Ndiaye S, Diarrassouba M, McLain G, Shamamba
L, et al. Community-based surveillance: A pilot experiment in the
Kabadougou-Bafing-Folon health region in Côte d´Ivoire. J Int Epide-
miol Public Health. 2019;2:11 https:// www. afenet- journ al. net/ conte nt/
artic le/2/ 11/ full.
114. Morens DM, Fauci AS. Emerging infectious diseases: Threats to human
health and global stability. PLoS Pathog. 2013;9(7):e1003467. https://
doi. org/ 10. 1371/ journ al. ppat. 10034 67.
115. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al.
Global trends in emerging infectious diseases. Nature. 2008;451:990–3.
https:// doi. org/ 10. 1038/ natur e06536.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 15
Mremietal. One Health Outlook (2021) 3:22
•
fast, convenient online submission
•
thorough peer review by experienced researchers in your field
•
rapid publication on acceptance
•
support for research data, including large and complex data types
•
gold Open Access which fosters wider collaboration and increased citations
maximum visibility for your research: over 100M website views per year
•
At BMC, research is always in progress.
Learn more biomedcentral.com/submissions
Ready to submit your research
Ready to submit your research
? Choose BMC and benefit from:
? Choose BMC and benefit from:
116. Bordier M, Uea-Anuwong T, Binot A, Hendrikx P, Goutard FL. Character-
istics of one health surveillance systems: A systematic literature review.
Prev Vet Med. 2018;S0167–5877(18)30365–9. https:// doi. org/ 10. 1016/j.
preve tmed. 2018. 10. 005.
117. Munyua P, Bitek A, Osoro E, Pieracci EG, Muema J, Mwatondo A, et al.
Prioritization of zoonotic diseases in Kenya, 2015. PLoS ONE. 2016;11(8).
https:// doi. org/ 10. 1371/ journ al. pone. 01615 76.
118. Ohene S-A. Experiences with JEE and impact on rabies programmes.
World Health Organization Ghana Country Office, 2017. https:// rabie
salli ance. org/ resou rce/ sally- ohene ghana- jee- rabies- progr ampar
acon- 2018.
119. Semakatte M, Krishnasamy V, Bulage L, Nantima N, Monje F, Ndumu D,
et al. Multisectoral prioritization of zoonotic diseases in Uganda, 2017: A
One Health perspective. PLoS ONE. 2018;13(5). https:// doi. org/ 10. 1371/
journ al. pone. 01967 99.
120. Stolka KB, Ngoyi BF, Grimes KE, Hemingway-Foday JJ, Lubula L, Maga-
zani AN, et al. Assessing the surveillance system for priority zoonotic
diseases in the Democratic Republic of the Congo, 2017. Health Secur.
2018;16(S1). https:// doi. org/ 10. 1089/ hs. 2018. 0060.
121. Joint Risk Assessment Operational Tool (JRA OT) An operational tool
of the tripartite zoonoses guide taking a multisectoral, one health
approach: a tripartite guide to addressing zoonotic diseases in
countries. Available: https:// www. oie. int/ filea dmin/ Home/ eng/ Media_
Center/ docs/ pdf/ onehe althp ortal/ EN_ Joint RiskA ssess mentO perat ional
Tool_ webve rsion. pdf. Accessed 19 July 2021.
122. Onyebujoh PC, Thirumala AK, Ndihokubwayo J-B. Integrating labora-
tory networks, surveillance systems and public health institutes in
Africa. Afr J Lab Med. 2016;5(3):a431. https:// doi. org/ 10. 4102/ ajlm.
v5i3. 431.
123. Lowe R, Bailey TC, Stephenson DB, Jupp TE, et al. The develop-
ment of an early warning system for climate-sensitive disease risk
with a focus on dengue epidemics in Southeast Brazil. Stat Med.
2013;32:864–83.
124. Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases.
Artificial Intelligence in Precision Health, Chapter 18, 2020. https:// doi.
org/ 10. 1016/ B978-0- 12- 817133- 2. 00018-5.
125. Yang L-P, Liang S-Y, Wang X-J, Li X-J, Wu Y-L, Ma W. Burden of disease
measured by disability-adjusted life years and a disease forecasting
time series model of scrub typhus in Laiwu, China. PLoS Negl Trop Dis.
2015;9(1):e3420. https:// doi. org/ 10. 1371/ journ al. pntd. 00034 20.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.
