Architecture of a Learning Surveillance System for Malaria
Elimination in India
S D Sreeganga
1a
, Susanna G. Mitra
1b
and Arkalgud Ramaprasad
1,2 c
1
Ramaiah Public Policy Center, Bengaluru, India
2
University of Illinois, Chicago, U.S.A.
Keywords: Cyberenvironment, Learning System, Surveillance, Feedback-loop, Malaria Elimination.
Abstract: Surveillance is critical for malaria elimination. Malaria transmission takes place in a dynamic and complex
environment. The key goal in developing a malaria surveillance system is to ensure that it is robust, systematic,
and effective for improving data availability for decision-making. We present a unified framework for
envisioning malaria surveillance informatics as an ontology-based feedback system. The framework presented
is a solution for the current fragmented and linear surveillance processes for malaria case management. It
encapsulates a comprehensive natural language enumeration of the requirements of the cyberenvironment,
structured into 5 dimensions - timing, surveillance process, information surveyed, malaria management, and
stakeholder, with each of them articulated as a taxonomy of its constituent elements. The elements are
combined to form natural language statements of the cyberenvironment requirement. The information
generation through the semiotic cycle provides real-time sense and response capability for timely and targeted
interventions. The response mechanism creates both positively and negatively reinforcing feedback-based
learning processes at multiple levels. Such a system enables data interoperability for capturing malaria
incidence, discover epidemiological clusters, and predict propagation dynamics. On a larger scale, the
integrative framework enables data harmonization, analytics, and visualization towards effective management
and knowledge generation on disease surveillance.
1 INTRODUCTION
Malaria is a major global health problem, and a
leading cause of disease and death across many
tropical and subtropical countries. The World Malaria
Report 2018 estimates that there were 219 million
malaria cases, and 435 thousand related deaths in
2017 (World Health Organization, 2018). Global
efforts to control malaria during 2000-2015 had
resulted in substantial reductions in cases of malaria
incidence and mortality rate (Cibulskis et al., 2016).
However, the public health burden of the disease has
continued through new manifestations of its source
from Plasmodium Falciparum to P. Vivax, emergence
of new transmission modes due to human mobility,
and epidemiological shifts in the populations most at
risk of malaria (Cotter et al., 2013). Adding to the
burden are issues of low malaria testing rates and high
a
https://orcid.org/0000-0001-7308-6851
b
https://orcid.org/0000-0001-5784-5950
c
https://orcid.org/0000-0003-1551-6854
numbers of unconfirmed malaria cases that need
constant monitoring to sustain the reductions
(McMorrow, Aidoo, & Kachur, 2011). These new
circumstances have prompted shifts in global
strategies from traditional control interventions to
novel measures for completeness, timeliness of
activities to seek out infections, and interrupt
transmissions (Cotter et al., 2013).
Constant malaria monitoring and surveillance
systems have been highlighted as critical and a core
intervention strategy to achieve malaria elimination
(The malERA Consultative Group on Monitoring,
Evaluation, and Surveillance, 2011). Malaria
surveillance in the current elimination phase is
directed at stopping local transmission of malaria. It
involves a dual strategy of a) identifying and
determining areas with local transmission and its
sources; and b) detecting the characteristics of
Sreeganga, S., Mitra, S. and Ramaprasad, A.
Architecture of a Learning Surveillance System for Malaria Elimination in India.
DOI: 10.5220/0008944103770382
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 377-382
ISBN: 978-989-758-398-8; ISSN: 2184-4305
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
377
transmission with intensified surveillance and
appropriate control measures taken (Cibulskis, 2012).
WHO 2018 report emphasizes that a strong
surveillance system requires high levels of access to
care and case detection, and complete reporting of
health information by all sectors, whether public or
private (World Hea Organization, 2018). This has led
to a concerted global effort towards large-scale,
robust surveillance mechanisms that measure rather
than estimate the actual burden of malaria over time
from large areas of the continent (Nkumama,
O’Meara, & Osier, 2017). Aiding such efforts are
technological advances in surveillance systems,
including timeliness for rapid detection of local
outbreaks (Chehab M. et al., 2018), interoperability
for rapid data exchange between different platforms,
(Brenas, Al-Manir, Baker, & Shaban-Nejad, 2017)
and accessible data storage with management (UCSF
Global Health Sciences, 2014).
However, the need to communicate this change in
strategic thinking to large, cumbersome health
systems has proven a challenge (Cao et al., 2014).
Malaria represents complex and dynamic interactions
among ecosystems, livelihoods, and health systems
(Mboera, Mfinanga, Karimuribo, Rumisha, &
Sindato, 2014). In case of malaria surveillance, there
is a strong argument for designing data reservoir
platforms that facilitate monitoring, learning and
evaluation (MLE) among multiple actors to optimize
coordinated, integrated disease detection, and
intervention efforts (Barclay, Smith, & Findeis,
2012). Current surveillance systems remain
predominantly linear and ineffective in tackling the
complexities of malaria elimination. This is evident
from a recent empirical study evaluating malaria
surveillance in 16 countries during 2015-2017. It
highlighted the main systemic gaps as lack of
coverage in remote communities and inadequate
health information architecture to capture high
quality case-based data. Other shortcomings were
partial integration of intervention information, poor
visualization of generated information, and disjointed
data for making programmatic decisions (Lourenço et
al., 2019).
To bring in systematic and effective data
generation, integration, and dissemination for malaria
elimination, we argue for a Learning Surveillance
System. This is in line with the Learning Health
Systems described as ‘cyber-social ecosystem’, to
solve complex interdisciplinary problems of timely
evidence, and supporting best care practices (Lessard
et al., 2019). It requires explicit conceptualization
using tools that can be used to establish a
communication protocol, describe information flows
within a complex setting of malaria elimination,
facilitate analysis, and design of a learning
surveillance system. Our paper introduces a
promising first step to help address this challenge by
demonstrating how an ontological approach can
facilitate analysis and design of a learning
surveillance system for malaria.
2 ONTOLOGICAL FRAMEWORK
The ontology encapsulates a comprehensive natural
language enumeration of the requirements of the
cyberenvironment for malaria surveillance using a
structured terminology that can be used to
systematically analyze and prioritize the functions of
the cyberenvironment (Brooks & Ramaprasad, 2008).
It builds on an earlier work titled ‘Ontology of a
Cyberenvironment for Malaria Surveillance’(Brooks
& Ramaprasad, 2008), by adding more elements to
the framework to reflect the current complex and
dynamic malaria landscape. Making use of this
ontological framework one can move from the
traditional linear surveillance system to a nonlinear
responsive, and iterative surveillance system for
malaria elimination programmes across the world.
The malaria surveillance ontological framework
is shown in Figure 1. The object of the framework is
to create an architecture of socio-cyberenvironment
for a learning surveillance system for malaria
elimination. The ontology comprises of five
dimensions, with each being defined by a taxonomy
of elements that constitute a learning surveillance
system. The framework encapsulates pathways for
the management of a malaria surveillance system.
Each pathway is a concatenation of an element from
the five columns, left to right, with the adjacent
words/phrases.
Malaria surveillance is based on various types of
data used for managing malaria by different
stakeholders, as listed in third to fifth columns of the
framework. The third column represents various
types of surveillance data integral to malaria
elimination. It includes data on mosquito species
characteristics, their habitation, regional population
demographics, case incidence rates which form the
information base for health analytics (Merkord et al.,
2017).
The fourth column from the left denotes malaria
management that focuses on managing symptoms,
causes, treatment, and prevention of malaria. This
includes targeted interventions for malaria control
through measures such as rapid diagnostic tests,
insecticide treated mosquito nets, mosquito control,
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378
Figure 1: Ontology of a Cyberenvironment for Malaria Surveillance in India.
and strategic management (Dhamnetiya & Sahu,
2015; van Eijk et al., 2016). Effective surveillance of
malaria cases and deaths is essential for identifying
the areas or population groups that are most affected
by malaria, and for targeting resources for maximum
impact. The fifth column from the left provides the
multitude of stakeholders including the healthcare
sector, different external entities, and individuals
engaged in malarial control ecosystem. Considering
the columns of data, malaria management, and
stakeholder along with each of its elements the
framework forms a natural language sentence.
Combining the three columns will create pathways
from simple information service to complex
interaction service. For example: clinical data for
active malaria cases by a clinic; or, financial data for
stock of drugs by a pharmacy.
The surveillance tools and strategies are
themselves characterized by timing and stages, listed
in the first two columns of the framework (from the
left). The second column of the framework articulates
the full scope of a surveillance system that allows
sense and response, making it a learning surveillance
system. Case identification, detection, and data
collection are direct and cover both active and passive
cases at health facilities, communities during
investigation, and screening activities. A malaria
elimination information system should include
automated data analysis to ensure timely outputs,
expert analysis for policy, and programming
decisions. The data generated as output results will
enable identification of threats such as outbreaks and
inform responses. The impact of the response can be
captured by the surveillance system and can inform
further iterative changes to the interventions. Expert
analysis of the data and its interpretation will impact
different combinations of interventions. The whole
cycle will automatically generate outputs tailored to
the level of the health systems including
visualizations of analyzed data, work task lists,
reports for internal use, and by external organizations.
A feedback system occurs on a real-time basis.
The first column represents the temporal
dimension of the iterative process that increases the
quality and reliability of the data. It can range from
the ad-hoc through real-time to predictive. Thus, the
learning surveillance system encapsulates both
immediate, and predictive indicators for malaria
elimination.
Tracing the ontology, the pathways of a malaria
surveillance system read as:
Real-time collection of clinical data for active
malaria cases by ASHA worker.
Predictive analysis of socio-economic data for
mosquito vector control by private agency.
On-demand feedback of epidemiological data for
Timing Surveillance Data MalariaManagemment Stakeholder
Adhoc Identifi cation Entomologi cal Medicaltreatment HealthcareProvider
Posthoc Detection Parasitological Activemalariacases People
Ondemand Collection Demographic Asymptomaticcases Phy sician
Periodic Analysis Socioeconomic Prophylaxis Nurse
Realtime Interpretation Clinical Stock ofdrugs Pharmacist
Predictive Application Epidemiological Fakedrugs ASHAworke r
Reporting Ecological Drugresistance Volunteer
Feedback Climate Pe rsonal Protection Entity
Geographical Insecticidetreatedbednets Hospital
Financial Interiorresidential spraying Cl inic
Insecticidetreatedclothing Pharmacy
Housedesign Home
Physicalbarriers Citizen
Mosqui to(vector)control Indiv idual
Mosqui tosource control Family
Adultmosquitocontrol Community
Identificationofnewspeci es Agency
Strategicmanage me nt Publ ic
Resourceallocation Private
Training NGO
Outcomeassessment Academia
[of]
[datafor]
[+ ]
[by/for]
Architecture of a Learning Surveillance System for Malaria Elimination in India
379
strategic management by public agency.
3 DISCUSSION
The ontological framework described above provides
the architecture of an integrated and consistent
knowledge source for malaria elimination. Through
its frame, one can collect, formalize, integrate,
analyze, and manipulate all types of malaria-related
data. The pathways formed capture the relationships
between timing, surveillance, data, malaria
management, and stakeholders of the malarial
system.
Its usefulness as a comprehensive and readily
searchable knowledge repository is evident, when
applied to recent studies within the domain. For
instance, the ontology clearly encapsulates the key
focus areas for malaria surveillance, as emphasized
by Barclay et al. (2012). The first urgent need for
rapid detection of existing, new or re-introduced
infections is clearly reflected by the ontological
pathway of real-time/on-demand detection of
parasitological, entomological, epidemiological, and
demographic data. The authors also highlight the
need for identification of periods of low transmission
(e.g., from symptomatic and asymptomatic
infections) when the parasite population could be
most amenable to elimination as well as trends in
malaria incidence, and prevalence in different age
groups, increasing parasite heterogeneity, changes in
seasonality. This complex phenomenon is indicated
through the ontological pathways of identification,
detection, and collection of data on asymptomatic
cases and analysis of data collected on
parasitological, demographic, and climate data
respectively. Even their emphasis on the issue of
detection of resistance currently central to malaria
elimination is captured by ontological pathway of
analysis of clinical data on drug resistance. Hence, the
ontology, in conjunction with regular medical
standards, can construct a semantically intelligent
malaria decision-making system.
The ontology is also a useful domain link for the
feedback loop in surveillance systems, which in
recent years, has become essential to Learning Health
Care Systems (Kass & Faden, 2018). The usefulness
of the feedback model was evident in the enhanced
malaria surveillance programme undertaken by the
Zambian government that recorded success in Lusaka
District within two years. It replaced intermittent
population-based surveys with data generation from
continuously operating health information systems.
The learning from the feedback helped redesign the
strategies for malaria elimination as it significantly
reduced malaria reporting, and unnecessary anti-
malarial treatment administration, especially in areas
with variable malaria transmission patterns (Chisha et
al., 2015).
The key difference between existing surveillance
systems and the proposed one is the real-time learning
capability through the feedback-loop. The iterative
cycle inherent to the feedback-loop is transformed
into an advanced learning model that can guide the
system along the trajectory of malaria elimination.
The timely feedback system will help recognize,
amplify the effective actions for malaria elimination,
and similarly attenuate the ineffective ones. The
learning from the feedback will help redesign the
strategies for malaria elimination. This learning
capability can be deployed to develop: (a) positively
reinforcing feedback cycles that amplify and
accelerate the desirable outcomes, and (b) negatively
reinforcing cycles that attenuate and decelerate
undesirable outcomes. Further this combination of
positively and negatively reinforcing feedback cycles
will generate a desirable non-linear, positive impact
on malaria elimination, that will not only verify trends
in reported malaria, but also incorporate a data
feedback loop to improve data uptake, use, and
quality. The semiotic cycle will lead to predictive
capability, responsiveness to occurrence, and
propagation for pro-active management of malaria. It
facilitates the development of predictive systems for
malaria involving repeated updates on the initial
conditions based on the new epidemiological data,
and the inference method that naturally lends itself to
this purpose, given its time-sequential application
(Roy, Bouma, Dhiman, & Pascual, 2015). Such
precision will help improve both the efficiency and
effectiveness of the efforts to eliminate the disease. It
will also help foster the sharing of knowledge
generated and its application.
Malaria surveillance systems aim to assist public
health practitioners and decision makers to (a)
identify the regions or populations affected by
malaria; (b) identify trends in malaria morbidity and
mortality; and (c) evaluate preventive or therapeutic
malaria interventions, and programs (Brenas et al.,
2017). In combination with the latest technological
advancements, the feedback process based on this
ontological framework enables a multi-layered
approach to malaria surveillance system. It involves a
three-level feedback mechanism that creates a sense
and response capability at different levels, to enable a
quick feedback cycle facilitating a non-linear,
iterative process for malaria elimination. For a rapid
feedback on malaria occurrence, at the first level, the
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process involves a robust reporting system for
different stakeholders, including doctors, patients,
and care-providers. Epidemiological cluster data is a
second level data-based process that will enable
identifying malaria ‘hotspots’ including regional
variation of high and low endemic areas. Propagation
Dynamics is the third level of surveillance strategy
for the prevention of transmission of malaria; it will
focus on both temporal and spatial propagation of
malaria. The transmission parameters themselves are
also updated by refitting the model over a moving
window of time. Application of these approaches
enables the predictability of epidemic malaria at
different levels.
The ontology is amenable to a multi-
organizational setup inherent to malaria management.
It creates an appropriate interface with community-
based approaches, common in malaria management.
The global evidence on malaria management suggests
necessary preconditions to ensure the effectiveness of
community-based approaches. For instance, there is
an emphasis on community engagement at the
inception and planning stage rather than being mere
recipients (Whittaker & Smith, 2015). In the case of
India, where ASHA (Accredited Social Health
Activists) workers are engaged in management of the
disease at the local level, there is a strong pitch for
their empowerment. It has been pointed out that
communities should be empowered to regularly
monitor and evaluate the effectiveness of
interventions. Such practices through institutions, and
individuals further enhances the community's
participation and ownership. (Das et al., 2015). The
ontological framework for malaria surveillance
complements such community-based approaches: it
creates a learning driven process with a multi-layered
feedback mechanism that facilitates a ‘Sense and
Response’ system. In addition to detection, diagnosis,
and monitoring of the cases, it will increase the speed
by which regional and national surveillance teams are
alerted to local events and prepare intervention
services for local demands.
The near real-time technique provided by this
framework can be implemented within the scope of
existing infrastructure and human resources. It
leverages the current accelerated internet and
smartphone penetration across the world. Minimum
training only is required for a user as it’s easy to use.
For instance, through cloud computing platform
integrated with mobile apps, which facilitates access
to data in the cloud through smartphones, feature
phones, tablets and desktops is useful for various
stakeholders at various skill levels (El-Sappagh, Ali,
Hendawi, Jang, & Kwak, 2019; Quan, Hulth, Kok, &
Blumberg, 2014). Therefore, this approach is relevant
in the context of resource-constrained countries in
parts of Asia, Africa, and Latin America, with already
overburdened health systems.
4 CONCLUSIONS
This paper argues for a learning surveillance system
for malaria elimination and presents the ontological
framework to develop the system. It provides a
detailed, yet holistic understanding of how semiotic
interchanges will make the Learning Surveillance
System vision a reality. The ontology has policy
implications as governments around the world look to
improving the efficiency, and outcomes of their
surveillance organizations, and systems.
Understanding how learning system ontology can
support surveillance will contribute to funding
planning, and policies. It highlights the gaps related
to the type of digital technology currently leveraged
for surveillance systems. Healthcare providers will
find a supportive platform in this ontology as it is
likely to change their practice in future.
ACKNOWLEDGEMENTS
We thank Dr Chetan Singai for his valuable insights
on the paper.
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