The Grand Challenges family of initiatives fosters innovation to solve key health and development problems. See below for Grand Challenges and related grant opportunities.

Grant Opportunities

Grand Challenges Global Call-to-Action

Building Malaria Modeling Capacity in Sub-Saharan Africa

<h2>Background</h2>
While mathematical modeling approaches have been used to understand malaria epidemiology and the potential impact of antimalarial interventions for some time, National Malaria Control Programs (NMCPs) across sub-Saharan Africa are showing a growing interest in working with modeling units to shape their National Strategic Plans and Global Fund applications, as well to evaluate the ongoing impact of control programs. Furthermore, R&amp;D partners in the malaria space are also increasingly working with modelers as part of the product development process, using quantitative insights to shape target product profiles, plan trials, and understand the market for a given product.
At present, many of the malaria modeling units contributing to these efforts are based in academic institutions in the Global North. At the Bill &amp; Melinda Gates Foundation, we believe that having local modeling expertise embedded within or easily accessible to NMCPs will improve programs' uptake of modeling as a strategic planning and evaluation tool, ultimately leading to improved data-driven decision-making by NMCPs. However, for this vision to be realized, the malaria modeling ecosystem across sub-Saharan Africa needs to be strengthened.
<h2>The Challenge</h2>
This RFP seeks innovative approaches to building a stronger malaria mathematical modeling ecosystem in sub-Saharan Africa. We are looking for 1 to 3 years projects that will achieve one or more of the objectives below:
<ul><li>Increasing the number of Ph.D.-trained mathematical modelers with malaria expertise based at sub-Saharan African institutions</li>
<li>Improving NMCP's understanding of and engagement with modeling approaches as a tool that can support strategic planning and/or evaluation work</li>
<li>Connecting malaria Product Development Partners (PDPs) with sub-Saharan African modelers</li>
<li>Bringing together discrete modeling units across sub-Saharan Africa to share expertise</li>
<li>Improving modelers' access to timely, high-quality data</li>
</ul>Funding level: up to USD $1,000,000 per year for each project, with a grant term of 1 to 3 years depending on the scope of the project.
We are looking for proposals that:
<ul><li>Are led by a PI based in sub-Saharan Africa (other global partners may be included). Teams comprising multiple African institutions will be given preference over applicants from single institutions</li>
<li>Demonstrate at least 80% of the funding is going to sub-Saharan African institutions</li>
<li>Focus on improving geospatial and/or mathematical modeling capacity for malaria (other related diseases may be included; for example, modelers may also work on vector-borne neglected tropical diseases)</li>
<li>Articulate how the project will lead to impact in the near-term and how those benefits will be sustained past the lifetime of the project</li>
<li>Demonstrate engagement with local and/or regional malaria decision-makers</li>
</ul>We will not fund proposals that:
<ul><li>Do not demonstrate that the majority of the work proposed will be undertaken by African scientists at African institutions</li>
<li>Do not demonstrate a pathway to sustainable impact</li>
<li>Do not engage local and/or regional malaria decision-makers</li>
<li>Do not focus on expanding geospatial or mathematical modeling capacity</li>
</ul>

While mathematical modeling approaches have been used to understand malaria epidemiology and the potential impact of antimalarial interventions for some time, National Malaria Control Programs (NMCPs) across sub-Saharan Africa are showing a growing interest in working with modeling units to shape their National Strategic Plans and Global Fund applications, as well to evaluate the ongoing impact of control programs. This RFP seeks innovative approaches to building a stronger malaria mathematical modeling ecosystem in sub-Saharan Africa.

Digital Health Services for Pregnant Women to Support Antenatal Risk Stratification in Sub-Saharan Africa

<h2>Background</h2>
Digital health is revolutionizing the landscape of global healthcare. Solutions such as telemedicine, electronic medical records, and digitally enabled devices help to provide accessible, high-quality care around the world. These services have the potential to be especially impactful in low-income areas where care provided by highly trained individuals is not as common. Digital health can empower individual patients by enabling them to manage their own healthcare journeys. It can also drive systemic change through affordable, wide-reaching services that reduce the strain on healthcare professionals and facilities in low-resource settings.
The Maternal, Newborn &amp; Child Health Discovery &amp; Tools team believes that the ability to leverage digital health systems can enhance care and reduce adverse birth outcomes in low-and-middle income countries. One area of focus is the stratification of pregnancy risk to ensure that patients are put on the appropriate care pathway. This can enable tertiary facilities to focus their constrained resources on high-risk pregnancies, while low-risk pregnancies are managed at lower levels of care. Antenatal Risk Stratification (ARS) is a portfolio of devices and data that predicts a pregnant woman’s risk of experiencing adverse birth outcomes in early pregnancy. Building and implementing an ARS solution requires three key steps. 1) Collect data on pregnant women (e.g., patient history, clinical data, and diagnostic results with an emphasis on ultrasound and hemoglobin assessment). 2) Use data as inputs in a robust, AI decision model that accurately predicts a pregnant woman’s risk of adverse birth outcomes. 3) Support clinical decision making by using the predicted risk to pre-emptively triage patients across different levels of the healthcare system. Through this process, ARS would enable more efficient resource allocation by sending the riskiest patients to high-level facilities, while referring low-risk patients to community or public health centers. ARS would aim to improve the quality of care for pregnant women by ensuring that they can receive the right level of care.
Developing and delivering an ARS solution will require a robust digital backbone including tools for data collection, automated analytics, and platforms that connect to patients and healthcare providers. Before ARS can be successfully implemented, a landscape of digital health devices, partners, and services must be put into place.
<h2>The Challenge</h2>
We seek patient-facing digital health services for pregnant women that have been developed and are actively being provided in sub-Saharan Africa (SSA). We will consider proposals for services that can support or contribute to our ARS vision via digital applications. Types of services that we would consider include:
<ul><li>Engagement: Services that increase the participation of pregnant women in digital healthcare. (E.g., a platform to provide group antenatal care or a telemedicine platform that allows pregnant women to send medical questions to doctors by text)</li>
<li>Adoption of existing platforms: Services that leverage existing digital systems/platforms to support pregnant women (e.g., contacting patients about care or scheduling using an existing messaging app like WhatsApp)</li>
<li>Data collection: Services that collect data from pregnant women that could support clinical care. (E.g., a mobile app that allows pregnant women to track their pregnancy through metrics such as weight and fetal movement)</li>
<li>Algorithm development: Services that use data from pregnant women to assess or make predictions about their health (e.g., a web-based tool where pregnant women can input information to receive an automatic assessment on whether or not they should see a doctor)</li>
</ul>We seek projects that will help us develop and deliver an ARS solution in sub-Saharan Africa. Proposals should endeavor to build upon existing solutions to help support ARS. Potential options include (but are not limited to):
<ul><li>Researching implementation methods for a solution (e.g., conducting market research for an existing app that supports one or more of the objectives outlined above)</li>
<li>Expanding the scope of a solution (e.g., translating a web-based app to mobile platforms to increase engagement)</li>
<li>Adding features/functionality to a solution (e.g., adding cloud-based data collection to a mobile app)</li>
<li>Improving the delivery of a solution (e.g., integrating a mobile app into an existing clinical health system)</li>
</ul>Funding level: up to USD $500,000 for each project, with a grant term of 6 to 24 months depending on the scope of the project.
We will consider solutions that are:
<ul><li>Developed and/or actively supported in sub-Saharan Africa (note: development/active support does not include cases when groups outside of SSA are testing their solutions on users in SSA)</li>
<li>Delivered in sub-Saharan Africa</li>
<li>Providing pregnancy-related services</li>
<li>Offered directly to pregnant women (i.e., patient-facing)</li>
<li>Currently available for use and interested in expansion</li>
<li>Serving a substantial and active user base in sub-Saharan Africa (e.g., more than ~100 users)</li>
<li>Digitally integrated (i.e., have a strong, technical component)  </li>
<li>Clearly linked to the development and implementation of ARS</li>
<li>Reaching women in both rural and urban areas in sub-Saharan Africa (note: this is a preference, not a requirement)</li>
</ul>We will not consider solutions that are:
<ul><li>Exclusively developed and maintained outside of sub-Saharan Africa</li>
<li>Currently unavailable to pregnant women in sub-Saharan Africa</li>
<li>Unrelated to pregnancy</li>
<li>Targeted primarily towards health-care providers (e.g., doctors or health centers)</li>
<li>Still in a proof-of-concept state (e.g., an idea for an app that has not yet been developed)</li>
<li>Still being beta tested (e.g., an app that is not being offered to the general public)</li>
<li>Primarily analog (i.e., are not digital services)</li>
<li>Unrelated to the development and implementation of ARS</li>
</ul>

Digital health is revolutionizing the landscape of global healthcare. Solutions such as telemedicine, electronic medical records, and digitally enabled devices help to provide accessible, high-quality care around the world. We seek patient-facing digital health services for pregnant women that have been developed and are actively being provided in sub-Saharan Africa (SSA). We will consider proposals for services that can support or contribute to our ARS vision via digital applications.

Innovations in Eliminating Neglected Tropical Diseases

<h2>Background</h2>
The elimination of neglected tropical diseases (NTDs) is feasible but its progress is hindered by the suboptimal implementation of available interventions. Challenges faced by NTD programs can range from poor targeting of interventions, low treatment coverage among specific sub-groups, and lack of surveillance systems that can meet the sensitivity and specificity requirements for the endgame of disease elimination. These challenges could be addressed by the development of innovative solutions but also by the application of existing tools and technologies which are effectively used by other health programs, including polio, malaria and immunization. We see an opportunity to identify and demonstrate feasibility of innovative solutions and new applications of existing technologies to address the challenges faced by national NTD programs in mapping, targeting, disease and vector surveillance, and other endgame strategies.
<h2>The Challenge</h2>
We have partnered with <a href="https://twitter.com/kikundi_cop" target="_blank" rel="noopener noreferrer">Kikundi</a>, a community of practice for NTD program managers in Africa, to identify areas for transformational innovation in service of national NTD programs. We invite proposals that will address one or more of the following areas:
<ul><li>Methods to rapidly map NTDs, including integrated mapping</li>
<li>Methods to better target existing interventions</li>
<li>Strategies to target subgroups routinely missed by programs</li>
<li>Approaches to vector surveillance</li>
<li>Integrated surveillance that leverages other health surveillance platforms</li>
<li>Strategies for post-elimination surveillance, including cross-border surveillance.</li>
</ul>Funding level: up to USD $200,000 for each project, with a grant term of up to 18 months depending on the scope of the project.
We are looking for proposals that:
<ul><li>Are led by institutions, which could be ministries of health, based in sub-Saharan Africa or South Asia (other global partners may be included but at least 80% of funding should go to sub-Saharan Africa or South Asia)</li>
<li>Demonstrate partnership with national NTD programs</li>
<li>Propose innovations and applications that are scalable</li>
<li>Have a plan for how the proposed solution would be tested or validated, and report its impact on NTD elimination goals and program efficiency</li>
</ul>We will not consider proposals that:
<ul><li>Do not include a lead investigator from an NTD-endemic country</li>
<li>Do not consider explicit collaboration with endemic country governments, government NTD programs and/or partners</li>
<li>Focus on current NTD intervention delivery or piloting of tools already used by NTD programs</li>
<li>Do not outline a clear path forward to scale and sustainable impact</li>
</ul>

The elimination of neglected tropical diseases (NTDs) is feasible but its progress is hindered by a paucity of effective intervention tools, the suboptimal implementation of existing tools, or both. We see an opportunity to identify and demonstrate feasibility of new applications of cutting-edge technologies used by other disease areas to address the challenges faced by national NTD programs and accelerate the elimination of NTDs.

Strengthening Data Science Capacity and the Ecosystem: Enable Data-Centered Public Health Interventions

<h2>Background</h2>
The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices. The supporting infrastructures and capacities through which researchers access and analyze these data, however, vary greatly. For several reasons, researchers in the Global South often are skeptical of broad calls for data sharing when they are left out of the analysis and results interpretation. Additionally, deploying “one-size-fits-all” analysis tools and governance processes across different regions from where they were created may produce poor analysis results and could even lead to misleading interpretations and conclusions when scientists analyze data without insight from the context in which it was collected.
As such, data science infrastructure and capacity needs to be proximally focused in the areas where the data emerge, to produce solutions that enable strong evidence-based practices to guide health interventions.
<h2>The Challenge</h2>
Collecting, sharing, analyzing and interpreting health-related data are essential to the planning, implementation, and evaluation of public health practice as well as enabling advanced knowledge for heath emergency preparedness. Many data resources are not reusable if they have not been sufficiently documented and contextualized, or appropriately collected and licensed for use, particularly in cases where secondary analyses are planned that were not originally anticipated when the data were originally generated.
The desired outcome of this request for proposals is development of tools and processes that are "fit-for-purpose" to facilitate collaboration among researchers in low- and middle-income countries. This effort is focused on filling data science gaps and challenges whose solutions will address global health problems while fostering multi-disciplinary collaborations among researchers. We are specifically interested in funding proposals led by early and mid-career researchers. We expect each primary applicant to have a senior scientific advisor who would commit to provide scientific and technical guidance and mentorship to the team and facilitate connections with international networks as needed.
Specifically, the objectives of the challenge will be to:
<ul><li>Develop and improve foundational tools, standards and protocols that enable data-centered health research, interventions, and health surveillance</li>
<li>Improve access to curated and linked datasets for research and training purposes</li>
<li>Enable South-South data-centered collaborations and build on and strengthen existing initiatives and ecosystems</li>
<li>Enhance Data Science infrastructure, analysis capabilities and local leadership to address public health problems</li>
<li>Foster innovation with health data in the interest of their respective local communities</li>
<li>Bridge the gap between data-centered research and policy through collaborations and design of appropriate data science communication tools and channels</li>
<li>Increase health data reliability and enable reuse by developing tools and processes to curate, standardize and aggregate data</li>
<li>Improve data collection, sharing, governance, regulatory compliance, and analysis processes to enable data-centered public health research</li>
</ul>What We Are Looking For
We will be developing a coordinated program in order to support these objectives. This will include cross-cutting cores which will provide specific functions to the program as well as individual research projects focusing on one of more specific disease areas.
Applications are invited from researchers working in healthcare, academia, industry, or research foundations, and will be welcomed from researchers based in any of the following regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. Each proposal should be submitted by one primary applicant, but the awards require the participation of at least two research institutions from the regions listed above and not both in the same country.
Program structure
<img alt="Strengthening Data Science Capacity and the Ecosystem Diagram" height="329" src="https://gcgh.grandchallenges.org/sites/default/files/images/picture1.png" width="540" />
Data governance and standards core
We seek to fund one data governance and standards core. This core would:
<ul><li>Support the research projects funded through this initiative including the capacity strengthening core</li>
<li>Leverage the funded research projects as use cases for development and refinement of data sharing processes and governance</li>
<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in low- to middle-income countries (LMICs) by developing processes to enable South-South collaborative analysis</li>
<li>Develop adapted standards or Standard Operating Procedures that address data sharing issues including anonymization and perturbation protocols</li>
<li>Develop re-usable tools, protocols, and standards to collect, clean, harmonize, and commonly structure individual data and meta-data related to one or many disease areas to increase health data reliability and enable reuse. New tools and process development should leverage and build on existing work</li>
<li>Develop data informed processes to overcome barriers to the collection of complete and accurate information; and challenges related to data and information governance and data reuse</li>
</ul>Capacity strengthening core
We seek to fund one capacity strengthening core to:
<ul><li>Support the research projects and the data infrastructure and governing core funded through this initiative</li>
<li>Bridge the gap between data scientists, modelers, regulators and policy makers, through trainings and best practices development</li>
<li>Develop re-usable training materials and handbooks</li>
<li>Lead and coordinate collaborative analysis efforts through different formats (workshops, hackathons, jamborees, etc.)</li>
<li>Lead capacity-building efforts targeting existing gaps in data science trainings in LMICs and building on existing initiatives (reusing available resources when available to avoid duplication of efforts)</li>
<li>Develop data-informed processes to overcome barriers to the collection of complete and accurate information, and challenges related to data and information governance and data reuse</li>
</ul>Data re-use research projects
We seek to fund two data re-use projects where research will focus on one of more specific disease areas:
<ul><li>Build data-driven public health platforms, tools and dashboards that guide policy making and public health interventions</li>
<li>Identify, use, curate and link clearly defined datasets (administrative, clinical, environmental, etc.) to address policy gaps or deliver insights and conceptual and/or technological leaps on Global Health research priorities including:
<ul><li>Maternal, Newborn and Child Health</li>
<li>Infectious diseases</li>
<li>COVID-19 and pandemic preparedness</li>
</ul></li>
</ul><ul><li>Proposals could focus on a specific disease area but need to demonstrate commitment to providing access to all research products including tools and processes developed</li>
</ul>Funding level 
Successful proposals will receive an award of up to USD $250,000 with an 18- to 24-month grant duration. While proposals require the collaborative participation of at least two investigators at institutions in different countries based in any of the regions listed above, proposals should be submitted by one primary applicant/institution. While restricted to one application per institution as the primary applicant, researchers may participate as collaborators in multiple collaborative applications.
Selected proposals will become part of a coordinated program aiming at Strengthening Data Science Capacity and the Ecosystem to Enable Data-Centered Public Health Interventions. We seek to build a network of researchers working together in close collaboration with the existing <a href="https://gcgh.grandchallenges.org/grants/JTdCJTIyc29ydCUyMiUzQSUyMmRlc2NlbmRpbmclMjIlMkMlMjJzb3J0RmllbGQlMjIlM0ElMjJkYXRlJTIyJTJDJTIybnVtYmVyUGVyUGFnZSUyMiUzQTEwMCUyQyUyMnBhZ2VOdW1iZXIlMjIlM0EwJTdE/JTdCJTIyeWVhcnMlMjIlM0ElNUIlNUQlMkMlMjJjaGFsbGVuZ2VzJTIyJTNBJTVCJTIyMTE3JTIyJTVEJTJDJTIydGVybXMlMjIlM0ElNUIlNUQlMkMlMjJpbml0aWF0aXZlcyUyMiUzQSU1QiU1RCUyQyUyMmxvY2F0aW9ucyUyMiUzQSU1QiU1RCU3RA==">Data Science Grand Challenges network</a> and its partners to build on lessons learned in different regions and further develop processes, tools and governance that maximize the impact of data-focused research studies, as well as to identify and address the global data-related challenges.
Successful proposals to be considered should: 
<ul><li>Be driven by a shared commitment to open science, data sharing, and building collaboration and analysis infrastructure to enable discoveries that will benefit people everywhere</li>
<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in LMICs, and involve patients and the wider public</li>
<li>Involve substantial collaboration between at least two research institutions in different countries encompassing Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. The suggested collaboration should be key to advancing the project goals and yield insights that are unlikely in the absence of the co-produced approaches. In addition, however, applications could include collaborations with institutions in other geographical areas</li>
<li>Have the potential to have impact on addressing public health issues within the proposed budget and timeframe of 18 -24 months</li>
<li>Data re-use proposals should use well-defined datasets that directly address the core research questions and have a clear governance plan and demonstrate how the data could benefit the broader community at a later stage.</li>
<li>Demonstrate a commitment to impact policies and the general public</li>
</ul>We will not consider funding for: 
<ul><li>Studies that aim to collect and generate new data through this funding</li>
<li>Proposals led by institutions not based in any of these regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil</li>
<li>Proposals that will involve a single research institution or solely involve collaborators from multiple institutions from the same country</li>
<li>Proposals that do not demonstrate a clear commitment to open science and making their findings, processes and/or tools developed accessible and reusable</li>
<li>Proposals that do not demonstrate how they would leverage existing work and networks</li>
<li>Proposals that do not demonstrate how they can benefit from and support other projects funded through this coordinated program</li>
<li>Proposals that are not accomplishable within the grant term</li>
</ul>

The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices. The supporting infrastructures and capacities through which researchers access and analyze these data, however, vary greatly. For several reasons, researchers in the Global South often are skeptical of broad calls for data sharing when they are left out of the analysis and results interpretation.

Strengthening Data Science Capacity and the Ecosystem: Enabling Data-Centered Public Health Interventions

Grand Challenges

Metagenomic Next Generation Sequencing to Detect, Identify, and Characterize Pathogens

<h2>Background</h2>
Next generation sequencing (NGS) has proven to be indispensable in the current SARS-CoV2 pandemic, from pathogen discovery to variant characterization to novel vaccine development in record-time. This approach to using NGS underscores the potential value of this technology and its application to many other diseases from Ebola to cholera to Pathogen X. Building upon the success of previous Bill &amp; Melinda Gates Foundation, Chan Zuckerberg Initiative (CZI) and Chan Zuckerberg Biohub (CZ Biohub) <a href="https://gcgh.grandchallenges.org/challenge/application-metagenomic-next-generation-sequencing-detect-and-identify-pathogens-round-22" target="_blank" rel="noopener noreferrer">Grand Challenges collaborations</a>, this year's opportunity will continue to offer exceptional opportunities for capacity building, on-going mentorship and the foundations of scientific, collaborative networks to detect, identify, and characterize pathogens. The ultimate goal will be to impact infectious disease decision making and public health by empowering local scientists with gold standard knowledge, experience and access to equipment and software.
Through this Grand Challenge, the Bill and Melinda Gates Foundation and Chan Zuckerberg Initiative will jointly offer funding for 10 scientific groups to explore the identification, characterization, and context of infectious disease in LMIC settings.
<h2>The Opportunity</h2>
One of the overwhelming lessons learned from the ongoing SARS-CoV-2 pandemic, has been the importance of being able to identify existing and emerging infectious diseases efficiently, in order to more effectively address and contain outbreaks, both locally and regionally. It has never been so important for clinicians and the global health community to have access to both accurate and timely estimates of disease burden and distribution. Traditionally, summaries of these data have been manually reported by national health ministries from regional clinical data, aggregated from local health centers. Local institutions, however, face multiple challenges to accurately assess the emerging needs of their community with current toolsets. For example, most clinical tests are based on common or easily detectable pathogens previously identified, which can miss occult and/or emerging pathogens. In addition, curation of large-scale pathogen data across multiple locations/platforms is time consuming and expensive, preventing facile integration of individual patient-level diagnostic data into a larger pathogen landscape on the regional and national level.
Over the last 18 months, we have seen how, when local institutions have the capacity – both at the hardware and software level - to detect emerging infections using pathogen sequencing technology, they are able to perform near-real-time data analysis that is rapidly translated into actionable public health decision making. At the heart of the ability to pivot to pathogen-specific sequencing during the pandemic, was the successful establishment of metagenomic sequencing platforms in low- and middle-resource settings, where there have traditionally been limitations in access or availability of the following essential components: a) equipment/reagents, b) specialized biochemical training, c) accurate reference pathogen sequence databases, and d) advanced computational analytics.
<h2>The Challenge</h2>
Recognizing barriers for adoption of next-generation sequencing in global health, the Bill &amp; Melinda Gates Foundation has partnered with the Chan Zuckerberg Biohub and the Chan Zuckerberg Initiative to enable populations in low- and middle-resource settings to benefit from cutting-edge pathogen detection, discovery and characterization. This partnership will provide highly specialized training in biosample preparation and sequencing to technical staff from awardee global health centers. Trainees will learn to use the open-source, open-access <a href="http://www.idseq.net/" target="_blank" rel="noopener noreferrer">IDseq</a> software developed by the Chan Zuckerberg Initiative (CZI) for the global health community to upload and analyze sequencing data, among other software such as Nextstrain. The BMGF-CZ Biohub-CZI partnership will therefore aim to provide selected applicants the benefits of onsite next generation sequencing and rapid pathogen detection to better understand their local pathogen landscape. We are also specifically seeking projects that endeavor to build upon their initial locally-focused effort to contribute to future data-informed decision-making at the population level via data sharing and pathogen data comparison across sites (multi-site studies will be prioritized).
This Grand Challenges (GC) award will support travel and accommodation of grantees to San Francisco for training at the CZ Biohub, and the following items: 1) <a href="https://gcgh.grandchallenges.org/sites/default/files/files/metagenomic_next_generation_sequencing_compatible_sequencers.pdf" target="_blank" rel="noopener noreferrer">a sequencer suitable for the global health environment</a><a href="#_bookmark1">[1]</a><a id="_bookmark0" name="_bookmark0"></a>, 2) a dedicated sequencing technician, and 3) sequencing reagents for the duration of the award. CZ Biohub and CZI training will include pilot analysis of samples from their home region during hands-on, intensive mentoring. This 2-week instructional period will include both biochemical sample preparation for sequencing and bioinformatic analysis using the IDseq software platform. Specifically, the hands-on training for bench scientists will include best practices and standards for sample processing, DNA &amp; RNA extraction, library preparation and data analysis on the global IDseq software platform. The combination of intensive training with molecular, capital equipment, reagent, and personnel support is intended to maximize the potential for sustainable, prospective, onsite analysis of patient samples upon return to the home site.
Molecular data generated at individual global sites is intended to be linked and automatically aggregated into IDseq for referencing by all participating clinics and associated sites. This method of local sequence analysis coupled with cross-site comparison could serve as a model for a process that would eventually lead to a network of pathogen detection nodes that could provide increased global transparency into regional pathogen distribution in an accurate and timely manner, and in ultimate support of a global pathogen surveillance network.
Examples of insights could include:
<ol><li>Investigating infectious etiologies for unknown medical cases</li>
<li>Identification of new pathogens</li>
<li>Detection of local outbreaks</li>
<li>Characterization of the local pathogen landscape, including vector-borne diseases</li>
<li>Assessment of emerging zoonotic disease, including wildlife and domestic animal reservoir sampling</li>
<li>Detection and classification of Antimicrobial Resistance (AMR) Markers, with potential for insight into antibiotic treatment discordance</li>
<li>Data-informed healthcare and resource allocation</li>
<li>Subsequent follow-up leveraging genomic epidemiology for public health, incorporating local epidemiological data and genomic analysis</li>
</ol>We are specifically looking for sites that believe in collaborative approaches and data sharing. Grantees will be expected to partner closely with scientists and engineers and provide feedback to help guide the cloud computing software’s development. Sites will also be expected to share data and collaborate with other labs in the network.
Funding level: up to USD $200,000 for each project, with a grant term of up to 24 months depending on the scope of the project.
We will consider sites that:
<ul><li>Provide a clearly scoped initial project that demonstrates why this technology will provide insights that are not currently possible.</li>
<li>Have space and capacity to run samples on a basic sequencer once provided with training, reagents, sequencer, and a dedicated computer. Have consistent access to electricity and the ability to upload data via the internet to a designated cloud server at least once a day.</li>
<li>Are willing to give product feedback and collaborate with an engineering team through scheduled video calls, email, or other messaging service. Are committed to open science, preprints, and data sharing.</li>
<li>Already have, or can rapidly acquire, necessary IRB approval for pathogen DNA and RNA sequence sharing from patients.</li>
<li>Are representative of larger geographic regions</li>
</ul>We will not consider sites that:
<ul><li>Do not articulate how they will leverage the value of next generation sequencing to impact health outcomes.</li>
<li>Are interested exclusively in Whole Genome Sequencing of human samples</li>
<li>Do not plan to share their pathogen data and discoveries.</li>
<li>Have extremely limited internet access.</li>
<li>Are limited to previously stored samples, without substantial potential for prospective sample collection.</li>
</ul>_____________________________________________
<a id="_bookmark1" name="_bookmark1"></a><a href="#_bookmark0">[1]</a> To ensure the sequencer continues to be used for purposes consistent with the foundation’s charitable objectives, we may request that funded applicants other than charities, universities, and similar organizations donate any sequencer purchased with grant funds to a charity or university, after the term of the project.

Recognizing barriers for adoption of next-generation sequencing in global health, the Bill &amp; Melinda Gates Foundation has partnered with the Chan Zuckerberg Biohub and the Chan Zuckerberg Initiative to enable populations in low- and middle-resource settings to benefit from cutting-edge pathogen detection, discovery and characterization. This partnership will provide highly specialized training in biosample preparation and sequencing to technical staff from awardee global health centers.