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.
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Nick Ruktanonchai of the University of Southampton in the United Kingdom will develop a web-based application to integrate currently disparate data on malaria disease risk, seasonal population dynamics, and past interventions, to identify prioritized areas for elimination efforts. Control programs are currently provided these data independently, making it difficult to know at a given time where elimination efforts would have the highest impact. They will develop the application together with a national malaria elimination program in one country in southern Africa. First, a framework will be created to integrate information on seasonal population dynamics established from mobile phone data along with malaria cases and risk. This framework will be adapted to interact directly with the existing health management systems used by health care workers to record disease incidence and treatment efforts. It will also enable prioritization to be adapted based on local operational constraints such as available personnel and infrastructure. They will also build a simple web interface for users to easily interact with the data integration and analytic framework.
Emmanuel Roux of the French National Research Institute for Sustainable Development in France will develop a method to standardize malaria datasets from different countries so that they can be used to monitor the disease across borders, which is crucial for elimination. They will co-design domain ontologies with relevant experts to bridge heterogeneities and standardize metadata across existing national surveillance databases, and use generic tools to extract data on individual disease incidence in cross-border areas to build a cross-border database. They will evaluate their method at the border region between French Guiana and Brazil by providing health agencies and researchers access to the harmonized cross-border database via a web application. They will also test its accuracy at the border between Colombia, Peru and Brazil.
Isabel Cruz of the University of Illinois at Chicago in the U.S. will build an ontology-based data integration framework that can predict where malaria incidence is likely to increase or decrease in Zimbabwe, to better target elimination efforts. Eliminating malaria requires being able to monitor the changing patterns of infection risk across an entire region, which is affected by multiple factors including the location of health centers, temperature, rainfall, type of landscape, and population distribution. Integrating these data is difficult because they come from different sources and are measured at different scales (resolution). Also, monitoring how a disease changes over space and time has been particularly challenging. They will develop methods using string matching to first translate the data into a common spatial data format, and then ontology matching to integrate the data. They will also introduce a novel resolution method that addresses uncertainty in spatial and temporal resolutions. These will be used for mapping a pilot region, and tools will be built to identify and visualize patterns of malaria progression in time and space.
Ayumi Arai of the University of Tokyo in Japan will use anonymized mobile phone data to produce a dynamic census that reveals the movements of all individuals in a population over time broken down into age and gender to help reduce regional malaria transmission. Human mobility and distribution play key roles in malaria transmission but it is difficult to monitor the movements of everybody in a population. They will gather call detail records and use them to develop an algorithm that considers non-phone users such as children and the elderly, and can predict gender and age group, which are linked to malaria vulnerability. To generate the dynamic census they will build a system incorporating application programming interfaces to support different input and output data formats to promote use by third parties and in other countries. They will apply the system to create a dynamic census of Mozambique, and validate it using data from transport networks and small-scale surveys.
Matt Berg of Ona Systems Inc. in the U.S. will develop a map widget that enables data collected by different platforms on different aspects of malaria, such as disease incidence and intervention efforts, to be accurately mapped and therefore more effectively integrated to help eliminate malaria. It is currently difficult to map specific events particularly in rural areas of developing countries, which lack formal addresses. Different groups use different naming schemes when recording disease-relevant data, making them difficult to cross-reference. They will develop the map widget so that users collecting or requesting data can pinpoint their location on a display of the layout of buildings and open spaces in their area using a mobile device. They will also adapt popular data collection platforms to be compatible with their system. Each location has an ID so a user can look up all disease-relevant events across different databases. In this way, the best intervention method to reduce malaria can be identified at each location. They will work with a national malaria elimination program to test their approach.
Helder Nakaya of the University of Sao Paulo in Brazil will identify hotspots of malaria transmission using the GPS data from mobile phones of infected individuals in order to find asymptomatic cases and help elimination efforts. Malaria is a major public health concern in many countries including Brazil. Eliminating the disease is difficult due in part to the existence of asymptomatic individuals who can still spread the disease but are difficult to detect. Relying on a patient remembering where they have been to identify asymptomatic individuals has not been adequate. Instead, they will test their online tool, which can extract the location history of an individual that has been recorded on their mobile phone, using patients with malaria in a reference hospital in Manaus, Brazil. The tool will be used to identify potential hotspots of transmission, which will be verified by taking blood samples from residents to identify those elusive asymptomatic patients. They will also develop an application for health care workers that displays the hotspots on a map.
Jonathan Jackson of Dimagi in the U.S., together with James Faghmous from the Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, will develop an open platform that combines real-time data on health, climate, and the environment at high resolution, and near real-time satellite data to inform on population density, in order to detect areas containing limited information (cold-spots) on malaria so that control programs can better allocate resources. Current efforts suffer from the absence of up-to-date information and the difficulty of predicting when and where interventions are needed. Platform development will be conducted in collaboration with academia and the private sector, and will be designed to use existing data recorded on mobile devices, which contain GPS data. The platform will be designed to use existing data recorded on mobile devices, which contain GPS data. These data will be supplemented by designing a machine-learning approach to extract population density from global satellite data. They will test the accuracy and usability of their software in Senegal.
Mark Westra and their team at the Akvo Foundation in the Netherlands will build a central data integration platform for malaria that assembles existing data from a variety of sources and enables it to be easily visualized, analyzed, and shared by all types of users. They will build an initial system and test it with a first set of users composed of key groups of stakeholders and users in multiple countries. This will help them gain a detailed understanding of what different users need from such a platform to ensure it becomes a valuable resource for the community. This information will then be used for iterative product development to build in additional functions and integrate new sources of data.
Nathan McEachen of TerraFrame Inc. in the U.S. will build an extract, transform and load (ETL) plugin so that diverse types of data on disease incidence, spread, and interventions, recorded with different methods can be easily uploaded into the District Health Information Software version 2 (DHIS2) open-source platform, to better inform disease elimination efforts. The DHIS2 is widely used particularly across sub-Saharan Africa to report, analyze and distribute disease-relevant information. However, data collected using different software or in different formats cannot easily be imported despite their potential significance for disease elimination. In cooperation with the Zambia National Malaria Control Centre (NMCC) and their DHIS2-expert in-country partner organization they will identify user requirements for the plugin, such as the systems being used and the nature of the incompatibilities, and test it with NMCCs in other countries.
Yang Cheng of Jiangnan University in China will develop a smartphone application to track Chinese individuals who move to work in other countries with high levels of malaria. When these migrant workers return home, there is a risk that they also import malaria, which can then be locally spread via mosquitoes and cause an outbreak. They will develop the application to measure body temperature and track location, and combine it with an existing malaria response system that is used to identify, track and treat malaria in the Jiangsu province, to improve the accuracy and speed of response.