The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination

*Arantxa Roca-Feltrer, Ann-Sophie Stratil and James K. Tibenderana*

#### **Abstract**

Adaptive surveillance systems are essential for national programmes to achieve their malaria elimination goals. Core principles of surveillance systems including accurate diagnosis and reporting of malaria cases, integration of health data across administrative levels and the need to link data to a response are well defined by international guidelines. Nevertheless, while the requirements of surveillance systems along the transmission continuum are clearly documented, the operationalization remains challenging for national programmes. Firstly, because the multi-level increase of surveillance efforts demanding real-time and case-based data as well as the capacity of the health force to trigger locally customized responses, is resource intensive and requires substantial investment. Secondly, because there is a gap in international alignment on best tools and practices on how to operationally implement these requirements. Recently, several initiatives have started to address this gap in international coordination, aiming to establish the operational guidance for elimination programmes to successfully implement adaptive surveillance systems.

**Keywords:** malaria elimination, adaptive surveillance, case investigation, digital systems, responsiveness

#### **1. Introduction**

The Global Technical Strategy for Malaria 2016–2030, published by the World Health Organization (WHO) in 2015, emphasizes surveillance as a core intervention for accelerating progress towards malaria elimination across endemic settings [1]. The successful and optimal implementation of other malaria interventions, including early detection and treatment and vector control measures, depend on the effectiveness of countries surveillance systems. Surveillance systems comprise all steps from the recording and reporting of data to data analysis and presentation, interpretation and evaluation and finally the dissemination and use of information [2]. The functioning of these systems depend on a competent and adequately resourced health force that adheres to surveillance requirements outlined national strategic plans, ensures high data quality throughout the system, and has the capacity to translate data into action while enabling rapid adaptations to respond to evolving contextual factors. Effective malaria surveillance systems allow national programmes to continuously monitor and evaluate the progress of their

programmes, detect and respond to outbreaks in time, and identify gaps in service delivery and intervention coverage in order to (re-)allocate resources in line with the programmatic needs.

We argue that the ability of surveillance systems to adapt to changing circumstances is a crucial element to resilient and effective systems that can support the achievement of national elimination targets. Changing circumstances may include changes to in-country transmission dynamics, e.g. moving from control to elimination settings or implementing context-specific surveillance approaches that respond to in-country heterogeneous malaria transmissions. These may also include external factors that may impact on malaria trends by altering specific malaria surveillance indicators (e.g. due to disruptions to access to care as observed during the COVID-19 pandemic) which require careful and ongoing interpretation of generated data in such rapidly changing environments. This has been shown to be an especially important factor during the COVID-19 pandemic where programmes are now required to understand the influence of COVID-19 on malaria indicators in order to correctly interpret observed malaria trends and take appropriate actions [3].

Despite the importance of strong surveillance systems, national programmes continue to experience challenges in the operational implementation of their surveillance systems particularly at the lowest levels. A landscaping exercise of surveillance systems from elimination countries in 2015–2016 reported that "surveillance systems in 2015–2016 [were] insufficient to support planning and implementing of targeted interventions and to measure progress towards malaria elimination" [4]. While progress has been made since then, countries continue to experience challenges in implementing adaptive surveillance systems.

In the following sections, we outline the importance of adaptive surveillance as a core intervention to malaria elimination. First, we describe the key elements of a functioning adaptive surveillance system capable of responding to all needs across the malaria transmission continuum. Then, we focus on the implications for surveillance system when transitioning from control to elimination settings. Lastly, we describe remaining challenges and rising opportunities in establishing resilient and effective surveillance system across countries to accelerate the elimination of malaria.

#### **2. What are the key elements of a functioning adaptive surveillance system capable of responding to all needs across the malaria transmission continuum?**

A malaria surveillance system is considered functional and responsive when it can produce evidence-based information (from quality data) that is routinely used for planning and decision making. To provide guidance on the principles and requirements for a strong malaria surveillance system, a reference manual was published by WHO in 2018 [2]. These principles are essential for guiding the establishment of a malaria surveillance system's core competencies, operationalizing surveillance systems in malaria endemic countries remains challenging. Outlines the following key principles of the design and establishment of malaria surveillance systems (**Table 1**).

It is worth noting that countries that have eliminated malaria—or have nearly done so—have documented significant improvements in their surveillance systems that made them possible to deploy a range of interventions and adapt them to needs over time. This ability to develop an adaptive responsive surveillance and information systems has been consistently reported as a key components of elimination programmes [5, 6]. Adaptive surveillance and information systems mean that

**73**

**Table 1.**

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination*

diagnostic tests (RDTs). 2 Integration All major components of a malaria surveillance system should be

surveillance.

of data.

transmission.

interventions.

6 Multisectorial approach In all transmission settings, a concerted effort must be made to

public health facilities.

Considered the foundation of a malaria surveillance system. Diagnoses should be made with either quality-assured malaria microscopy or rapid

National SOPs for surveillance should be based on a country's needs and on WHO recommendations and be able to address the heterogeneity of malaria within a country's boundaries. The Global Technical Strategy for Malaria 2016–2030 introduced the concept of a continuum, whereby progress towards malaria elimination is considered to be a continuous

integrated into broader health management information systems (HMIS). The HMIS system should, in turn, be responsive to the promptness and granularity of data required for effective malaria

Regardless of the malaria burden, front-line staff involved in the detection, recording and reporting of cases should also be the first users

Necessary investments in surveillance and system transition, including in human resources, should be made to respond to the anticipated reduction in disease burden and be alert after interruption of

All surveillance data must be linked to a decision at some level of the health system, even if the decision results in no immediate change in

include cases detected in other sectors (e.g. in private and other nongovernmental health care facilities), as well as those detected in

from innovation and advances in technology.

as a consequence of the results of surveillance

Like most other health interventions, surveillance is likely to benefit

Good understanding of the biology and behavioral ecology of vector species is essential for making programmatic decisions and monitoring and evaluating vector control interventions, including quality assurance.

Surveillance systems should be assessed routinely to ensure their accuracy, reliability, completeness, precision, timeliness and integrity. The assessment should also include the appropriateness of actions taken

process rather than a set of independent stages. [1]

they undergo through continuous improvement cycles that are triggered through the evolving country specific malaria epidemiological factors as well as changing external factors, ultimately leading to a resilient and robust system capable of responding to all malaria transmission contexts and reach malaria elimination. The next section details the specific implications of adaptive surveillance systems when

Only when health information systems (HIS) lead to public health decisionmaking, can surveillance systems be considered functional. For National Malaria Control Programmes, a good malaria information system (MIS) permits decisions on resource allocation to be done on and informed and unbiased way. A key step to understand MIS performance is to assess the quality of data, the use of information made by decision makers and the enabling organizational, technical and behavioral factors that influence MIS performance. System performance is measured through

transitioning across the continuum of malaria transmission intensities.

*Key principles of a malaria surveillance system (adapted from [2]).*

*DOI: http://dx.doi.org/10.5772/intechopen.96879*

1 Accurate parasitological diagnosis of a malaria

case

3 Responsiveness to country needs and country heterogeneity (malaria surveillance as a continuum)

4 Functional capacity to analyze, use and act

5 Surveillance data must be linked to a response

7 Enable innovations to accelerate efforts

8 Be able to track rapid changes of malaria epidemiological factors

9 Allow for continuous improvements

on data

**# Principles Description**


*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination DOI: http://dx.doi.org/10.5772/intechopen.96879*

#### **Table 1.**

*Current Topics and Emerging Issues in Malaria Elimination*

the programmatic needs.

programmes, detect and respond to outbreaks in time, and identify gaps in service delivery and intervention coverage in order to (re-)allocate resources in line with

We argue that the ability of surveillance systems to adapt to changing circumstances is a crucial element to resilient and effective systems that can support the achievement of national elimination targets. Changing circumstances may include changes to in-country transmission dynamics, e.g. moving from control to elimination settings or implementing context-specific surveillance approaches that respond to in-country heterogeneous malaria transmissions. These may also include external factors that may impact on malaria trends by altering specific malaria surveillance indicators (e.g. due to disruptions to access to care as observed during the COVID-19 pandemic) which require careful and ongoing interpretation of generated data in such rapidly changing environments. This has been shown to be an especially important factor during the COVID-19 pandemic where programmes are now required to understand the influence of COVID-19 on malaria indicators in order to

correctly interpret observed malaria trends and take appropriate actions [3].

lenges in implementing adaptive surveillance systems.

Despite the importance of strong surveillance systems, national programmes continue to experience challenges in the operational implementation of their surveillance systems particularly at the lowest levels. A landscaping exercise of surveillance systems from elimination countries in 2015–2016 reported that "surveillance systems in 2015–2016 [were] insufficient to support planning and implementing of targeted interventions and to measure progress towards malaria elimination" [4]. While progress has been made since then, countries continue to experience chal-

In the following sections, we outline the importance of adaptive surveillance as a core intervention to malaria elimination. First, we describe the key elements of a functioning adaptive surveillance system capable of responding to all needs across the malaria transmission continuum. Then, we focus on the implications for surveillance system when transitioning from control to elimination settings. Lastly, we describe remaining challenges and rising opportunities in establishing resilient and effective surveillance system across countries to accelerate the elimination

**2. What are the key elements of a functioning adaptive surveillance system capable of responding to all needs across the malaria** 

A malaria surveillance system is considered functional and responsive when it can produce evidence-based information (from quality data) that is routinely used for planning and decision making. To provide guidance on the principles and requirements for a strong malaria surveillance system, a reference manual was published by WHO in 2018 [2]. These principles are essential for guiding the establishment of a malaria surveillance system's core competencies, operationalizing surveillance systems in malaria endemic countries remains challenging. Outlines the following key principles of the design and establishment of malaria surveillance

It is worth noting that countries that have eliminated malaria—or have nearly done so—have documented significant improvements in their surveillance systems that made them possible to deploy a range of interventions and adapt them to needs over time. This ability to develop an adaptive responsive surveillance and information systems has been consistently reported as a key components of elimination programmes [5, 6]. Adaptive surveillance and information systems mean that

**72**

of malaria.

systems (**Table 1**).

**transmission continuum?**

*Key principles of a malaria surveillance system (adapted from [2]).*

they undergo through continuous improvement cycles that are triggered through the evolving country specific malaria epidemiological factors as well as changing external factors, ultimately leading to a resilient and robust system capable of responding to all malaria transmission contexts and reach malaria elimination. The next section details the specific implications of adaptive surveillance systems when transitioning across the continuum of malaria transmission intensities.

Only when health information systems (HIS) lead to public health decisionmaking, can surveillance systems be considered functional. For National Malaria Control Programmes, a good malaria information system (MIS) permits decisions on resource allocation to be done on and informed and unbiased way. A key step to understand MIS performance is to assess the quality of data, the use of information made by decision makers and the enabling organizational, technical and behavioral factors that influence MIS performance. System performance is measured through

the quality of data produced and the effective use that is given to information produced [7]. Data quality can be assessed through it composite indicators: completeness, timeliness and accuracy and should be conducted at different levels of the system (community, health facility and district). Use of information is assessed through the production, sharing and discussion of relevant reports that guide programmatic activity implementation. Technical, organizational and behavioral components should also be routinely assessed at all levels as they constitute an integral part of the surveillance system.

Establishing and institutionalizing a data demand and data use culture is one of the most challenging components to effectively operationalize at country level as it requires significant investments for resourcing long-term organizational capacity across all administrative levels to implement actionable responses using existing health information systems and based on the surveillance intelligence produced. The following sections outline the key adaptations, challenges and opportunities that malaria surveillance systems face to respond to all levels of malaria transmission in the move towards elimination.

#### **3. What are implications of transitioning from control to elimination?**

Delivering on the key principles outlined in the previous section can be challenging for national programmes transitioning towards elimination. Surveillance systems in elimination and control settings are designed to achieve their respective goals, i.e. control and elimination of malaria. While in control setting, the goal is to provide data insights to guide interventions at scale, e.g. mass net distributions, in elimination settings, the focus shifts to quickly trigger locally targeted responses to every single case. These goals require different granularity levels in terms of temporal and case (investigation) resolution. We use the term resolution here to describe the level of needed data granularity. While periodic reporting is sufficient in control settings, surveillance systems in elimination settings strive for real-time reporting to trigger immediate responses, i.e. they strive for high temporal resolution. Similarly, in control settings, caseload is usually investigated at higher administrative levels leading to a response at national, province or district level, while in elimination settings, every single case needs to be investigated to trigger targeted local responses, i.e. elimination settings require high case-level resolution [8, 9].

**Figure 1** depicts the levels of temporal and case resolution needed along the transmission continuum.

When transitioning from control to elimination settings, national programmes need to continuously adapt their surveillance systems in order to cater to these new requirements and trigger appropriate responses. As the term transmission continuum suggests, transitioning from one phase to the next happens gradually and in specific geographical areas within countries (hence increasing the heterogeneity of the malaria transmission in that country) [10]. It is therefore advisable to strengthen and adapt surveillance components gradually and timely to ensure they are functioning when the elimination phase is reached.

In elimination settings, notification of every very case within 24 h, to trigger timely case investigation (often within 3 days), enabling a timely investigation of transmission foci (often within 7 days) [2]. This case-based surveillance approach has been named the "1–3-7 approach" and was launched in China in 2012 and has since been adapted to other country contexts, including Cambodia and Myanmar [11–13]. This approach demands the redesign of reporting structures to deliver on these timely reporting and response requirements. A priority is ensuring quality data reporting from both public and private service. It is a known challenge to

**75**

by 2023 [18–20].

**Figure 1.**

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination*

successfully integrate data from the private sector into the national surveillance system [14]. To achieve this, there is an increasing need to establish digital data collection to ensure rapid and complete case reporting from all health system levels, including the private sector [9, 15]. Many countries have developed digitalized reporting systems. One example is Mozambique, a country with high transmission heterogeneity, where, since 2016, community health workers are equipped with smartphones carrying an app through which they can report every patient consultation – including malaria cases. This facilitates the notification of community cases

*Changes in surveillance system requirements along transmission continuum (adapted from [9]).*

Additional to this temporal component of early case notification and investigations, surveillance system need to increase the case resolution to inform customized and locally targeted responses. This includes case investigations, focus investigation and the development of related data collection tools. Programmes also need to expand their case detection approaches to not only detect symptomatic cases actively seeking care with health providers (passive case detection), but also detect asymptomatic cases and populations that might not be easily reached through existing health system structures (active case detection) [2]. Active case detection requires more investment from existing health staff to go out to test remote populations and/or new personnel who carry these additional responsibilities. Sri Lanka's elimination strategy developed a specific approach were active case detection was carried out by mobile malaria clinics targeting remote areas with no immediate access to alternative health system structures [17]. More recently, Cambodia, where remaining malaria cases cluster along international borders and hard-to-reach forests, followed this example with a similar approach. The national programme has introduced the role of mobile malaria workers who go on outreach activities and actively approach forest workers including mobile and migrant populations, the population group most at risk of malaria infection. This approach has proven to be successful, with Cambodia aiming for *P. falciparum* malaria elimination

Once high-resolution data is available, national programmes need to generate insights through data analysis [9]. Previously, this required input from expert data analysts limiting the extent to which national programme could actually produce the necessary outputs to guide decision making. More recently, analytical outputs

to higher administrative levels that carry out case investigations [16].

*DOI: http://dx.doi.org/10.5772/intechopen.96879*

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination DOI: http://dx.doi.org/10.5772/intechopen.96879*

#### **Figure 1.**

*Current Topics and Emerging Issues in Malaria Elimination*

integral part of the surveillance system.

sion in the move towards elimination.

transmission continuum.

are functioning when the elimination phase is reached.

the quality of data produced and the effective use that is given to information produced [7]. Data quality can be assessed through it composite indicators: completeness, timeliness and accuracy and should be conducted at different levels of the system (community, health facility and district). Use of information is assessed through the production, sharing and discussion of relevant reports that guide programmatic activity implementation. Technical, organizational and behavioral components should also be routinely assessed at all levels as they constitute an

Establishing and institutionalizing a data demand and data use culture is one of the most challenging components to effectively operationalize at country level as it requires significant investments for resourcing long-term organizational capacity across all administrative levels to implement actionable responses using existing health information systems and based on the surveillance intelligence produced. The following sections outline the key adaptations, challenges and opportunities that malaria surveillance systems face to respond to all levels of malaria transmis-

**3. What are implications of transitioning from control to elimination?**

Delivering on the key principles outlined in the previous section can be challenging for national programmes transitioning towards elimination. Surveillance systems in elimination and control settings are designed to achieve their respective goals, i.e. control and elimination of malaria. While in control setting, the goal is to provide data insights to guide interventions at scale, e.g. mass net distributions, in elimination settings, the focus shifts to quickly trigger locally targeted responses to every single case. These goals require different granularity levels in terms of temporal and case (investigation) resolution. We use the term resolution here to describe the level of needed data granularity. While periodic reporting is sufficient in control settings, surveillance systems in elimination settings strive for real-time reporting to trigger immediate responses, i.e. they strive for high temporal resolution. Similarly, in control settings, caseload is usually investigated at higher administrative levels leading to a response at national, province or district level, while in elimination settings, every single case needs to be investigated to trigger targeted local responses, i.e. elimination settings require high case-level resolution [8, 9]. **Figure 1** depicts the levels of temporal and case resolution needed along the

When transitioning from control to elimination settings, national programmes need to continuously adapt their surveillance systems in order to cater to these new requirements and trigger appropriate responses. As the term transmission continuum suggests, transitioning from one phase to the next happens gradually and in specific geographical areas within countries (hence increasing the heterogeneity of the malaria transmission in that country) [10]. It is therefore advisable to strengthen and adapt surveillance components gradually and timely to ensure they

In elimination settings, notification of every very case within 24 h, to trigger timely case investigation (often within 3 days), enabling a timely investigation of transmission foci (often within 7 days) [2]. This case-based surveillance approach has been named the "1–3-7 approach" and was launched in China in 2012 and has since been adapted to other country contexts, including Cambodia and Myanmar [11–13]. This approach demands the redesign of reporting structures to deliver on these timely reporting and response requirements. A priority is ensuring quality data reporting from both public and private service. It is a known challenge to

**74**

*Changes in surveillance system requirements along transmission continuum (adapted from [9]).*

successfully integrate data from the private sector into the national surveillance system [14]. To achieve this, there is an increasing need to establish digital data collection to ensure rapid and complete case reporting from all health system levels, including the private sector [9, 15]. Many countries have developed digitalized reporting systems. One example is Mozambique, a country with high transmission heterogeneity, where, since 2016, community health workers are equipped with smartphones carrying an app through which they can report every patient consultation – including malaria cases. This facilitates the notification of community cases to higher administrative levels that carry out case investigations [16].

Additional to this temporal component of early case notification and investigations, surveillance system need to increase the case resolution to inform customized and locally targeted responses. This includes case investigations, focus investigation and the development of related data collection tools. Programmes also need to expand their case detection approaches to not only detect symptomatic cases actively seeking care with health providers (passive case detection), but also detect asymptomatic cases and populations that might not be easily reached through existing health system structures (active case detection) [2]. Active case detection requires more investment from existing health staff to go out to test remote populations and/or new personnel who carry these additional responsibilities. Sri Lanka's elimination strategy developed a specific approach were active case detection was carried out by mobile malaria clinics targeting remote areas with no immediate access to alternative health system structures [17]. More recently, Cambodia, where remaining malaria cases cluster along international borders and hard-to-reach forests, followed this example with a similar approach. The national programme has introduced the role of mobile malaria workers who go on outreach activities and actively approach forest workers including mobile and migrant populations, the population group most at risk of malaria infection. This approach has proven to be successful, with Cambodia aiming for *P. falciparum* malaria elimination by 2023 [18–20].

Once high-resolution data is available, national programmes need to generate insights through data analysis [9]. Previously, this required input from expert data analysts limiting the extent to which national programme could actually produce the necessary outputs to guide decision making. More recently, analytical outputs

can be made available more easily through digitalized automated solutions. Data analysis in elimination setting should facilitate the identification of specific populations at risk, and should allow the micro-stratification of risk areas based on individual case data. Timely analysis of individual-level case data is crucial to improve early warning systems and establish robust alert systems to prevent epidemics and control foci. At this point, visualizing trends and risks via appropriate tools including charts and maps is essential.

Following data visualization and interpretation, the final and arguably most important step is linking data to action. The health force needs to be trained to interpret these high-resolution outputs and trigger targeted responses. Functioning communication flows between different administrative levels, decision-makers and other stakeholders are the enabling environment needed to link surveillance data to action. A case study mention "a well-coordinated surveillance system with excellent vertical and horizontal communication" as a key component that lead to the successful malaria elimination in Sri Lanka [17].

Lastly, it is critical that all these components are facilitated via an integrated platform with clear procedures for data recording, reporting, visualization and use. Systems should be built around central data storage and management system to ensure smooth integration of malaria indicators and data sources into routine HIS [9]. New digital solutions can facilitate this. The project Visualize No Malaria that has been implemented in Zambia since 2015 has exemplified an approach where a range of new digital tools facilitated linkages between exiting routine and non-routine database, automated data workflows, and create customized data visualizations to generate one integrated surveillance system [21]. Experiences from Cambodia outline the process and challenges of shifting from an offline information system towards an integrated web-based surveillance system for malaria elimination [22]. New digital solutions also help make surveillance systems more resilient to changes in external circumstances by allowing simple adaptations that were previously difficult to implement. External circumstances like the COVID-19 pandemic require rapid adaptations of surveillance systems [3, 23]. In a before mentioned project in Mozambique where community health workers submit data through a smartphone app, the underlying digital platform was adapted in response to the COVID-19 pandemic. Now, the app includes education modules related to COVID-19, and data entry forms for COVID-19 symptom tracking (some symptoms like fever overlap with malaria symptoms). This enabled the Ministry of Health to rapidly gather essential insights on COVID-19 knowledge gaps among community health workers and will strengthen malaria surveillance by providing insights into how community health workers handle the similarity of malaria and COVID-19 symptoms [24, 25].

These increased demands of the surveillance system require resources to create functioning data systems, procure appropriate testing (e.g. malaria species-sensitive tests that are able to differentiate between *P. falciparum* and *P. vivax* malaria to allow species-specific responses where required), develop new guidelines and build the capacity of the health force to fulfill these new/additional requirements. Due to these intensified focus on surveillance, the proportional spending on the surveillance component of malaria programmes increases and case studies show that programme expenses to achieve malaria elimination can even be greater than the expenses during the control phase, while international supporting funding of the health system decreases [26, 27]. This may put a specific strain on national programmes catering to the needs of multiple transmission strata at the same time. However, as surveillance systems become more resilient, resources and personnel can progressively shifts tasks and embed additional roles and responsibilities to optimize resources and expertise. Thailand for example has identified the need to integrate malaria services into the general health service system rather than

**77**

the future.

between their recent initiatives [32].

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination*

continuing to implement a vertical malaria programme. Building capacity and mobilizing resources at all health system levels will support the achievement of the

**4. What are the current gaps in surveillance systems of elimination** 

One of the remaining challenges for many eliminating countries is the limited capacity of their surveillance systems to monitor and track interventions against *Plasmodium vivax* and monitor vivax relapses. In countries were both *Plasmodium falciparum* and *Plasmodium vivax* are present, malaria interventions have led to steady declines of falciparum malaria, resulting in an increase of the relative contribution of vivax malaria (among all infections). As opposed to *P. falciparum* infection, which does not have latency (dormant stages), treatment of *P. vivax* malaria poses a particular challenge to elimination efforts due to its potential to cause periodic relapses [29, 30]. A fit for purpose surveillance system in these countries should not only be able to monitor the delivery of tailored *P. falciparum* interventions, but also track the implementation of approaches to safely deliver appropriate

While the requirements of surveillance systems along the transmission continuum are clearly documented through comprehensive guidance documents outlined in previous sections, the operationalization of this guidance remains challenging for national programmes. Even though many countries are successful in establishing functioning systems on the path to elimination, as shown in previous sections [6], they mostly rely on a learning by doing approach. There remains a gap in coordinating and standardizing efforts among the wider surveillance community. In December 2020, the Malaria Policy Advisory Committee within WHO has acknowledged this "lack of coordination and standardization of tools to monitor the quality of malaria surveillance and to understand its strengths and weaknesses" [31]. Recently, several initiatives have started to address this gap, aiming to establish the operational guidance and best practices for surveillance systems of malaria elimination programmes. The WHO has recently developed a surveillance assessment toolkit which will be published in the first quarter of 2021 [31]. This toolkit will frame a systematic approach on how to measure the performance of malaria surveillance systems which will harmonize the way national programmes assess the quality of their surveillance systems and identify needs for improvement The WHO has further announced the set-up of a Malaria Strategic Information Reference Group (MSIRG). The MSIRG will focus on five main areas: Guidance, digital solutions, surveillance assessments and system strengthening, programme reviews and subnational tailoring of interventions and burden estimation. The RBM Surveillance, Monitoring and Reference Group (SMERG) is a global platform to foster communication on malaria surveillance, monitoring and evaluation and convenes members to coordinate related activities. The SMERG is currently in the process of establishing a Committee dedicated to Surveillance Practice and Data Quality (SP&DQ Committee) with the key objectives to improve the visibility of surveillance and data quality improvement initiatives and to strengthen global coordination on surveillance-related efforts. The two groups (MSIRG and SMERG) will work closely together to ensure synergic efforts

These recent initiatives will lay the groundwork for more efficient international

alignment of surveillance efforts and improved harmonization of systems in

*DOI: http://dx.doi.org/10.5772/intechopen.96879*

country's malaria elimination goal by 2024 [28].

**countries and how to overcome?**

treatment to *P. vivax* cases.

continuing to implement a vertical malaria programme. Building capacity and mobilizing resources at all health system levels will support the achievement of the country's malaria elimination goal by 2024 [28].

#### **4. What are the current gaps in surveillance systems of elimination countries and how to overcome?**

One of the remaining challenges for many eliminating countries is the limited capacity of their surveillance systems to monitor and track interventions against *Plasmodium vivax* and monitor vivax relapses. In countries were both *Plasmodium falciparum* and *Plasmodium vivax* are present, malaria interventions have led to steady declines of falciparum malaria, resulting in an increase of the relative contribution of vivax malaria (among all infections). As opposed to *P. falciparum* infection, which does not have latency (dormant stages), treatment of *P. vivax* malaria poses a particular challenge to elimination efforts due to its potential to cause periodic relapses [29, 30]. A fit for purpose surveillance system in these countries should not only be able to monitor the delivery of tailored *P. falciparum* interventions, but also track the implementation of approaches to safely deliver appropriate treatment to *P. vivax* cases.

While the requirements of surveillance systems along the transmission continuum are clearly documented through comprehensive guidance documents outlined in previous sections, the operationalization of this guidance remains challenging for national programmes. Even though many countries are successful in establishing functioning systems on the path to elimination, as shown in previous sections [6], they mostly rely on a learning by doing approach. There remains a gap in coordinating and standardizing efforts among the wider surveillance community. In December 2020, the Malaria Policy Advisory Committee within WHO has acknowledged this "lack of coordination and standardization of tools to monitor the quality of malaria surveillance and to understand its strengths and weaknesses" [31]. Recently, several initiatives have started to address this gap, aiming to establish the operational guidance and best practices for surveillance systems of malaria elimination programmes. The WHO has recently developed a surveillance assessment toolkit which will be published in the first quarter of 2021 [31]. This toolkit will frame a systematic approach on how to measure the performance of malaria surveillance systems which will harmonize the way national programmes assess the quality of their surveillance systems and identify needs for improvement The WHO has further announced the set-up of a Malaria Strategic Information Reference Group (MSIRG). The MSIRG will focus on five main areas: Guidance, digital solutions, surveillance assessments and system strengthening, programme reviews and subnational tailoring of interventions and burden estimation. The RBM Surveillance, Monitoring and Reference Group (SMERG) is a global platform to foster communication on malaria surveillance, monitoring and evaluation and convenes members to coordinate related activities. The SMERG is currently in the process of establishing a Committee dedicated to Surveillance Practice and Data Quality (SP&DQ Committee) with the key objectives to improve the visibility of surveillance and data quality improvement initiatives and to strengthen global coordination on surveillance-related efforts. The two groups (MSIRG and SMERG) will work closely together to ensure synergic efforts between their recent initiatives [32].

These recent initiatives will lay the groundwork for more efficient international alignment of surveillance efforts and improved harmonization of systems in the future.

*Current Topics and Emerging Issues in Malaria Elimination*

successful malaria elimination in Sri Lanka [17].

ing charts and maps is essential.

can be made available more easily through digitalized automated solutions. Data analysis in elimination setting should facilitate the identification of specific populations at risk, and should allow the micro-stratification of risk areas based on individual case data. Timely analysis of individual-level case data is crucial to improve early warning systems and establish robust alert systems to prevent epidemics and control foci. At this point, visualizing trends and risks via appropriate tools includ-

Following data visualization and interpretation, the final and arguably most important step is linking data to action. The health force needs to be trained to interpret these high-resolution outputs and trigger targeted responses. Functioning communication flows between different administrative levels, decision-makers and other stakeholders are the enabling environment needed to link surveillance data to action. A case study mention "a well-coordinated surveillance system with excellent vertical and horizontal communication" as a key component that lead to the

Lastly, it is critical that all these components are facilitated via an integrated platform with clear procedures for data recording, reporting, visualization and use. Systems should be built around central data storage and management system to ensure smooth integration of malaria indicators and data sources into routine HIS [9]. New digital solutions can facilitate this. The project Visualize No Malaria that has been implemented in Zambia since 2015 has exemplified an approach where a range of new digital tools facilitated linkages between exiting routine and non-routine database, automated data workflows, and create customized data visualizations to generate one integrated surveillance system [21]. Experiences from Cambodia outline the process and challenges of shifting from an offline information system towards an integrated web-based surveillance system for malaria elimination [22]. New digital solutions also help make surveillance systems more resilient to changes in external circumstances by allowing simple adaptations that were previously difficult to implement. External circumstances like the COVID-19 pandemic require rapid adaptations of surveillance systems [3, 23]. In a before mentioned project in Mozambique where community health workers submit data through a smartphone app, the underlying digital platform was adapted in response to the COVID-19 pandemic. Now, the app includes education modules related to COVID-19, and data entry forms for COVID-19 symptom tracking (some symptoms like fever overlap with malaria symptoms). This enabled the Ministry of Health to rapidly gather essential insights on COVID-19 knowledge gaps among community health workers and will strengthen malaria surveillance by providing insights into how community health workers handle the similarity of malaria and COVID-19 symptoms [24, 25]. These increased demands of the surveillance system require resources to create functioning data systems, procure appropriate testing (e.g. malaria species-sensitive tests that are able to differentiate between *P. falciparum* and *P. vivax* malaria to allow species-specific responses where required), develop new guidelines and build the capacity of the health force to fulfill these new/additional requirements. Due to these intensified focus on surveillance, the proportional spending on the surveillance component of malaria programmes increases and case studies show that programme expenses to achieve malaria elimination can even be greater than the expenses during the control phase, while international supporting funding of the health system decreases [26, 27]. This may put a specific strain on national programmes catering to the needs of multiple transmission strata at the same time. However, as surveillance systems become more resilient, resources and personnel can progressively shifts tasks and embed additional roles and responsibilities to optimize resources and expertise. Thailand for example has identified the need to integrate malaria services into the general health service system rather than

**76**

## **5. Conclusions**

A strong surveillance system is critical to implementing an efficient and effective malaria program to monitor disease trends and optimize targeting of interventions to accelerate progress towards malaria elimination. For countries to strengthen and sustain the practice of using data for decision-making and accelerate efforts towards malaria elimination, dynamic and adaptive surveillance systems are needed. To accelerate efforts towards malaria elimination, surveillance systems must be able to respond to the dynamic and evolving external socio-ecological patterns (as recently experienced through the COVID-19 pandemic) guided by lessons learned on resilience and adaptive management in order to effectively operate at national and subnational levels. Several global initiatives to document these best practices are on the way and will provide international alignment of surveillance efforts and improved harmonization of systems in the future.

#### **Author details**

Arantxa Roca-Feltrer\*, Ann-Sophie Stratil and James K. Tibenderana Malaria Consortium, London, United Kingdom

\*Address all correspondence to: a.roca@malariaconsortium.org

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**79**

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination*

publications/tl-18-12/. [Accessed

[8] Bridges DJ, Winters AM, Hamer DH. Malaria elimination: surveillance and response. Pathog Glob Health.

[9] Ohrt C, Roberts KW, Sturrock HJW,

surveillance for malaria elimination. Am

Wegbreit J, Lee BY, Gosling RD. Information systems to support

J Trop Med Hyg. 2015;93:145-52.

[10] World Health Organization. A framework for malaria

[11] Kheang ST, Sovannaroth S,

Barat LM, Dysoley L, Kapella BK, Po L, et al. Malaria elimination using the 1-3-7 approach: lessons from Sampov Loun, Cambodia. BMC Public Health.

[12] Aung PP, Thein ZW, Hein ZNM, Aung KT, Mon NO, Linn NYY, et al. Challenges in early phase of implementing the 1-3-7 surveillance and response approach in malaria elimination setting: A field study from Myanmar. Infect Dis Poverty. 2020;9:18.

[13] Lu G, Liu Y, Beiersmann C, Feng Y, Cao J, Muller O. Challenges in and lessons learned during the implementation of the 1-3-7 malaria surveillance and response strategy in China: a qualitative study. Infect Dis

[14] Mercado CE, Ekapirat N,

[15] Bennett A, Avancena ALV, Wegbreit J, Cotter C, Roberts K, Gosling R. Engaging the private sector in malaria surveillance: a review of

Dondorp AM, Maude RJ. An assessment of national surveillance systems for malaria elimination in the Asia Pacific.

Poverty. 2016;5:94.

Malar J. 2017;16:127.

27 January 2021].

2012;106:224-31.

elimination. 2017.

2020;20:544.

*DOI: http://dx.doi.org/10.5772/intechopen.96879*

[1] World Health Organization. Global Technical Strategy for Malaria 2016-

[3] RBM Partnership to End Malaria - Surveillance Monitoring and Evaluation Reference Group. Monitoring and evaluation of malaria-related routine data during the COVID-19 pandemic

[2] World Health Organization. Malaria Surveillance, Monitoring and Evaluation: A Reference Manual. 2018.

2020. Available from: https:// endmalaria.org/sites/default/files/ Monitoring%20and%20evaluation%20 of%20malaria-related%20routine%20

data%20during%20the%20

COVID-19%20pandemic\_August2020. pdf. [Accessed 6 December 2020].

[4] Lourenco C, Tatem AJ, Atkinson PM, Cohen JM, Pindolia D, Bhavnani D, et al. Strengthening surveillance

systems for malaria elimination: a global landscaping of system performance, 2015-2017. Malar J. 2019;18:315.

[5] World Health Organization, Roll Back Malaria Partnership, PATH. Eliminating malaria: learning from the past, looking ahead (Progress & impact series, n. 8) ISBN:

9789241502504. 2011. Available from: https://apps.who.int/iris/bitstream/ handle/10665/79937/9789241502504\_ eng.pdf;jsessionid=92E5273ADCB33F EB4CB841E0790F0EE1?sequence=1.

[Accessed 21 January 2021].

[6] World Health Organization.

of Routine Information System

Malaria - Elimination case studies 2012. Available from: https://www.who.int/ malaria/areas/elimination/casestudies/ en/. [Accessed 27 January 2021].

[7] MEASURE Evaluation. Performance

Management (PRISM) Toolkit: PRISM Tools 2019. Available from: https:// www.measureevaluation.org/resources/

**References**

2030. 2015.

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination DOI: http://dx.doi.org/10.5772/intechopen.96879*

#### **References**

*Current Topics and Emerging Issues in Malaria Elimination*

efforts and improved harmonization of systems in the future.

Arantxa Roca-Feltrer\*, Ann-Sophie Stratil and James K. Tibenderana

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: a.roca@malariaconsortium.org

Malaria Consortium, London, United Kingdom

provided the original work is properly cited.

A strong surveillance system is critical to implementing an efficient and effective malaria program to monitor disease trends and optimize targeting of interventions to accelerate progress towards malaria elimination. For countries to strengthen and sustain the practice of using data for decision-making and accelerate efforts towards malaria elimination, dynamic and adaptive surveillance systems are needed. To accelerate efforts towards malaria elimination, surveillance systems must be able to respond to the dynamic and evolving external socio-ecological patterns (as recently experienced through the COVID-19 pandemic) guided by lessons learned on resilience and adaptive management in order to effectively operate at national and subnational levels. Several global initiatives to document these best practices are on the way and will provide international alignment of surveillance

**5. Conclusions**

**78**

**Author details**

[1] World Health Organization. Global Technical Strategy for Malaria 2016- 2030. 2015.

[2] World Health Organization. Malaria Surveillance, Monitoring and Evaluation: A Reference Manual. 2018.

[3] RBM Partnership to End Malaria - Surveillance Monitoring and Evaluation Reference Group. Monitoring and evaluation of malaria-related routine data during the COVID-19 pandemic 2020. Available from: https:// endmalaria.org/sites/default/files/ Monitoring%20and%20evaluation%20 of%20malaria-related%20routine%20 data%20during%20the%20 COVID-19%20pandemic\_August2020. pdf. [Accessed 6 December 2020].

[4] Lourenco C, Tatem AJ, Atkinson PM, Cohen JM, Pindolia D, Bhavnani D, et al. Strengthening surveillance systems for malaria elimination: a global landscaping of system performance, 2015-2017. Malar J. 2019;18:315.

[5] World Health Organization, Roll Back Malaria Partnership, PATH. Eliminating malaria: learning from the past, looking ahead (Progress & impact series, n. 8) ISBN: 9789241502504. 2011. Available from: https://apps.who.int/iris/bitstream/ handle/10665/79937/9789241502504\_ eng.pdf;jsessionid=92E5273ADCB33F EB4CB841E0790F0EE1?sequence=1. [Accessed 21 January 2021].

[6] World Health Organization. Malaria - Elimination case studies 2012. Available from: https://www.who.int/ malaria/areas/elimination/casestudies/ en/. [Accessed 27 January 2021].

[7] MEASURE Evaluation. Performance of Routine Information System Management (PRISM) Toolkit: PRISM Tools 2019. Available from: https:// www.measureevaluation.org/resources/ publications/tl-18-12/. [Accessed 27 January 2021].

[8] Bridges DJ, Winters AM, Hamer DH. Malaria elimination: surveillance and response. Pathog Glob Health. 2012;106:224-31.

[9] Ohrt C, Roberts KW, Sturrock HJW, Wegbreit J, Lee BY, Gosling RD. Information systems to support surveillance for malaria elimination. Am J Trop Med Hyg. 2015;93:145-52.

[10] World Health Organization. A framework for malaria elimination. 2017.

[11] Kheang ST, Sovannaroth S, Barat LM, Dysoley L, Kapella BK, Po L, et al. Malaria elimination using the 1-3-7 approach: lessons from Sampov Loun, Cambodia. BMC Public Health. 2020;20:544.

[12] Aung PP, Thein ZW, Hein ZNM, Aung KT, Mon NO, Linn NYY, et al. Challenges in early phase of implementing the 1-3-7 surveillance and response approach in malaria elimination setting: A field study from Myanmar. Infect Dis Poverty. 2020;9:18.

[13] Lu G, Liu Y, Beiersmann C, Feng Y, Cao J, Muller O. Challenges in and lessons learned during the implementation of the 1-3-7 malaria surveillance and response strategy in China: a qualitative study. Infect Dis Poverty. 2016;5:94.

[14] Mercado CE, Ekapirat N, Dondorp AM, Maude RJ. An assessment of national surveillance systems for malaria elimination in the Asia Pacific. Malar J. 2017;16:127.

[15] Bennett A, Avancena ALV, Wegbreit J, Cotter C, Roberts K, Gosling R. Engaging the private sector in malaria surveillance: a review of

strategies and recommendations for elimination settings. Malar J. 2017;16:252.

[16] Malaria Consortium. upSCALE: Strengthening mobile health in Mozambique 2019. Available from: https://www.malariaconsortium. org/resources/publications/1287/ upscale-strengthening-mobilehealth-in-mozambique. [Accessed 21 January 2021].

[17] World Health Organization. Malaria-Free Sri Lanka 2016. Available from: http://origin.searo.who.int/ srilanka/areas/malaria/malaria-free-srilanka.pdf. [Accessed 2020-12-07].

[18] Kingdom of Cambodia. Cambodia Malaria Elimination Action Framework 2021-2025. 2020.

[19] Lek D, Callery JJ, Nguon C, Debackere M, Sovannaroth S, Tripura R, et al. Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report. Malar J. 2020;19:151.

[20] Stratil AS, Vernaeve L, Lopes S, Bourny Y, Mannion K, Hamade P, et al. Eliminating *Plasmodium falciparum* malaria: results from tailoring active case detection approaches to remote populations in forested border areas in north-eastern Cambodia. Malar J. 2021;20:108.

[21] Health Data Science Exchange. Data analytics for malaria elimination in Zambia 2015. Available from: https:// datascienceforhealth.org/visualize-nomalaria. [Accessed 21 January 2021].

[22] Fu C, Lopes S, Mellor S, Aryal S, Sovannaroth S, Roca-Feltrer A. Experiences From Developing and Upgrading a Web-Based Surveillance System for Malaria Elimination in Cambodia. JMIR Public Health Surveill. 2017;3:e30.

[23] World Health Organization. Tailoring malaria interventions in the COVID-19 response. 2020.

[24] Feldman M, Krylova VL, Farrow P, Donovan L, Zandamela E, Rebelo J, et al. Community health worker knowledge, attitudes and practices towards COVID-19: Learnings from an online cross-sectional survey using a digital health platform, UpSCALE, in Mozambique 2020. Available from: https://www.malariaconsortium. org/resources/publications/1401/ community-health-worker-knowledgeattitudes-and-practices-towards-covid-19-learnings-from-an-online-crosssectional-survey-using-a-digital-healthplatform-upscale-in-mozambique. [Accessed 21 January 2021].

[25] Kevin Baker. Rapid support of the COVID-19 response at community level in Mozambique through adapting the existing upSCALE digital health platform and telemedicine. 2020 Global Digital Health Forum; 7 December 2020; 2020. Available from: https:// www.malariaconsortium.org/resources/ publications/1406/rapid-support-ofthe-covid-19-response-at-communitylevel-in-mozambique-throughadapting-the-existing-upscale-digitalhealth-platform-and-telemedicine. [Accessed:

[26] Haakenstad A, Harle AC, Tsakalos G, Micah AE, Tao T, Anjomshoa M, et al. Tracking spending on malaria by source in 106 countries, 2000-16: an economic modelling study. Lancet Infect Dis. 2019;19:703-16.

[27] Sabot O, Cohen JM, Hsiang MS, Kahn JG, Basu S, Tang L, et al. Costs and financial feasibility of malaria elimination. Lancet. 2010;376:1604-15.

[28] Ministry of Public Health Thailand. Guide to Malaria Elimination For Thailand's Local Administrative Organizations and the Health

**81**

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination*

*DOI: http://dx.doi.org/10.5772/intechopen.96879*

Network 2019. Available from: http://malaria.ddc.moph.go.th/ downloadfiles/Guide%20to%20 Malaria%20Elimination%20for%20 Thailand%20LAO\_EN.pdf. [Accessed

[29] Baird JK, Valecha N, Duparc S, White NJ, Price RN. Diagnosis and Treatment of *Plasmodium vivax* Malaria. Am J Trop Med Hyg. 2016;95:35-51.

[30] Olliaro PL, Barnwell JW, Barry A, Mendis K, Mueller I, Reeder JC, et al. Implications of *Plasmodium vivax* Biology for Control, Elimination, and Research. Am J Trop Med Hyg.

[31] World Health Organization. Overview of the Malaria Surveillance Toolkit 2020. Available from: https:// www.who.int/docs/default-source/ malaria/mpac-documentation/mpacdecember2020-session1-surveillancetoolkit-update.pdf?sfvrsn=f3091836\_9.

[Accessed 21 January 2021].

[32] RBM Partnership to End Malaria - Surveillance Monitoring and Evaluation Reference Group. Informative session on SP&DQ Committee. 27 January 2021;

21 January 2021].

2016;95:4-14.

Virtual.

*The Role of Adaptive Surveillance as a Core Intervention to Achieve Malaria Elimination DOI: http://dx.doi.org/10.5772/intechopen.96879*

Network 2019. Available from: http://malaria.ddc.moph.go.th/ downloadfiles/Guide%20to%20 Malaria%20Elimination%20for%20 Thailand%20LAO\_EN.pdf. [Accessed 21 January 2021].

*Current Topics and Emerging Issues in Malaria Elimination*

[23] World Health Organization. Tailoring malaria interventions in the

[24] Feldman M, Krylova VL, Farrow P, Donovan L, Zandamela E, Rebelo J, et al. Community health worker knowledge, attitudes and practices towards COVID-19: Learnings from an online cross-sectional survey using a digital health platform, UpSCALE, in Mozambique 2020. Available from: https://www.malariaconsortium. org/resources/publications/1401/ community-health-worker-knowledgeattitudes-and-practices-towards-covid-19-learnings-from-an-online-crosssectional-survey-using-a-digital-healthplatform-upscale-in-mozambique.

COVID-19 response. 2020.

[Accessed 21 January 2021].

[26] Haakenstad A, Harle AC, Tsakalos G, Micah AE, Tao T,

Anjomshoa M, et al. Tracking spending on malaria by source in 106 countries, 2000-16: an economic modelling study. Lancet Infect Dis. 2019;19:703-16.

[27] Sabot O, Cohen JM, Hsiang MS, Kahn JG, Basu S, Tang L, et al. Costs and financial feasibility of malaria elimination. Lancet. 2010;376:1604-15.

[28] Ministry of Public Health Thailand. Guide to Malaria Elimination For Thailand's Local Administrative Organizations and the Health

[Accessed:

[25] Kevin Baker. Rapid support of the COVID-19 response at community level in Mozambique through adapting the existing upSCALE digital health platform and telemedicine. 2020 Global Digital Health Forum; 7 December 2020; 2020. Available from: https:// www.malariaconsortium.org/resources/ publications/1406/rapid-support-ofthe-covid-19-response-at-communitylevel-in-mozambique-throughadapting-the-existing-upscale-digitalhealth-platform-and-telemedicine.

strategies and recommendations for elimination settings. Malar J.

[16] Malaria Consortium. upSCALE: Strengthening mobile health in Mozambique 2019. Available from: https://www.malariaconsortium. org/resources/publications/1287/ upscale-strengthening-mobilehealth-in-mozambique. [Accessed

[17] World Health Organization. Malaria-Free Sri Lanka 2016. Available from: http://origin.searo.who.int/ srilanka/areas/malaria/malaria-free-srilanka.pdf. [Accessed 2020-12-07].

[18] Kingdom of Cambodia. Cambodia Malaria Elimination Action Framework

[19] Lek D, Callery JJ, Nguon C, Debackere M, Sovannaroth S, Tripura R, et al. Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report. Malar J.

[20] Stratil AS, Vernaeve L, Lopes S, Bourny Y, Mannion K, Hamade P, et al. Eliminating *Plasmodium falciparum* malaria: results from tailoring active case detection approaches to remote populations in forested border areas in north-eastern Cambodia. Malar J.

[21] Health Data Science Exchange. Data analytics for malaria elimination in Zambia 2015. Available from: https:// datascienceforhealth.org/visualize-nomalaria. [Accessed 21 January 2021].

[22] Fu C, Lopes S, Mellor S, Aryal S, Sovannaroth S, Roca-Feltrer A. Experiences From Developing and Upgrading a Web-Based Surveillance System for Malaria Elimination in Cambodia. JMIR Public Health Surveill.

2017;16:252.

21 January 2021].

2021-2025. 2020.

2020;19:151.

2021;20:108.

**80**

2017;3:e30.

[29] Baird JK, Valecha N, Duparc S, White NJ, Price RN. Diagnosis and Treatment of *Plasmodium vivax* Malaria. Am J Trop Med Hyg. 2016;95:35-51.

[30] Olliaro PL, Barnwell JW, Barry A, Mendis K, Mueller I, Reeder JC, et al. Implications of *Plasmodium vivax* Biology for Control, Elimination, and Research. Am J Trop Med Hyg. 2016;95:4-14.

[31] World Health Organization. Overview of the Malaria Surveillance Toolkit 2020. Available from: https:// www.who.int/docs/default-source/ malaria/mpac-documentation/mpacdecember2020-session1-surveillancetoolkit-update.pdf?sfvrsn=f3091836\_9. [Accessed 21 January 2021].

[32] RBM Partnership to End Malaria - Surveillance Monitoring and Evaluation Reference Group. Informative session on SP&DQ Committee. 27 January 2021; Virtual.

**83**

**Chapter 6**

**Abstract**

in Africa.

**1. Introduction**

Increased Trends of *P. vivax* in

Mean for Malaria Elimination?

*Kolapo Oyebola and Alfred Amambua-Ngwa*

Sub-Saharan Africa: What Does it

*Mary Aigbiremo Oboh, Mamadou Ndiath, Olumide Ajibola,* 

*Plasmodium vivax* being the most geographically spread *Plasmodium* species is considered sparsely distributed in sub-Saharan Africa (sSA) while *P. falciparum* is the most prevalent species in this region. Thus, control strategies in sSA have been disproportionately targeted towards falciparum malaria. Nevertheless, with the use of more sensitive malaria diagnostic platforms, there are more reports of *P. vivax* and other non-falciparum malaria in sSA. In addition, *P. vivax* is presumed benign, however there are new findings of severe cases recorded from *P. vivax* single or mixed infection with other *Plasmodium* species. Besides, the extended dormant period (lasting for weeks or months) is a challenge for achieving effective cure for vivax infections. Although, chloroquine has been proscribed for treatment *P. falciparum*, it still remains the drug of choice for *P. vivax* in most Asian countries where it is predominant. In sSA, artemisinin combination-based therapies (ACTs) are used for treatment of falciparum malaria and, it is probable that the use of ACT could be enhancing adaptive selection for *P. vivax* in the face of its increasing prevalence in the population. Hence, understanding epidemiological and biological factors, and data that could be contributing to the observed steady increase in *P. vivax* prevalence in sSA is important. In this chapter, we discuss the mechanisms for invasion of red blood cells, trends in increasing prevalence of vivax malaria, diagnostic tools, and the public health implications of *P. vivax* and *P. falciparum* co-endemicity

**Keywords:** *Plasmodium vivax*, *Plasmodium falciparum*, sub-Saharan African,

In sub-Saharan Africa (sSA), *Plasmodium falciparum* is responsible for most of the human malaria infection and less frequently, malaria could be due to *P. ovale*, *P. malariae* and *P. vivax* [1]. Significant gains observed in massive reduction in global malaria cases and mortalities in the early 2000s have been largely due to strategic malaria interventions [1]. Nevertheless, signals of reversal in gains are beginning to emerge in most African countries [1]. This stall in progress has been attributed to increased prevalence of non-falciparum malaria parasites on one hand and the development of resistance by both the parasite and vector to drugs and insecticides

Malaria elimination, Duffy negative antigen

#### **Chapter 6**
