Of the 20 million children across the world with incomplete or no essential immunization (EI) for vaccine-preventable diseases, nearly half live in countries with conflicts and population displacement (e.g., Afghanistan, Central African Republic, Iraq, Mali, Nigeria, Pakistan, and Somalia) (1). Conflicts and regional instabilities generally lead to poor vaccination coverage and interrupted vaccine schedules (2) due to disruption of health systems and impeded access to care resulting in vaccine delivery inequities. Currently, the barriers to vaccine preventable disease control are increasingly less about pathogen biology and more about the identification of sub-populations missed by the Expanded Programme on Immunization and therefore left without equitable access to interventions like essential immunization and supplementary vaccination campaigns (3, 4). Immunization programs miss or underserve hard-to-reach sub-populations for various reasons including geographic inaccessibility, irregular population migration due to regional instabilities, and nomadic lifestyles. Public health must harness innovative and effective technologies to improve the remote identification of vulnerable, hard-to-reach sub-populations in order to conduct outreach when feasible and to deliver services to those populations during periods of accessibility.
Understanding the geographic distribution of target populations for health interventions is a critical component of microplanning — a population-based, detailed planning method aimed at delivering health-care interventions like childhood essential immunizations by addressing the implementation demands of a specific setting (5). Microplans critically inform decisions regarding appropriate delivery strategies (i.e., fixed-post, outreach, or mobile) and logistics needed to reach children targeted for the intervention (i.e., target populations) (6). Despite the utility of current microplans, arguments have been made for updated methods of microplanning that leverage Geographic Information Systems (GIS) and satellite imagery to generate high quality and up-to-date maps of target population distributions and maps of built features such as residential structures and settlements (7, 8). In their Reach Every District (RED) strategy for essential immunization, the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) recognized the need for these updated methods and outlined new GIS-enhanced microplanning tactics for improved location surveillance of some populations
In some situations, GIS-based microplanning incurs higher costs than traditional, non-GIS based microplanning; however, this does not necessarily imply cost ineffectiveness. A recent analysis cost-effectiveness conducted in two Nigerian states determined that increased cost for GIS-based microplanning was mostly due to purchasing additional vaccines for populations previously uncounted and unreached by traditional microplanning methods (7). Not only does GIS-based microplanning save resources when executed appropriately, it also protects the lives of field workers in settings where conflict could compromise their security by reducing the need for deployment to high-risk areas (6). When in-person access is safe and feasible, having field workers physically present in the region of interest allows for ground-truthing which is needed to validate maps generated remotely (i.e., generated using imagery and without physical access to the area of interest). Supplementing microplanning methods with the integration of GIS technologies could further support other public health interventions, such as spraying insecticides for mosquito abatement and malaria prevention (9, 10) and the provision of maternal and child health care services (7).
To support the integration of GIS technology in public health planning, researchers take advantage of high- or very high-resolution (VHR) satellite imagery generated by satellites like GeoEye, QuickBird, RapidEye, and WorldView. Sub-meter resolution imagery from these satellites allows analysts to digitize features such as buildings, rooftops, roads, nomadic camps, and informal settlements. The size of a population can even be modeled from these footprints.
Large-scale feature digitization (e.g., digitization of individual structures across multiple districts or provinces) from imagery without automation methods is very time-consuming for a small group of analysts, especially when the features of interest are sparse in the imagery. Consequently, a method of participatory data acquisition has gained popularity – the “mapathon” – which is a time-limited, crowd-sourced effort by a group of trained participants with or without formal geospatial analysis backgrounds. Participants, used in this paper to describe both the group of contributors and validators, generate spatial data of features like residential structures or informal settlements within a specific area of interest by using GIS platforms, such as OpenStreetMap and ArcGIS Online. Generally there is no financial incentive for contributions made during a mapathon (11) and anyone with a computer and internet connection can contribute. Consequently, humanitarian efforts frequently rely on mapathons to identify mobile populations and undetected settlements (11, 12). Similarly, data generated from mapathons are useful for detecting and enumerating populations missed during immunization campaigns; thereby, optimizing immunization campaign microplans. Mapathons also provide data that are used to map health facility catchment areas when merged with other key information (12).
An alternative method to using mapathons is automated feature extraction (AFE), a type of model-based feature generation, which can be semi- (i.e., some human support) or fully automated (i.e., no human support). After an initial time investment to manually develop training data using selected examples of features of interest (e.g., man-made structures) and examples of features not of interest (e.g., large boulders), AFE does not require time- consuming and labor-intensive steps such as identifying structures and placing points or drawing polygons manually on a computer. AFE relies on computer algorithms and models to learn patterns, edges, and shapes of features (e.g., rooftops or settlement footprints) to digitize and categorize. Machine learning algorithms are designed to enhance performance by effectively teaching the computer how to extract the desired spatial data from imagery with both precision and accuracy. AFE has been leveraged for a myriad of purposes, such as mapping agricultural land use (13–16) and water boundaries (17, 18), estimating human and livestock populations (19, 20), road feature extraction (21, 22), building feature extraction (23–29), and to support disaster relief efforts (30, 31).
Like mapathons, AFE relies on high-resolution imagery for optimal performance, but image collection parameters can be refined to account for cloud cover, thick vegetation, and low spectral resolution. Additionally, using a time-series of images can improve the accuracy of feature detection by minimizing false-positives (14, 18) and are especially helpful when analyzing pre- and post-disaster impacts to roads (30) and facilities (31). AFE could be particularly useful for essential immunization efforts because it generates spatial data from imagery rapidly and has the potential to be more accurate than mapathons.
There is currently no information on how results from participatory mapping compare to the results from AFE; if researchers determine AFE to be as accurate and precise as mapathons but faster at generating spatial data, increasing its use could save valuable resources and time for public health programs without compromising quality. Additionally, as geospatial professionals gain a deeper understanding of the strengths of each method, future projects can more optimally combine the two to complement and enhance their end-products.
Disparities in equitable access to health services will decrease when additional sub-populations are identified in microplans and serviced by EI campaigns and other public health interventions. Here, we seek to explore and compare the accuracy of two methods of feature generation – mapathons and AFE – to provide evidence for the suitability of each method in identifying hard-to-reach populations vulnerable to vaccine-preventable diseases in inaccessible areas and whether the two methods can work in a complementary or synergistic way.