Molly Brown of Syngenta Foundation for Sustainable Agriculture in the U.S. will develop an early warning system for crop diseases for rural farmers in Africa by gathering data using existing infrastructure and mobile tools from commercial partners and applying machine learning pest models. Insect pests cause almost half of crop losses in Africa each year, which impacts both food supply and the economy. An effective disease and pest surveillance system provides early warning to minimize crop loss but is often unavailable to rural farmers in poor countries who lack tools for sufficient pest data collection. To address this, they propose to leverage the existing infrastructure, including demonstration farms, retailers, and mobile tools, of a commercial partner to gather the required data, such as field photographs, data on pest prevalence, and satellite weather data. Mobile tools will be available on computer and Android devices with off-line modes. They will pilot test their approach in Zambia focusing on damage to maize crops caused by the fall armyworm. Real-time data will be uploaded to a freely available, public portal with information on geography, pest populations, and affected crops.

William Martin of the International Food Policy Research Institute in the U.S. will combine existing, high-quality survey data collected from individual households in rural Ethiopia and Nigeria with agricultural information from the Global Agro-Ecological Zone (GAEZ) database to model the impact of specific investments on poor communities to better inform policy decisions. National Agricultural Investment Plans (NAIPS) shape policy development by outlining investments required to stimulate growth in Africa through transformation of the agricultural sector. However, they rarely consider the impact on individual households. To address this, they will use existing household survey data from the Living Standards Measurement Study – Integrated Services on Agriculture (LSMS-ISA), which was designed to dissect the relationship between agriculture and poverty. These data will be combined with the publicly available GAEZ database, which assesses agricultural resources and potential, to model the direct impact of policy decisions, such as investments in research and rural infrastructure (irrigation, electrification), on households. Results of the analysis will help prioritize future agricultural investments for maximum impact on the poorest families.

Federico Costa of the Federal University of Bahia, Brazil, Mitermayer Galvão dos Reis of Fiocruz, Brazil, and Nathan Grubaugh and Albert I Ko of Yale University in the U.S. will establish metagenomic next generation sequencing in clinical settings in an urban region of Brazil classified as an infectious disease ‘hot spot’ to help develop new diagnostics and identify emerging pathogens. Rapid urbanization in Salvador, a metropolitan of 2.9 million inhabitants in Northeast Brazil, has produced a favorable environment for the emergence and spread of infectious diseases, and was the founding site for the recent Zika epidemic. Early detection is critical for preventing disease spread, particularly in Salvador, which is a transport hub and popular holiday destination. However, diagnosis can be challenging in low-resource settings, especially when the causative pathogen is unknown, the disease has diverse symptoms, or a known pathogen starts causing new symptoms. They will collect around 160 clinical samples from patients with suspected infections at a local infectious disease reference hospital and a maternity unit and apply a next generation sequencing approach together with the IDseq analysis platform to identify pathogens from the sequencing data.

James Berkley and Abdi Abdirahman of the University of Oxford in the United Kingdom will test whether metagenomic analysis of clinical samples from patients with suspected infectious diseases can better identify the causative pathogens than current diagnostic methods, to help improve treatment. In Africa and Asia, many severely malnourished infants die after being discharged from a hospital likely due to infectious disease syndromes such as pneumonia, diarrhea, and sepsis. However, the causative pathogen remains unidentified in the vast majority of cases due to the limited sensitivity of current diagnostic methods. Another group highly vulnerable to the limitations of diagnostics currently available in low-resource settings are those with suspected viral encephalitis, which can cause acute seizures and coma. If diagnosed correctly, some of these diseases are treatable. They will use next generation sequencing to perform metagenomic analyses of samples from patients in both groups to identify the causative pathogens, and test whether first isolating extracellular vesicles that may concentrate viral nucleic acids from the samples can improve sensitivity.