The Lacuna Fund is accepting proposals for the Antimicrobial Resistance (AMR) Program to support efforts to develop open and accessible datasets for machine learning applications related to understanding and mitigating the impacts of Antimicrobial Resistance (AMR) in low- and middle-income countries (LMICs) in Africa, Latin America and South and Southeast Asia.
Antimicrobial resistance is a major global health problem, affecting the health and survival of people across all genders as well as social and economic development. Efforts addressing AMR are likely to be extremely cost-effective and improve global public health security and human development. However, the impacts of AMR in low and middle-income contexts (LMICs) are more adverse due to a less integrated healthcare system, poor data availability, socio-economic determinants of health, among others.
This call will be focused on datasets in antimicrobial resistance. This includes an improved understanding of AMR, Infection Prevention and Control (IPC) efforts, access and development of new drugs, surveillance, diagnosis and the One Health approach, among many others.
Funding Information
The total pool available is approximately $1 million USD. They would like to fund projects in each of the target regions (Africa, Latin America, South and Southeast Asia) and anticipate supporting 3-4 smaller projects with budgets < $100k and 1-3 larger, more complex projects with budgets ranging from $200-300k. The Technical Advisory Panel will assess the feasibility and suitability of the budget as well as the linkage between the budget and grant narrative as part of the selection criteria.
Need
Lacuna Fund seeks proposals from qualified, multidisciplinary teams to develop open and accessible training and evaluation datasets for machine learning applications for AMR in LMICs in Africa, Latin America and South and Southeast Asia.
Proposals may include, but are not limited to:
Collecting and/or annotating new data;
Annotating or releasing existing data;
Augmenting existing datasets from diverse sources to fill gaps in local ground truth data, decrease bias (such as geographic bias, gender gaps or other types of bias or discrimination), or increase the usability of data and technology related to AMR in low- and middle-income contexts;
Linking and harmonizing existing datasets (such as across regions, time, pathogens, treatment, behavioral choices or climate factors).
The TAP sees a need for training and evaluation datasets that will help them better understand the impacts of AMR and develop interventions to mitigate these impacts. They seek datasets identified by local experts designed to address locally identified needs, so the following are illustrative examples only.
Datasets may include, but are not limited to the following:
Datasets to improve Infection Prevention and Control (IPC), gathering large amounts of immunization, access to Water, Sanitation, and Hygiene (WASH), transmission and outbreaks data. Specifically, lab consumption data could be used to design predictive models. These datasets would support better IPC, from a more equitable vaccination landscape to mapping access to water that is fit for human consumption.
Datasets for Drug Discovery, enabling ML support to drug development from plants, molecules, bacteria, and genetics data. These datasets would also contribute to high quality categorization of plants and pathogens for drug discovery. Informants mentioned leveraging indigenous knowledge for work related to medicinal plants as imperative in this endeavour.
Datasets for global AMR Surveillance and Anticipatory Approaches, tracking AMR globally, as well as genome-type datasets to identify the emergence of resistance strains. Real time datasets tracking outbreaks, from pre-surveillance approaches to early warnings, would also be helpful to apply ML to tackle AMR. The One Health approach is particularly relevant in this context.
Datasets that support diagnostics, such as datasets improving antibiograms and mapping disease and response, which would inform ML applications supporting diagnostics in resource-constrained settings. Better diagnosis would lead to prescribing the right antimicrobial at the right time, thus reducing resistance.
Datasets containing information on access to antimicrobials and lack thereof, supporting the prediction and alert of expected antimicrobials shortages. Datasets could also explore the use and misuse of antimicrobials, especially in contexts where antibiotics are most commonly acquired without prescription.
Datasets improving the basic biological understanding of pathogens and AMR, such as datasets that define and analyze factors influencing the occurrence of AMR among patients of a certain disease, linking records of patients at different time points, patient outcomes, and understanding the influence of environmental factors on AMR. Other examples include datasets labeling treatment data and follow up, and datasets on changing infectious diseases’ epidemiology and socio-economic factors. Chromosomal resistance and plasmid datasets would also be valuable. ESKAPE pathogens and pediatric formulations are considered high priority.
Across all datasets, gender-responsiveness, inclusion of key vulnerable groups, especially those bearing the brunt of AMR, as well as those at the intersections (gender, disability, age, socio-economic status, access to WASH, exposure to climate factors such as extreme weather events, etc.).
Eligibility Criteria
Lacuna Fund aims to make its funding accessible to as many organizations as possible in the AI for social good space and cultivate capacity and emerging organizations in the field.
To be eligible for funding, organizations must:
Be either a non-profit entity, research institution, for-profit social enterprise, or a team of such organizations. Individuals must apply through an institutional sponsor. Partnerships are strongly encouraged as a way to strengthen collaboration and maximize the benefits derived from the use of the datasets, but only the lead applicant will receive funds.
Have a mission supporting societal good, broadly defined.
Be headquartered in the country or region where data will be collected. The lead applicant must be based in a low- or middle-income country in Africa, Latin America and South and Southeast Asia, and must propose work in the same country, or a low- or middle-income country in the same region. Institutions based in other countries or regions can apply as partners of the lead institution. As stated above, only the lead applicant will receive funds.
Have all necessary national or other approvals to conduct the proposed research. The approval process may be conducted in parallel with the grant application, if necessary. Approval costs, if any, are the responsibility of the applicant.
Have the technical capacity – or the ability to build this capacity through a partnership described in the proposal – to conduct dataset labeling, creation, aggregation, expansion, and/or maintenance, including the ability to apply best practice and established standards in the specific domain (i.e., antimicrobial resistance) to allow high quality AI/ML analytics to be performed by multiple entities.
