Nigeria encompasses a unique and significant role in Africa. Not only is it the continent’s most populous country, clocking in at 206 million people, but it also is also the continent’s largest economy (with a total GDP of $441 billion in 2021). However, it also maintains a less desirable superlative title, which is that it bears the highest burden of malaria deaths on the continent, and 27% of all malaria deaths worldwide in 2020. In raw numbers, this amounts to an estimated 64.5 million cases annually. As a result, the public health challenge of malaria in Nigeria is enormous, and the stakes are high. Though Nigeria’s National Malaria Elimination Program (NMEP) has risen to meet this challenge consistently throughout the years, attaining the goal of reducing malaria morbidity to less than 10% parasite prevalence and mortality attributable to malaria to less than 50 deaths per 1,000 by 2025 is far from guaranteed. One key in realizing this goal is seasonal malaria chemoprevention (SMC), which typically consists of routine administration of two antimalarial drugs to children 3 to 59 months of age during the peak months of malaria transmission. Malaria Consortium and Akros worked with the Nigeria NMEP to support the planning, tracking, and delivery of SMC in six rural health facility catchments in the Shagari local government area of Sokoto State, Nigeria in 2021.
Senegal is hardly alone in experiencing challenges with their COVID-19 vaccine roll out. Like many countries, much of the challenge has been related to hesitancy and demand. Recognizing that having ready access to COVID-19 vaccination-related behavior indicators can inform a better understanding of why coverage is not reaching saturation and to whom additional resources and efforts should be directed, Akros (in collaboration with Fraym and GRID3) worked with Senegal Ministry of Health and Social Action (MSAS) departments and in-country partners to build a custom geospatial dashboard that demonstrates these data.
Hesitancy and lack of demand issues for vaccines can stem from a number of causes. For example, rural communities far from health facilities administering vaccines may require significantly extra effort and expenses to travel to get the intervention, resulting in a demand challenge. The response to this demand-driven low-coverage problem will be different than hesitancy-driven challenges and the data required to respond effectively is likewise different. In this example, understanding: 1) Which communities are farther than a reasonable traveling distance to the health facility, 2) where exactly those communities are, and 3) how many people are expected to be found there, is valuable information that can be used to maximize the chances of a successful response. However, access to these types of granular geospatial demographic and health data that promote this level of evaluation to allow progress against such bottlenecks, has not been widely available or accessible to staff needing to make critical resource prioritization decisions.
The solution—hyperlocal geospatial data for COVID-19 vaccinations
With vaccination hesitancy and demand as the major challenges to achieving higher coverage, the data prioritized for this dashboard were proxy indicators for providing more insight into these challenges. Further, in order to decentralize decision making and empower district and health facility staff to make decisions that drive up coverage, this data was made accessible through the dashboard at 1km x 1km cells that can be aggregated up to health facility and district-level indicators. The dashboard interface allows this hyperlocal data to be, quickly and easily, geospatially explored before downloading for further analysis or input into external planning tools.
This work was built upon a history of collaboration among these partners — which has had success in providing detailed microplanning services utilizing granular spatial data to government malaria and neglected tropical disease (NTD) programs, with demonstrated examples for malaria in Zambia, Nigeria, and Senegal and for NTDs in Rwanda and Kenya. Building on that technical capacity, the dashboard in Figure 1 (showing COVID-19 vulnerable populations against health facility catchment areas) was built to display modeled COVID-19 vulnerability data to enable more informed decisions within vaccination planning workflows.
The geospatial dashboard consolidated a wide variety of data and relevant COVID-19 vulnerability and risk models into the visualization to be filtered by region, district, and health facility to inform all levels of health planning. Largely using demographic and health surveys, the data includes statistically sound high-quality, geo-tagged household survey data, satellite imagery-derived data products, health metrics, and health infrastructure. This hyperlocal data, down to 1km grid cells, allows for the visualization of the spatial distribution of priority groups and classifies individuals within priority groups using WHO-guided indicators of vulnerability. These include elderly population groups and groups that receive a high vulnerability score generated within the COVID-19 vulnerability model. Other COVID-19 indicators within this model included vaccine allocation, exposure, co-morbidities, information access, prevention activities, and vaccination likeliness — all of which were able to be filtered, displayed, and extracted for all levels of the health administration hierarchy to inform microplanning.
Digital Square at PATH is pleased to announce that Digital Square’s Board has approved eight proposals for investment—as part of Notice F—to strengthen adaptable, replicable digital tools designed to work together seamlessly to improve health outcomes and help close the health equity gap around the world.
The advancement of mature digital public goods for health (global goods) is crucial for saving lives and improving health around the world because these free and open-source digital health tools can be used across different countries and health program verticals, cutting down on fragmentation and duplication to accelerate scale and health impact.
This funding call encouraged applicants to focus on aligning to global health standards and guidance, as well as working with local teams to build capacity in health technologies in the countries and contexts where they will be used. Notice F consists of three workstreams:
Create a set of standard implementation examples using the WHO digital adaptation kit (DAK) for antenatal care so that more digital systems will include data and health content that are consistent with WHO’s antenatal care recommendations.
Strengthen the technologies of software global goods so they can be deployed as stand-alone products, while building the capacity of new innovators and implementers.
Last month, the World Health Organization (WHO) sent joyous shock waves through the global health community and the Global South by officially recommending that the new RTS,S/AS01 (RTS,S) malaria vaccine be adopted into widespread use among children in sub-Saharan Africa and other regions with moderate to high P. falciparum malaria incidence. Now the international community, through Gavi, the Vaccine Alliance, has just stepped forward to help finance the rollout of the world’s first malaria vaccine.
It has been true for some time that sub-Saharan Africa bears the largest malaria burden in the world, with children shouldering the largest proportion of deaths. Over 90% of global cases are on the continent, with children under the age of 5 years constituting a staggering two-thirds of all malaria deaths. This is due to a confluence of factors, not least because of the widespread prevalence of P. falciparum (the most deadly species of malaria parasite), and a very efficient mosquito that spreads it (Anopheles gambiae). The economic impact of malaria is estimated to cost Africa $12 billion every year—a figure that also factors in costs of healthcare, absenteeism, days lost in education, decreased productivity due to brain damage from cerebral malaria, and loss of investment and tourism. The introduction of an effective vaccine offers a beam of hope in the fight to mitigate the massive toll this centuries-old disease inflicts on Africa.
In recent years, countries within the Southern African Development Community (SADC) region of Africa have been making outstanding strides toward the challenging goal of malaria elimination. This commitment to elimination is the guiding mandate of Elimination 8 (E8), which focuses on the following countries: Angola, Botswana, Eswatini, Mozambique, Namibia, South Africa, Zambia, and Zimbabwe. Through E8, these ministerial bodies have resolved to coordinate their efforts to collaboratively work toward the shared regional goal of malaria elimination.
To that end, in 2017, E8 contracted with MENTOR to deliver indoor residual spray (IRS) in border districts of southern Angola. The MENTOR Initiative is a registered nonprofit organization devoted to reducing deaths and suffering from tropical diseases. In Angola, MENTOR has been a stable National Malaria Control Program (NMCP) partner supporting vector control, case management, and surveillance activities since 2004. MENTOR has established offices across Angola and has full country reach in its operations, making it the ideal partner for E8 to engage for expanded IRS interventions. Since 2017, MENTOR has implemented IRS with reported high coverage rates in all campaigns conducted. For its 2020 campaign, MENTOR aimed to improve IRS reporting systems and increase accountability to donors and NMCP. To realize this goal, they chose to pilot Reveal—a digital global good and open source spatial intelligence platform used to drive the delivery of life-saving interventions. MENTOR collaborated with Akros to configure and support the field pilot of Reveal v. 1 in Menongue District (Cuando Cubango Province).
Through Reveal, field teams and managers use detailed household and community maps, protocols, and a data-collection and dashboarding platform for decision-making support to plan, implement, and adjust interventions so they achieve the greatest impact. Akros’ partnership with MENTOR Initiative to pilot Reveal v. 1 in Menogue District to support IRS delivery first included assessment and scoping to understand three key elements: intervention and data collection and management protocols, resource availability, and the environment within which Reveal was to be implemented and sustained. Akros then conducted system configuration to align IRS standard operating procedures (SOPs) with the Reveal platform, set-up mobile application and coverage dashboards, and configured feedback loops and tasking for unique user roles. Following that, the Reveal team participated in campaign planning, including enumeration and base map preparation and work to plan and validate in-field assumptions. Finally, the Reveal team supported implementation in December 2020 and provided post-implementation support.
Maximal coverage defines the effectiveness of vaccination campaigns. Over one year after the start of the devastating COVID-19 pandemic, countries around the globe are vaccinating those ages 16 and older, while vaccine-producing companies are conducting vaccine trials for children. Positivity and hope infuse vaccine distribution efforts, but as these initiatives ensue, attention is being drawn to the challenges of vaccine distribution, namely, those left behind.
Mass vaccination campaigns are critical to the introduction of new vaccines, to providing doses to those who may have missed routine doses, and to giving a second opportunity to those who may not have developed immunity. In each instance, with greater coverage comes stronger, more resilient communities. However, zero-dose children, or children who have not received any routine vaccinations, are often missed by these campaigns. With every child left unvaccinated, communities’ vulnerability to vaccine-preventable diseases escalates. Fortunately, in bracing for future vaccination efforts, we can look to previous initiatives to guide our efforts. In particular, the potential of geospatial data and technology to ensure all, including zero-dose children, are included.
From June through to December 2020, Akros, in partnership with Johns Hopkins University, Macha Research Trust, and the Zambia Ministry of Health, utilized spatial intelligence and the Reveal platform to identify and vaccinate zero-dose children following a nationwide Zambian vaccination campaign for measles and rubella.
We are thrilled to announce Akros has been awarded a Grand Challenges Explorations Grant, an initiative of the Bill & Melinda Gates Foundation! Grand Challenges Explorations (GCE) grants support impactful innovations striving to remedy critical global health and development problems. With this grant, Akros will incorporate into Reveal the ability to integrate data from human movement models. Reveal is a web-based mapping platform which uses spatial intelligence to ensure all receive life-saving interventions. With the ability to integrate human movement models with Reveal, decision makers and field teams will be better able to predict where people will be at different times of the day and seasons to ensure no one is missed with lifesaving resources.
The existing Reveal Platform, improves health campaign coverage by utilizing spatial intelligence and context-appropriate technology. Presently, Reveal maps communities at the household level and offers intervention teams a streamlined interface to plan, implement, track and monitor campaign coverage. Relative to traditional approaches in which local health teams aggregate population data by hand and process it in hard copy, Reveal’s user-centered technology offers a more accurate population count and implementation system—designed to include even the most remote of households.
Delivering health campaigns at high coverage rates can be challenging—particularly in places where frequent movement is common. Permanent relocation may swell or shrink a population, influencing critical resource distribution. Seasonal migration may redistribute a population for months at a time. Daily or weekly movements may make certain individuals more likely to be left out of a campaign. In these situations, health workers may arrive at a household to deliver interventions, but instead find the family has shifted, for even just a few months depending on fishing or farming needs. Take for instance, Nchelenge District in Northern Zambia.
At the time of writing, 33.7 million cases of COVID-19 have been reported worldwide. Regardless of socioeconomic standing, health systems around the world have shuddered beneath the weight of an international pandemic; leading to overflowing ICUs, overextended health care resources, and disrupted critical supply chains.
Accordingly, international attention and funding has turned to global public health and preparedness. The World Health Organization (WHO) has estimated approximately US$1.7 billion total funding is needed to adequately respond to COVID-19 until December 2020. As of September 21, 2020 WHO reports receiving 79.5% of their goal, with an additional 4% expected from pledges—a combined US$1.51 billion raised in the span of a few months. Resources have been rightly and urgently mobilized to offer aid now, but as a responsible global health community, we must look toward the future and set in motion plans to meet anticipated gaps and needs.
If we are to respond effectively and reach the most vulnerable populations, our future interventions to protect people from COVID-19 transmission will inevitably rely on community health structures to disseminate aid and vaccines. If the systems in place are not adequately equipped to respond, those interventions will fall short. Further, if we lack good data on the population and location of communities, getting resources to all those in need will be even more challenging. However, presently, population data are often inconsistent, outdated or quite coarse. Compromised by unclear boundaries or moving populations, the resulting data typically offers only a blurred picture of communities, making it challenging for public health teams to allocate resources effectively.
Zambia, along with a handful of countries within Southern and Eastern Africa, is on track to reduce malaria cases 40% by 2020. Relative to 2015, the country’s progress so far translates to as many as 700,000 cases prevented annually. Such tremendous strides speak to the success of preventative interventions, such as improved access to indoor residual spraying (IRS) and insecticide treated nets (ITNs), but the work is far from over.
The citizens and communities across Zambia rely on annual district planning to determine where, how much of, and what interventions are needed in a particular population. In answering these questions, district teams begin their microplanning processes, determining on a local level the nuances of the year’s malaria interventions. These teams must deeply understand the communities they serve. Who is at greatest risk of infection? Where do they live? What settlements ought to be targeted? And what resources are needed to bring a community out of harm’s way? Lacking this information, district teams cannot fully grasp the extent of preventable malaria cases and consequently limit their capacity to act.
Traditionally, teams of local community health workers aggregate this information on foot and process it in hard copy. This costly and time consuming work flow jeopardizes the data’s accuracy, totality, and speedy delivery to key decision makers. System bottlenecks, limited resources, or a lack of confidence in the data can then undermine the quantitative foundation of an intervention. Weary decision makers might turn instead to outdated data and an imprecise understanding of the population they aim to serve. What ought to be a concrete step in the year’s plan bends to inefficacious circumstances.
Accurate quantification of a population, and the ability to locate this population with precision, are fundamental requirements for reporting the true coverage and effectiveness of public health interventions—such as childhood immunizations, indoor residual spraying (IRS) for malaria, or mass drug administration (MDA) for neglected tropical diseases.
Public health interventions, however, often rely on field teams to locate rural villages or even homes on the ground. In areas where there are no street address systems, or where homes are not mapped, manual searches often result in groups of households being missed; thus preventing the delivery of services to those in need. When service coverage is subsequently reported as a function of the population found, the impact and effectiveness of an intervention may be overstated.
Spatial Intelligence and the Reveal Solution
The transformative field of spatial intelligence is revolutionizing digital health and public health more broadly. Artificial intelligence (AI), digital maps, and spatial modeling are powerful, burgeoning toolsets; but until more recently, they have not benefited field workers and large-scale, labor-intensive campaigns. Now, the power of these digital tools is being accessed by field workers in rural, underserved communities.
Reveal, an open-source platform and global good, uses spatial intelligence to help field workers effectively navigate and deliver life-saving interventions to people who previously would have been missed, increasing the true coverage of interventions and improving health outcomes for vulnerable populations.
Supporting an IRS Campaign in Zambia
Satellite imagery was enumerated to establish a baseline understanding of structure count and spatial distribution in several districts. These were layered with risk maps to target high-risk regions, which enabled users to identify eligible households and assign teams to priority areas.
Using Reveal’s mobile and map-based interface, field workers were able to navigate through communities, identify targeted households, and collect intervention data against eligible households in a coordinated manner within and across teams. The near real-time feedback of data, as a result of the mobile application’s offline and peer-to-peer (P2P) syncing functionality, inspired increased teamwork and cohesion as the campaign progressed and teams worked toward a common goal.
Through dashboards, map-based visualizations, and built-in feedback loops, intervention managers were able to actively monitor campaign progress toward targets, in a given spray area and as a whole, thus facilitating data-driven course correction to optimize performance and maximize impact.
With the support of Reveal, Siavonga District increased its absolute coverage of IRS from 51.5% to 75.5%, while Sinazongwe increased from 31.5% to 61.9%. These changes in coverage were possible due to a better understanding of resource needs. In other words, the use of Reveal allowed districts to better understand the size and distribution of the target population, thus impacting planning and implementation.