Hasan Uludag of RJH Biosciences in Canada will develop an affordable immunotherapy system based on genome-integrating transposons that works inside the body for the treatment of a wide variety of diseases such as cancer and diabetes. Emerging immunotherapies offer promising treatment for many diseases, but they require genetic modification of immune cells outside the body, and are thus labor intensive and expensive, limiting their utility in developing countries. They will use engineered nanoparticles in a new approach to immunotherapy that modifies immune cells inside the body. The nanoparticles are derived from polymeric materials that can encapsulate nucleic acids and proteins and release them into host cells. These nanoparticles will be dispersed in a hydrogel matrix with immunostimulatory molecules to create a living bioreactor inside the host that will attract and genetically modify immune cells. They will select polymers for their ability to deliver DNA-based transposons (to facilitate integration into the host genome) to immune cells and to stably express a reporter gene. Optimal polymers will be transferred into mice and they will evaluate transfection efficiency into immune cells with a fluorescent reporter gene. Finally, they will test the therapeutic efficacy of their in situ immune cell engineering approach in a mouse leukemia model.

Fredos Okumu of the Ifakara Health Institute in Tanzania will develop technology to evaluate mosquito control interventions using a combination of artificial intelligence, infrared spectroscopy, and entomology. Malaria caused over 400,000 deaths in 2017, the majority in the developing world, and an effective way to control the disease is to target the mosquitoes that transmit it. Current tools cannot precisely measure mosquito age or life-expectancy, and are therefore unable to predict the impact of mosquito control interventions. The biochemical composition of the mosquito exoskeleton varies with species and age; as the types of chemical bonds change so does the amount of light absorbed in the mid-infrared region. This can be measured with mid-infrared spectroscopy (MIRS), and they will combine this with machine learning to measure the age of mosquito populations. Using a dataset collected from over 25,000 lab-raised mosquitoes, they have developed a supervised machine learning model that accurately predicts mosquito age and species. They will optimize this model to work also on wild mosquito populations, develop an online platform for real-time analysis of mosquito MIRS data, and test its ability to measure the effectiveness of malaria control interventions.

Denise Monack of Stanford University in the U.S. will use a genetic approach to identify the molecular mechanisms that enable the typhoid fever-causing bacterium S. Typhi to survive in aquatic environments and to rapidly adapt to transmission to humans. Annually, S. Typhi causes over 20 million infections and 200,000 deaths, mostly among populations that lack access to clean drinking water. Understanding how S. Typhi persists in water and then quickly adapts to its human host is critical for controlling transmission. Bacteria use various mechanisms to adapt to environmental changes, including so-called RNA thermometers (RNATs), which form secondary structures in mRNAs that can rapidly activate gene expression when temperatures change. They will use their established genetic screening approach to identify new RNATs in S. Typhi and validate their ability to promote bacterial persistence within aquatic microbial communities by generating mutants. They will also follow up on past work in which a bioinformatics approach identified new RNATs that may regulate the expression of the chitinase enzyme, which is used by the cholera-causing bacterium to bind to plankton and create a protective environmental niche. They will evaluate whether chitin is also important for S. Typhi persistence and transmission.

Joseph Tucker of the London School of Hygiene and Tropical Medicine in the United Kingdom will hold a national crowdsourcing contest to develop a social media-based intervention to improve confidence in childhood vaccines and boost coverage in China. Expert-driven strategies have been launched to promote vaccination coverage in China, but have had limited effect. As an alternative approach, they will apply crowdsourcing to tap into the knowledge of individuals to design a more effective, online intervention. They will open the contest with a call for new ideas that use text, images, and videos to promote vaccinations; enable online evaluation of those ideas by crowd and expert judges; and assemble a steering committee of health experts to produce the finalists. The final content of the intervention will be developed by the finalists in an intensive 'designathon' event. They will test the new intervention in select community health centers in three cities in China and analyze its ability to improve confidence in vaccinations.

Windy Tanner and Jim VanDerslice of the University of Utah in the U.S., together with colleagues from Mehran University of Engineering and Technology in Pakistan, will analyze water samples to determine the conditions that promote the survival of the typhoid fever causing bacterium Salmonella Typhi, and they will use metagenomic deconvolution to identify any gene exchange from other microbial species that may produce drug-resistant strains. S. Typhi is responsible for over 100,000 deaths each year, mostly in the developing world where fecal contamination of food and drinking water is common. The emergence of drug-resistant strains has limited the available treatment options. Biofilms are environmental niches with complex microbial communities and are ubiquitous in the environments where S. Typhi is commonly found. They will sample water and biofilms from a variety of these environments along the fecal-drinking water transmission route in the Sindh province of Pakistan and test for the presence of S. Typhi using qPCR and culture methods. They will also evaluate whether specific organisms stabilize and protect S. Typhi in these biofilms and could cause resistance gene exchange.

Itoro Ata of the Solina Center for International Development and Research in Nigeria will implement a program to support unemployed mothers in Nigeria by linking education on the importance of vaccinations with vocational skills education and training to improve immunization coverage. Many rural areas in Nigeria have consistently low rates of routine immunization and large populations of unemployed, stay-at-home mothers whose children are at risk of vaccine-preventable disease. They will create a direct link between community health systems and economic empowerment programs focused on vocational skill development by creating the Routine Immunization Buddy System (RIBS). Mothers in the program will be placed in small support groups and paired with another woman within the group. The lead woman of each group will be trained by community health workers to provide practical information on vaccines using a teaching tool called Hannun Rigakafi (the immunization hand). Women in the RIBS groups will also be taught about possible work options, such as farming, and offered associated tools and training. Pairing education on vaccines with vocational training should help boost the confidence of the mothers and better motivate them to complete the five routine childhood immunizations for their children. The program will be piloted in the Kaduna state of Nigeria and evaluated for improving vaccination knowledge and demand.

Pietro Alano of the Instituto Superiore de Sanità in Italy will develop a biochip that mimics the midgut of the Anopheles mosquito and can be used to more easily and quickly test candidate anti-malarial compounds for blocking transmission of the causative Plasmodium parasite. Malaria is a potentially fatal infection caused by parasites transmitted between humans through the bites of infected mosquitoes. When a mosquito bites an infected person, immature Plasmodium gametocytes enter the mosquito and transform into an invasive ookinete stage in its midgut. They then traverse the gut wall to the external gut lumen, where they enter their parasite stage. To eliminate malaria, compounds are needed that block the transmission of Plasmodium. However, current methods to evaluate the candidate transmission-blocking drugs or vaccines that are under development are slow and involve feeding malaria-infected blood to mosquitoes, which is potentially dangerous. As an alternative, they will create a biochip to reproduce the mosquito midgut environment that can support the development of parasites, and develop a bioluminescent antibody-based technique to count successfully traversing ookinetes. They will test the performance of the biochip using known anti-transmission drugs.

Andrew Jackson of the University of Liverpool in the United Kingdom will determine whether the amoeba, Acanthamoeba, which is commonly found in water and soil, acts as a host for Salmonella Typhi bacteria, which cause typhoid fever, to support growth and disease spread in Malawi. Typhoid fever is a systemic, potentially fatal illness, usually contracted by consuming contaminated drinking water. An estimated 11-21 million cases occur worldwide each year. Acanthamoeba is known as the 'Trojan Horse' of the microbial world for its ability to host a number of human pathogens, including S. Typhi. It is speculated that Acanthamoeba acts as an environmental reservoir to facilitate the survival of S. Typhi, and perhaps other human pathogens. They will prove the widespread presence of Acanthamoeba-Salmonella associations directly by using single-cell DNA sequencing. Individual amoeba will be isolated from water and soil samples from typhoid hotspots in Malawi using fluorescence-activated cell sorting. Total DNA in each amoeba will be sequenced in order to identify carriage of S. Typhi strains. Single nucleotide polymorphism analysis will be used to compare these with bacteria in local clinical isolates to determine the role of Acanthamoeba in disease transmission.

Feng Fu of Dartmouth College in the U.S. will use social networks to promote positive attitudes and overcome negative views of vaccinations and thereby increase demand. The success of vaccinations has led to steep declines in the incidence of many serious diseases. However, this has decreased the perception of disease risk and thereby lowered vaccination coverage as parents concerns switch to other factors, such as cost and the perceived risk of the vaccination itself, which are fueled via social media channels. These current low vaccination rates exhibit so-called hysteresis whereby the past concerns about safety or necessity prevent the rates from increasing even when the concerns have been disproven. To overcome this, they will use computer modeling approaches to test the ability of targeting social networks to leverage social "contagion" (i.e., spread) of positive attitudes to vaccine knowledge. They will use a healthcare intervention dataset from a network of rural villages in Honduras to model how one or more health-related behaviors or beliefs of an individual affects the group to simulate the social contagion process related to vaccines. They will also evaluate the potential positive impact of influential individuals who publicly support vaccination. The results will be used to develop social network targeting algorithms to increase the demand for vaccination. Their modeling results will be validated using the real data from the village networks.

Caroline Aura from the University of Nairobi in Kenya will teach frontline health workers and caregivers new skills so they can apply simple techniques such as swaddling and rocking to lessen the pain and distress of infants during injections to improve vaccination rates. Vaccination rates are still too low in many low-resource settings, which may be due in part to the discomfort they cause infants. This in turn makes caregivers reluctant to obtain all the recommended vaccinations for their children. Methods exist to reduce the associated pain of injections, but health workers lack the knowledge and skills to implement them. To test their approach, they will recruit vaccinators and community health workers at four rural immunization centers and use seminars and workshops to teach them pain-relieving techniques, including using specific positions and making soothing sounds. They will also develop audio-visual training tools and illustrative guides to help teach the techniques to parents for them to use at home as well. All healthy children under 12 months old visiting the centers for a vaccination will also receive one of the pain relief techniques. They will evaluate the ability of the health workers to manage pain, the level of distress of the infants, and the experience of the caregivers.

Ernest Darhok of Broadreach in South Africa will use mobile technology to improve access to child immunization services for populations living on the Kenya-Uganda border and help ensure all children are fully vaccinated. Refugee populations living in cross-border settings and migrant communities are particularly difficult to cover because of limited access, poor coordination across borders, and lack of efficient tracking. They have been using a human-centered approach to understand what these populations need to vaccinate their children, and have pilot tested the use of near-field communication cards with an immunization application that holds a child’s vaccination and health data for caregivers, which they can also use to plan more convenient appointments. This card can then be viewed and updated by health workers on both sides of the border using a mobile system. They will extend this pilot study to a wider population in Kenya and Uganda to evaluate the effect on vaccination rates against polio, and apply machine learning methods to better forecast vaccination needs at cross-border facilities to avoid stocks running out. They will obtain user feedback at all stages to help improve their approach.

Amos Kahwa of Damax Solutions Company Ltd. in Tanzania will use human-centered design principles to develop a community-supported, social marketing approach that breaks down misconceptions and psychosocial barriers to immunization in developing countries and thereby increases demand. Research has identified several causes of the current low demand for vaccinations including misconceptions about safety, inadequate knowledge of schedules, and negative experiences at clinics. Current approaches designed to increase demand such as better education and incentives have had a limited effect. As an alternative, they propose to directly target the misperceptions and societal influences and empower the communities to help. They will first consult with community members and local government to gain further insight into the drivers of low demand that will be used to design a prototype package of interventions in collaboration with the local community. They will perform several rounds of testing and refining this package, and they will evaluate its ability to improve perceptions and social norms towards vaccinations and ultimately to improve demand.

Marnie Winter of the University of South Australia, together with Tina Bianco-Miotto, Claire Roberts, and Clare Whitehead of the University of Adelaide in Australia and the University of Toronto in Canada, will develop and test short-interfering RNAs (siRNA) high-density lipoprotein (HDL) nanocarriers for the treatment of preeclampsia. Globally, ten million women develop preeclampsia during pregnancy each year, which results in the deaths of 76,000 women and 500,000 babies; 99% of these are in developing countries. Most current treatments focus on treating the symptoms (high blood pressure and proteinuria) rather than the molecular causes. Some of the causative molecules, such as the angiogenesis inhibitor sFlt1, can be blocked by specific siRNAs, but the challenge is targeting the siRNAs to the right cells in the body. HDL delivery systems for this purpose are effective and safe, and both siRNAs and HDLs are stable at room temperature, important for therapies in resource-poor areas. They will optimize the formulation of their HDL nanocarrier manufacturing platform, and characterize siRNA loading, carrier stability, size, cellular uptake, and silencing ability in 2D culture. Further, they will bioengineer an ex-vivo placenta model that fully recapitulates the structural and phenotypic complexity of a preeclamptic placenta and use it to evaluate tissue penetration and silencing abilities of the siRNA-nanocarrier complex.

Aaron Jenkins of the University of Sydney in Australia will combine genomics approaches with physical chemistry to identify the organisms and environmental factors in riverbeds that support the survival and spread of the bacterium, Salmonella enterica serovar Typhi, which causes typhoid. Aquatic environments are a major reservoir of typhoid, but how the bacteria survive in these conditions is unclear, making it difficult to prevent the disease spreading to humans. They hypothesize that S. Typhi survive in biofilms associated with sediments in riverbeds, and that the composition of this niche promotes its ability to infect humans. To test this, they will sample aquatic biofilms from areas of high, low, and zero typhoid incidence in Fiji, and identify the microbial communities supporting S. Typhi survival using antibody capture and metagenomics. They will also use fluorescence in situ hybridization to determine the spatial organization of S. Typhi in multispecies biofilms. In addition, they will analyze the composition of the sediments and soil of the riverbeds, and the nutrients being taken up by resident fish and crustaceans. By combining these results with their epidemiological data, they can identify the ecological niches that support high typhoid incidence, which will help develop and guide intervention strategies to block transmission to humans.

Darryl Russell of the University of Adelaide in Australia is seeking safer contraceptives that block ovulation without altering hormone levels and cause fewer side effects using an automated in-vitro screening platform that measures cell adhesion in the cumulus-oocyte complex, which is required to release the oocyte from the ovary. In Phase I, they built the screening platform by isolating cumulus-oocyte complexes from mice, culturing them in fibronectin-coated multi-well plates, and quantifying adhesion in a 96-well plate format using an automated assay. In a first run, they screened a library of 129 FDA-approved chemical compounds over four months and identified seven candidate contraceptives with known protein targets, one of which showed a strong reduction in ovulation when tested in mice. In Phase II, they will study whether one of the main target proteins identified in their first screen is a key target for blocking ovulation. They will also test whether the other candidates from their first screen can block ovulation in mice and screen larger and more diverse libraries to identify new candidate contraceptives and prioritize them for further drug development and testing.

Drew Arenth, Benjamin Fels, and Suvrit Sra of Macro-Eyes in the U.S. are applying a statistical machine learning approach to the immunization supply chains of health facilities in Tanzania that accurately and continuously predicts demand to ensure the right vaccines and levels are being stocked. Currently, vaccine supply is largely fixed or driven by depleted stocks. This leaves children unable to be vaccinated due to stock outs at clinics, as well as often high levels of waste, which could both be overcome by better forecasting vaccination needs for individual clinics. In Phase I, they worked with an NGO and the Ministry of Health to access routinely-collected daily vaccination data from 710 health facilities spread across Tanzania. These data were then used to train algorithms to identify predictive patterns that were tested on independent datasets. This led to a model that could accurately forecast future vaccine consumption. In Phase II, they will work out how best to integrate their approach as an automated component within the existing supply chain infrastructure in Tanzania and develop tools and train advocates to demonstrate its value and encourage implementation and adoption.

Peter Wagstaff of Self Help Africa in Ireland will build an advanced machine learning algorithm that automatically analyzes high-resolution satellite images for near real-time, low-cost detection of crop pests and diseases across wide, varied landscapes. Current detection methods are either resource- or cost-intensive and limited in their ability to provide up-to-date information across large and complex geographic areas. Crop pests and diseases can alter leaf color and expose soil, which can be detected by very high-resolution satellite imaging. They will combine satellite images provided by their partner with field data on the fall armyworm crop pest collected by their project team over 18 months on smallholder plots in the Balaka district in Malawi. These data will be used to train an algorithm to detect pests and diseases. They will use cloud-based workflows to enable computationally intensive processing of large quantities of high-resolution images in near real-time. The accuracy of the algorithm will be evaluated by an independent field survey. Note: This grant is funded by the Foundation for Food and Agriculture Research (FFAR).

David Aanensen from the University of Oxford and the Wellcome Sanger Institute in the United Kingdom and Maria van Kerkhove of the World Health Organization in Switzerland will combine next generation DNA sequencing technology with a simple, web-based data collection, processing, and distribution platform to better track the global spread of deadly infectious diseases including Middle East Respiratory Syndrome (MERS-CoV). MERS - also known as camel flu - is a viral disease that causes fever, cough, diarrhea, and shortness of breath, and is transmitted from camels to humans. One third of people diagnosed with the disease die. Next generation sequencing (NGS) technology allows rapid, inexpensive detection of pathogens as they spread. However, laboratories in different member states use different formats for sequencing data, and there is no mechanism for sharing it in real time. This limits the value of the technology for stopping outbreaks. To address this, they will establish routine sequencing protocols for both human and camel samples, and develop an interactive web platform on which the sequencing and epidemiological data can be shared. This will help develop more effective, real-time medical and non-medical interventions at local, national, and international levels. Once established, the protocols developed here may be applied to outbreaks of other diseases.

The emergence of drug resistance has rendered most clinically used drugs ineffective. There is, therefore, the need to discover new, safe, effective and novel chemo types with new modes of action. This project seeks to continue medicinal chemistry efforts on a chemical class identified from the MMV Pathogen box to develop an early lead with in vivo antitubercular/antimalarial activity as a proof-of-concept

This project builds on intra-African and international (pharmaceutical companies, academic institutions) collaborations to identify medicinal chemistry starting points from the screening of target-based chemical libraries against the causative agents of malaria, leishmaniasis and Human African Trypanosomiasis (HAT), also known as sleeping sickness.

This research proposes to apply new protocols that have been developed at RUBi combined with traditional computational drug discovery approaches to further improve our understanding of rational drug discovery in the context of tuberculosis and malaria. Additionally, where applicable, it aims to identify novel hits from African natural products against these diseases as screening of them may lead to the development of novel pharmaceutics in Africa

The propensity of malaria parasites to develop resistance motivates the ongoing discovery and development of antimalarials with new modes of action. Heinrich Hoppe’s research focuses on employing novel bioassays to find inhibitors of Arf1, a GTPase that regulates protein secretion, in order to validate it as an antimalarial drug target

Achim Hoerauf of IMMP in Germany will apply artificial intelligence (AI) to speed the development of treatments for onchocerciasis, which is an infectious disease commonly known as River Blindness caused by a parasitic worm. The parasites are spread by affected blackflies, and the worm larvae accumulate in the skin and eyes, causing irritation and sometimes blindness. Nearly 21 million cases occur each year, and 99% of affected people live in Africa. The drug currently used for treatment kills only worm larvae, and studies are ongoing to identify more effective drugs that target adult worms. However, evaluating these drug candidates requires manual analysis using microscopy of samples of irritated skin from patients after treatment. This process is time consuming and slows drug development. To address this, they will use samples that have already been manually annotated to train an AI system to automatically analyze future samples to recognize worm body parts, gender, vitality, and stage of development. Once established, the AI system will be tested with samples from a new clinical trial - tissues from patients treated with the new drug will be analyzed in parallel by human and computer. Once optimized, the AI system will take over the analysis, and the much slower human analysis will only be needed as a quality control system.

Cara Brook, Jean-Michel Héraud, and Soa Fy Andriamandimby of the Pasteur Institute in Madagascar, and Jessica Metcalf of Princeton University in the U.S. will establish metagenomic next generation sequencing (NGS) in Madagascar to analyze samples from undiagnosed fever patients and from bats to identify bat-derived viruses that cause human infectious diseases and help develop new diagnostics. It is estimated that up to 75% of emerging human diseases are derived from an animal reservoir. The majority of these zoonoses emerge in low-resource settings in equatorial regions likely due to living conditions and limited access to healthcare. Madagascar has long been geographically isolated, and Madagascan fruit bats are considered potential major sources of several different zoonotic diseases such as Ebola. However, identifying disease-causing viruses using traditional diagnostic methods requires the development of targeted assays and is laborious and inefficient. In contrast, metagenomic NGS is a powerful diagnostic tool that can simultaneously assay many viruses and identify new ones. Their institution serves as a national reference laboratory for several diseases in Madagascar, and to help develop local next generation sequencing and data analysis capacity, they will pilot the approach with a subset of 380 human clinical samples already collected. They will then process an additional 410 human samples for NGS library preparation, sequencing, and pathogen analysis in Madagascar.

Carol Sze Ki Lin of the City University of Hong Kong and Srinivas Mettu of the University of Melbourne in Australia will develop a new, low-cost bioreactor system to mimic the human gut and facilitate simultaneous growth of multiple bacterial strains with diverse growth requirements. A healthy mixture of bacteria in the human gut is essential to overall health, and live biotherapeutics could be used to restore this population in infants whose gut microbiota has been damaged by malnutrition. Manufacture of these therapeutics is difficult and expensive: the human gut contains multiple strains of bacteria with diverse environmental and nutritional growth requirements, and designing a bioreactor to accommodate such variation is difficult. They will create stratified growth zones within one reactor based on immobilization of the bacteria on a low-cost, biodegradable plant-based cellulose hydrogel. They will alter the porosity and surface chemistry of the hydrogel to create variations in pH and oxygen and nutrient levels within the reactor to simulate the human gut from stomach to rectum. This will facilitate the simultaneous growth of ten bacterial strains with diverse growth requirements, first in a lab setting and later on a commercial scale.

Nguyen Thanh Hung and colleagues in Children’s Hospital 1 in Vietnam will implement next generation sequencing to identify the diverse viral causes of encephalitis in children in Vietnam and develop more accurate and rapid diagnostics to improve clinical outcomes. Encephalitis is an inflammation of the brain commonly caused by viral infection and is a major contributor to childhood morbidity and mortality worldwide. Treatment requires rapid diagnosis so that the appropriate antimicrobial therapy can be administered. However, traditional diagnostics are inadequate, and identify less than half of cases. Further confounding accurate diagnosis is the emergence of new and diverse pathogens that cause encephalitis. Next generation sequencing could overcome these challenges by more rapidly sequencing viral nucleic acids than previous methods and at lower costs. They will test this at their 1,600-bed hospital located in Ho Chi Minh City in Vietnam with samples from around 150 children presenting with encephalitis and compare the results with routine diagnostics. Overall, they aim to identify the major causes of encephalitis across Vietnam, map them temporally and spatially, and characterize disease outcomes.

William Kunin of the University of Leeds in the United Kingdom will develop methods to monitor agricultural pest outbreaks in Africa using data from dual-polarization weather radar. Pest infestation is responsible for up to 50% of pre-harvest crop loss in Central Africa, and control depends on the ability to monitor local pest outbreaks and movement over large areas – a difficult and expensive task. Sophisticated dual polarization Doppler weather radar is designed to detect airborne objects like rain and hail. However, because it is sensitive to size and shape, it can also be used to detect specific types of insects, including crop pests, and algorithms exist to separate the different signals. They will use micro-CT scans to create three-dimensional models of pests common to Rwandan crops and conduct simulations to predict radar data patterns for swarms of particular pests at varying densities. Through collaboration with the Rwandan meteorological service, they will analyze radar data to identify pest outbreaks and their movements across the country. Once established, the methods will be applied across Africa to provide a low-cost, advanced warning system for crop protection.

Nico Vandaele and Catherine Decouttere from the Katholieke Universiteit Leuven in Belgium along with workers at the St. Francis of Assissi Community Dispensary in Kenya will develop a decision support tool for community workers who vaccinate children that incorporates the diverse characteristics and needs of the local parents and caregivers to help them improve vaccination uptake. The diverse characteristics of caregivers in a specific catchment area, such as their profession and household situation, affect the challenges they face when seeking health care and the solutions needed to overcome them. They plan to develop a tool for community workers to map these characteristics so that they can exploit them to provide a better health service, such as by tailoring appointment times, arranging transport, and sending reminders. This will enable them to work more efficiently. They will build a paper-based prototype in collaboration with community workers and pilot test the tool and validate maps of selected communities.

Muhammad Imran Nisar, Furqan Kabir, Fyezah Jehan, and Syed Asad Ali of Aga Khan University in Pakistan will employ a metagenomic sequencing approach to better identify bacterial and viral infections, including those caused by novel pathogens, in newborns and infants in Pakistan. Pakistan has the highest neonatal mortality rate in the world. Almost one third of neonatal deaths are thought to be caused by an infectious disease, but accurate diagnosis is challenging in low-resource settings. In a recent study, despite using elaborate detection methods, only 28% of suspected infectious cases could be confirmed. In addition, novel pathogens escape detection because conventional microbiological methods are limited to detecting a predetermined selection. To overcome these limitations, they will establish a metagenomics approach for next generation sequencing with the cloud computing IDseq software to identify the infectious pathogens. They will sequence and analyze 150 archived blood and nasopharyngeal specimens from a previous study and additionally analyze blood and swab samples to be collected from sick newborns and infants with suspected infections over a six-month period at a primary healthcare center in a peri-urban community in Karachi, Pakistan.

Ricardo Valladares of Siolta Therapeutics in the U.S. will develop a low-cost method to manufacture large quantities of mixed populations of bacteria for use as biotherapeutics to restore a healthy population of gut microbes in infants. A diverse population of bacteria in the infant gut is essential for health, but malnourishment and antibiotics can destroy microbial diversity and cause metabolic and immune problems. Gut health may be restored by treatment with a consortium of bacterial strains. However, mass production of these strains is challenging as many have complex nutritional and environmental growth requirements. To date consortium microbes have been cultured individually, resulting in a costly, small-scale process. They will first study bacterial populations in healthy infant guts to determine what species are required and the combinations that co-exist naturally. To support their growth in vitro, they will optimize a vegetable-based culture media composed of low-cost, widely available food staples, and then apply bioreactor technology used by commercial breweries to manufacture large quantities of the microbial mixture at low cost. Together, these approaches could help make microbiota-based biotherapeutics accessible in low- and middle-income countries.

Benedict Mongula of the University of Dar es Salaam in Tanzania will analyze two apparently conflicting national agricultural policies centered on either large-scale agriculture or smallholder farmers and determine how to combine them to benefit all stakeholders for inclusive agricultural transformation. Agriculture is central to the Tanzanian economy, yet its impact is limited by a lack of infrastructure, education, and market access. Current agricultural policy is shaped by two conflicting approaches: large-scale agriculture under the Southern Agricultural Growth Corridor (SAGCOT), driven by wealthy foreign investors; and smallholder farming under the government-led Agricultural Sector Development Programme (ASDP). SAGCOT has been criticized for removing land from rural farmers and increasing poverty, while ASDP has many projects in place but has not demonstrated significant impact on the industry. They will investigate how to link the two approaches for inclusive agricultural transformation – improvements to the industry that protect all stakeholders, regardless of social class, culture, gender or age. Through consultation with participants, including government officials, investors and ordinary citizens, they will analyze the two approaches, identify which of the large-scale agricultural investments are most consistent with inclusive transformation, and determine to what extent both SAGCOT and ASDP address the needs of the diverse population. The new policy framework will be presented in the form of a journal article, stakeholder meetings, and workshops.

Melanie Bannister-Tyrrell of Ausvet in Australia will create an SMS-based communication system for farmers in Kenya to anonymously report crop disease and pest infestations and generate surveillance data to minimize crop loss. Pest infestation and disease cause substantial crop losses each year. In many low-income countries, farmers do not report disease to local agricultural authorities because they fear their crops will be destroyed without compensation. Yet information on the presence and spread of pests is needed to inform decisions on planting and control measures. They will optimize the design of an SMS interface for anonymous reporting using SMS codes for diseases and pests. The system will be piloted in one county, leveraging existing users and community volunteers to recruit new users. Reports will be used to estimate the local prevalence of pests and disease using modified small-area statistics. If a credible threat is detected, all users will be alerted. The system will improve connection and trust between farmers and agricultural extension workers and improve surveillance.

Kindie Tesfaye-Fantaye of the International Maize and Wheat Improvement Center in Mexico will develop a computational model that incorporates the variable characteristics of households and farms to better predict the outcomes of agricultural interventions in Ethiopia in order to inform policy choices. Agriculture is central to the Ethiopian economy; it accounts for almost 50% of the gross domestic product and 80% of total employment, yet the industry struggles with limited infrastructure and environmental challenges. Prioritization of agricultural policies has generally relied on analysis of past observations, which are static and tend to ignore variability. They will build and validate an agent-based model that uses current data to model future outcomes, and input biophysical (e.g., soil, climate) and socioeconomic (e.g., household characteristics, land use, access to market and financing) data. They will test their model by comparing five candidate policy options under consideration by the government in terms of impact, effectiveness, efficiency, and inclusiveness. Once established, the model will be scaled up for policy intervention in other Sub-Saharan countries including Tanzania and Nigeria.

Amit Lal of Geegah LLC in the U.S. will develop battery-powered ultrasonic imagers to collect and wirelessly transmit high-resolution images of soil and airborne pests for the early detection of crop threats across large farming areas in rural Africa. Crop losses due to pest infestation negatively impact both food security and local economies. Damage caused by nematodes is particularly difficult to detect because the symptoms visible above ground are not unique and are often incorrectly attributed to deficiencies in soil nutrients or moisture. Currently the only way to test for nematodes is through root and soil samples taken after harvest, when it is too late to respond to an infestation. They will integrate complementary metal-oxide semiconductors (CMOS) with GHz ultrasonic imagers to detect nematodes and survey soil properties over large areas to detect infestations before crop damage occurs and transmit the data in real time. They will optimize the sensor technology in a controlled laboratory setting to maximize sensitivity and specificity and minimize power consumption and then transition to a farm setting for incorporating data transmission via the radio frequency wireless network.

Aart Van den Beukel of Safi Sana in the Netherlands will enable digital monitoring of the entire waste and sanitation supply chain to improve quality control, reduce costs, and help communities transform waste into resources such as agricultural and energy products. Poor sanitation and waste management can drive poverty and disease, but it is difficult to monitor in low-resource settings. The sanitation and waste industry can also provide unique opportunities for social and economic benefit when communities are given access and support. They will install data collection systems to monitor all elements of waste processing such as GPS data from waste truck drivers to help improve efficiency and reduce contamination and costs. These data will be provided on a tailored, computer-based platform to relevant stakeholders and local communities, along with education and awareness training to encourage new waste-converting enterprises.

Christine Lamanna and Todd Rosenstock of the World Agroforestry Centre in Kenya will develop a strategy that combines local knowledge and a Bayesian network model to prioritize agricultural policy using Tanzania’s Agriculture and Food Security Investment Plan as a case study. Agriculture is responsible for nearly one third of Africa’s gross domestic product, yet productivity suffers from limited infrastructure and lack of access to markets and financing. Many policy options exist to stimulate agricultural transformation, however countries struggle to prioritize them and progress is limited. They will develop a Bayesian network to model the cost and risks of implementing specific agricultural policies as well as the economic, social and environmental benefits. Using the Tanzanian plan as a case study, they will develop a data-driven model for policy prioritization that incorporates risk (financial, climate, logistical, political) and reflects stakeholder perspectives to create a sense of ownership over the process. This strategy will allow for direct and transparent comparison of diverse policy options and provide decision-makers with clear prioritization information.

Anne De Groot of the GAIA Vaccine Foundation in the U.S., along with Mika Kunieda of Keio University in Japan and Eliza Squibb and Julia Shivers of ZTwist Design in Boston, will design and distribute printed fabric baby-wraps that use West African iconography to represent the infant vaccination schedule to new mothers in Niger to encourage vaccine completion and reduce child mortality in West Africa. Due to low literacy rates among women in Niger and lack of adapted vaccination information, there are high dropout rates for pediatric vaccination and 40% of Nigerien children are unprotected from measles. In partnership with the Niger Ministry of Health's Social Mobilisation team, the Vaccine Calendar Baby-Wrap addresses key barriers to vaccination coverage in Niger by leveraging local textile traditions to bridge the information gap between the Ministry of Health's efforts to promote free vaccination and mothers’ understanding of the schedule. This visual tool provides mothers with a personal understanding of the vaccination series while enabling them to become health advocates who can educate their peers. As a practical educational intervention for children's health, the Baby-Wrap serves as a strategy for improving information equity in populations with low literacy.

Jignesh Patel of the Indian Institute of Technology Hyderabad in India will increase vaccination coverage among low-income urban populations in India by designing a mobile vaccine service including a smartphone-based management application that provides customized vaccinations at homes and schools and at lower cost. Vaccination offers excellent protection against many diseases, however coverage is low among low-income populations in urban centers: immunization at private clinics is unaffordable, and public clinics have longer wait times leading to lost wages. They will develop an affordable, convenient, in-home vaccination service and a smartphone-based management application to schedule appointments, automate routes, record vaccination data and collect customer feedback. They will pilot test their approach among low-income populations in three cities by recruiting and training healthcare workers and drivers and evaluating its effect on vaccination coverage.

Suparna Kalghatgi of Bempu Health Private Limited in India will introduce an online, personalized messaging platform to educate parents and caregivers on immunization and address their concerns to increase vaccine compliance and decrease childhood morbidity in India. Despite the existence of a universal immunization program, there are 7.4 million unvaccinated children in India, and only 65% of infants are fully vaccinated by age one. Limited caregiver knowledge is a key barrier to vaccination: parents must understand why vaccination is important and know the prescribed immunization schedule and where vaccine services are available. They will develop BabyOnTrack - an online support system that runs through WhatsApp, a free online messaging application widely used across India, to provide daily communications including practical infant health education, personalized counseling, and vaccination reminders, to new parents. They will test their approach by running small pilots in private and government hospitals where babies will be enrolled as they are discharged after birth and evaluate its effect on parent engagement and vaccination compliance. The program will also help health centers better manage vaccine supplies to reduce waiting times, which will further improve the vaccination experience for caregivers.

Sumeet Singh of Onekeycare Ventures Private Limited in India will develop a voice-based platform to store immunization records and improve caregiver attitudes toward vaccination in rural India. Adherence to a standard immunization schedule is essential to reduce childhood death from vaccine-preventable diseases. However, many mothers in rural and remote India are unaware of the recommended childhood immunization schedule and how crucial it is to follow it. They will develop a voice-based platform for health workers to easily update immunization records and to provide automated reminder calls to caregivers when vaccinations are due. They will also develop voice-based games to educate caregivers on hygiene best practices and the importance of immunization for health and provide the opportunity to earn points that can be redeemed for rewards. Importantly, these systems are independent of internet or smartphone access, which are largely absent in remote areas. They will test their platform in a target area by training a group of health workers and influencer caregivers and evaluating its effect on awareness and behavior.

Rachel Sklar of Pit Pumpers Ltd. in Rwanda will develop an interactive SMS messaging platform to increase community use of companies to safely empty pit latrines in Rwanda by using communication to increase demand and thereby decrease cost and wait-times for service calls. Pit latrines, used by nearly two billion people worldwide, must be emptied regularly to avoid public health risks associated with fecal contamination of groundwater. Companies in Rwanda provide an emptying service with cost-saving opportunities when several households book together. However, this is more difficult to arrange for users in remote areas, and households may resort to unsafe dumping. They will improve coordination of service requests among remote communities by using local shop owners (duka agents) to identify households needing an emptying service. These leads will be followed up with targeted SMS marketing, and the agents paid a commission for those converted to customers. The interactive SMS messaging platform will provide households with a referral code to invite neighbors to join the service call, and the resulting cluster of customers pay a reduced rate for emptying. It will further include educational messages explaining the negative impacts of manual dumping of fecal sludge to promote behavior change. They will develop the platform and test their approach by training agents in Kigali, Rwanda.

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.

Thushan de Silva, Abdul Karim Sesay, Helen Brotherton, and Beate Kampmann of the Medical Research Council in the United Kingdom will locally implement equipment and methods for next generation sequencing of a range of clinical sample types to detect infectious pathogens in hospitalized neonates in low-resource settings. Almost three million children under five years old die each year in sub-Saharan Africa, which is the highest rate globally. A substantial proportion is likely to be caused by pathogenic infections, including multi-drug resistant organisms. However, defining the etiology of neonatal infections, which is crucial for timely and effective treatment and to block disease spread, remains challenging in countries with limited resources. They will test the potential for next generation sequencing to overcome this challenge by conducting a pilot study using clinical samples from around 30 hospitalized neonates within an ongoing clinical trial in a low-resource setting in West Africa. They will optimize protocols on site for detecting pathogens from a range of sample types and develop methods to specifically detect relevant anti-microbial resistant strains. They plan to use the study to install a network of sites across the region that can perform next generation sequencing and share the resulting large datasets with other sites.

Ophelia Venturelli of the University of Wisconsin-Madison in the U.S. will study the growth kinetics and microbial interactions of a synthetic bacterial community in order to optimize bioreactor design and produce large quantities of mixed cultures at low cost. Mixed microbial populations are used to reconstitute the healthy gut microbiota in infants and children who have suffered malnutrition. However, affordable, large-scale production of microbial communities for biotherapeutics is challenging, in part because of poor understanding of how the growth and viability of individual strains are impacted by environmental factors and the presence of other microbes. To address this, they will build a dynamic model of a synthetic 12-member community that mimics the functional and phylogenetic diversity of the human gut microbiome and monitor its stability and growth over time and in response to variations in culturing parameters. This information will be used to predict community growth and stability in bioreactors, which will then be tested. Their approach is low cost, scalable to industrial-sized bioreactors, and can be generalized to other microbial communities relevant for human health.