Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
Qingfeng Li of Johns Hopkins Bloomberg School of Public Health in the U.S. will develop a computational simulation tool to optimize the design of health campaigns in low-income settings. Health campaigns are complex events involving multiple, interconnected components, such as families and socioeconomic contexts, as well as being time restricted and targeting specific populations. Their tool uses geospatial measures and community maps, and it includes an automated algorithm to test different design strategies to identify the optimal design. They will develop their tool and test whether it can improve the vaccination rate from the current average of 30% to 50% for a selected immunization campaign in Ethiopia, where rates are low and uneven across the country.
Robert Miros of 3rd Stone Design, Inc. in the U.S. will adapt their portable vaccine refrigerator, which is battery powered and can be monitored remotely, to maximize the charge life so that it can support vaccination campaigns in low- and middle-income countries. Vaccines are normally stored in ice boxes and manually tracked, both of which are unreliable and can cause spoiling. Their vaccine refrigerator integrates thermo-electricity cooling and battery power together with an algorithm that can sense temperature and adjust it according to the available power. They will develop a new algorithm that can correlate charge times with vaccination routes, and integrate upgraded battery technologies to prolong charge life. This will then be field-tested in a selected vaccination campaign.
Coite Manuel of Food Chain LLC. in the U.S. will develop a web-based tool that takes existing road network and population data for any country, divides it into regions, and identifies the closest health facility based on type and time of travel to improve health campaign planning and better monitor population health. Current approaches to map catchment areas for health facilities use administrative boundaries or population statistics, which often don't reflect where people actually go. Once the site catchment areas have been defined, they will overlay heatmaps to identify vulnerable areas for targeting during health campaigns. They will use opensource tools and free or low-cost technologies such as MapBox, and partner with at least one country to help develop and test their technology.
David Hammel of Balcony Labs Inc. in the U.S. together with their partner Direct-Relief will develop a communication tool that combines smartphone messaging with geographical information systems to enable health campaign managers to communicate directly with health workers and civilians in a specific region of interest to improve the impact of their campaigns. The tool enables managers to send messages such as alerts, instructions, or surveys, directly to target areas, as well as collect geo-specific information in real-time for updating campaign designs and evaluating outcome. Once they have developed the dashboard and mobile software module, in partnership with a leading NGO, they will evaluate its performance in 2-3 campaigns incorporating 100,000 mobile phone users.
Pascal Geldsetzer of Stanford University in the U.S. will develop a computational tool to support health campaigns in low- and middle-income countries that can predict the number and location of the people that need targeting. They will use freely available databases, including the Demographic and Health Surveys (DHS), covering over 10 million households and high-resolution population estimates to estimate the percentage of children under 5-years-old who are un- or under-vaccinated within each 30m by 30m area. They will then use algorithms to determine the optimal numbers and locations of campaign posts needed to ensure a desired vaccination coverage within a given budget. This work will lay the foundation for developing a simple Android application to help managers increase the impact of health campaigns.
Hannah Wild of Stanford University in the U.S. will develop a modelling-based approach that uses remote sensing and geospatial analysis to include neglected and high-risk nomadic populations in health databases and for campaign planning. Nomadic pastoralists are some of the poorest populations but they are often missed by health services and campaigns because they are difficult to track. They will design algorithms and methods that use satellite imagery and open access geospatial data to capture population movements over time, which will be validated in the field. They will also develop the technical infrastructure to scale these methods and produce automated packages and best-practice guidelines so they can be implemented in a variety of low-resource settings.
James Lavery of Emory University in the U.S. will adapt an organizational learning tool to enable global health campaigns to draw on their experiences, improve their partner interactions, and enhance their overall impact. Global health campaigns are primarily evaluated in terms of program delivery and outcomes. However, these large and complex organizations interact with many different partners, and there is an untapped opportunity to improve their performance by learning about how their design and approaches affect each other. They will modify an established research fairness initiative framework for two global health campaigns to measure 45 indicators, such as the nature of a relationship, across defined subtopics. These data will be analyzed to understand how the tool can improve campaign impact, and they will assess the value of the framework for global health campaigns in a qualitative case study.
Simon Mutembo of the Macha Research Trust in Zambia will develop a method to identify and map children who have never received vaccinations so that they can be targeted during mass vaccination campaigns. Many of these children live in remote areas and are missed by population estimates. Their method combines field work by community health workers with spatial intelligence using a geospatial application on smart phones to develop geographical maps of vaccination coverage at the household level. Households with low or no vaccinations can then be targeted directly by campaign health workers. They will test their approach using a non-randomized controlled before and after study associated with planned measles and rubella mass vaccination campaigns in 14 areas in Zambia. Their method will also provide a more reliable measure of vaccination coverage.
Sangeeta Jobanputra of Connecti3 LLC in the U.S. together with the University of Rwanda and Multiverse Investments will develop a method that uses existing datasets and predictive analytics to better plan all types of health campaigns to broaden their coverage and minimize costs. The challenge of identifying those in need of a specific health service is a barrier to successful health campaigns. To address this, they will use existing databases to identify and score predictors of higher risk to a specific health condition, such as level of poverty in vitamin A deficiency. They will also incorporate data on the health campaign resources needed, such as facility location and medicines. These data will be used to develop algorithms that can identify the target population and predict the resources needed for an effective campaign.
Laura Smith of Research Foundation for the State University of New York in the U.S. will develop a decision-making tool that can plan more effective health campaigns in low- and middle-income countries by considering any competing interests of stakeholders. Health campaigns involve many different government and private stakeholders with differing interests. They will apply a systems dynamics modelling approach to two health campaigns for children under five-years-old in Zimbabwe by holding group modelling workshops with multisectoral stakeholders to identify perceived barriers and potential solutions. These data will be combined with published data to develop a quantitative decision-making tool with a user-friendly interface that can be used by the different stakeholders to select the preferred campaign design.