DataHealers

DataHealers' project utilizes Geo-Tagged Natural Language Processing to combat antimicrobial resistance in Nigeria. It focuses on analyzing AMR-related data, employing geospatial mapping for visualization, and predictive analysis to tailor health policy interventions effectively, thereby enhancing the AMR response through data-driven insights.

The Team Lead for our solution is Bashirudeen Opeyemi, who is a Pharmacist with expertise in Machine Learning Engineering and experience in public health projects utilising GeoNLP.

Antimicrobial resistance (AMR) is a critical global health issue, impacting all countries regardless of income level. Globally, the rise in antibiotic resistance poses a significant threat, diminishing the effectiveness of standard antibiotics against widespread bacterial infections. The 2022 Global Antimicrobial Resistance and Use Surveillance System (GLASS) report shows concerning resistance rates among prevalent bacterial pathogens, with median reported rates of 42% for third-generation cephalosporin-resistant E. coli and 35% for methicillin-resistant Staphylococcus aureus. About 20% of urinary tract infections caused by E. coli exhibited reduced susceptibility to standard antibiotics in 2020, making common infections harder to treat. Additionally, drug-resistant tuberculosis (MDR-TB) and drug-resistant parasites present significant challenges to healthcare, especially in malaria-endemic regions like Nigeria. Only about 40% of people with drug-resistant TB accessed treatment in 2022. 

In Nigeria, AMR is exacerbated by drug misuse and inadequate healthcare infrastructure. The proposed solution by DataHealers, employing Geo-Tagged Natural Language Processing, targets the localisation and effectiveness of health policy interventions. This approach is essential in addressing the causes and consequences of AMR, particularly in low-resource settings and among vulnerable populations. Implementing improved surveillance, robust antimicrobial stewardship practices, and coordinated global action with a One Health approach are vital steps in combating this public health crisis, potentially impacting a significant portion of the population affected by AMR in Nigeria and globally.

The solution by DataHealers targets healthcare providers, policymakers, and communities in Nigeria, focusing on managing antimicrobial resistance (AMR). It assists healthcare providers by offering insights into local AMR patterns, aiding in effective antibiotic prescriptions. Policymakers benefit from data-driven insights for creating targeted interventions in AMR-affected areas, including resource allocation and policy formulation. Communities, especially in high AMR regions, indirectly benefit through improved public health outcomes and reduced drug-resistant infections.

Engaging the Target Audience: DataHealers will collaborate with local health authorities and institutions to understand AMR-related challenges and needs. Regular interaction with healthcare professionals and community members through surveys, interviews, and focus groups ensures the solution meets real-world requirements. This approach is vital for tailoring the solution to local contexts and enhancing the overall effectiveness of AMR management strategies in Nigeria.

Our solution offers several public goods, contributing significantly to the well-being of communities, especially in the context of antimicrobial resistance (AMR) management. The primary public goods provided include:

1. Open-Access Data and Insights: The core output of our solution is a comprehensive set of data and insights on AMR patterns and trends, which will be publicly available. This open-access approach ensures that valuable information is accessible to researchers, healthcare professionals, and policymakers globally, fostering a collaborative effort in combating AMR.

2. Educational and Awareness Tools: We will publish white papers and peer-reviewed publications detailing our findings and methodologies. This knowledge transfer will aid in raising awareness about AMR and educating the public and healthcare communities about effective strategies for AMR management.

3. Free-to-Use Dashboard: A user-friendly, interactive dashboard will be developed, displaying real-time data and analytics on AMR. This tool will be freely accessible to all, providing essential information for informed decision-making in healthcare and policy.

Utilising Geo-Tagged Natural Language Processing (GeoNLP), our solution is designed to create a tangible impact in managing antimicrobial resistance (AMR) in Nigeria, especially for underserved and vulnerable populations. The impact is expected through several logical steps:

1. Activity - Data Collection and Analysis: By collecting and analysing AMR-related data from diverse sources, including social media and health reports, we can identify patterns and hotspots of AMR.

2. Output - Targeted Information: The GeoNLP analysis provides detailed insights into regional AMR trends, enabling healthcare providers and policymakers to understand the specific AMR challenges in their areas.

3. Outcome - Informed Decision-Making: This targeted information empowers healthcare providers to prescribe effective antibiotics and enables policymakers to implement focused AMR control strategies.

For underserved populations, often most affected by AMR due to factors like limited healthcare access, our solution offers crucial benefits:

• Improved Healthcare Strategies: By identifying AMR hotspots, resources can be allocated more efficiently, ensuring that those in need receive appropriate interventions.
• Increased Awareness: Dissemination of AMR insights raises awareness among these populations, promoting better health practices and antibiotic usage.

Over the next year, our primary focus will be on refining and validating our GeoNLP solution within select regions in Nigeria. This involves enhancing the model's accuracy, expanding data sources, and conducting pilot tests to gauge effectiveness and gather feedback.

In the following three years, our strategy for scaling impact includes:

1. Geographical Expansion: We plan to extend our solution to additional regions in Nigeria and other countries with similar AMR challenges. This will involve adapting the model to local contexts and collaborating with regional health authorities for implementation.

2. Technological Advancements: Incorporating advanced features such as predictive analytics into the GeoNLP model will enhance its capability to anticipate AMR trends, enabling more proactive health interventions.

3. Strengthening Partnerships: Building robust partnerships with public health organisations, NGOs, and governments will be crucial for broader implementation and integration of our solution into national AMR strategies.

4. Community Engagement and Education: Conducting awareness programs and community engagement activities will be vital in promoting understanding and effective use of our solution at the grassroots level.

Over the next year, our focus will be on refining the Geo-Tagged Natural Language Processing (GeoNLP) system and expanding its data sources to enhance its accuracy and relevance in Nigeria. We plan to initiate collaborations with local healthcare institutions and NGOs to widen our data collection and increase community engagement. This will enable us to fine-tune our solution to meet the specific needs of various regions within Nigeria.

In the subsequent three years, the aim is to scale our impact geographically and functionally. Geographically, we will extend our solution to other countries in the region with similar AMR challenges, adapting our system to local contexts based on regional data and collaboration with local health authorities. Functionally, we intend to integrate additional features into our system, such as predictive modeling, to anticipate AMR trends and enable proactive measures.

Furthermore, we plan to strengthen our partnerships with global health organizations and leverage their networks for broader implementation and impact. By continuously improving our technology and expanding our reach, we aim to transform our solution into a key tool in the global fight against antimicrobial resistance, ultimately benefiting a larger population across multiple regions.

Several barriers may impede our goals over the next year and three years, along with strategies to overcome them:

1. Financial Constraints: Funding is crucial for research, development, and scaling. We plan to overcome this by seeking grants from global health organizations, government funding, and partnerships with NGOs.

2. Technical Challenges: As we refine and scale our GeoNLP system, we may face technical hurdles, particularly in data processing and analysis. Collaborations with academic institutions and tech companies will provide us with the necessary technical expertise and resources.

3. Data Accessibility: Access to diverse and comprehensive data sets is essential. We intend to mitigate this by establishing agreements with healthcare institutions for data sharing, ensuring compliance with privacy regulations.

4. Cultural and Educational Barriers: Successfully implementing our solution requires community engagement and awareness. We plan to conduct educational workshops and collaborate with local leaders to bridge cultural gaps and promote understanding of AMR.

5. Policy and Legal Challenges: Navigating different regulatory environments can be complex. We will engage legal experts to ensure compliance with national and international regulations, particularly concerning data privacy and healthcare policies.

We are applying to The Trinity Challenge because it aligns perfectly with our mission to combat antimicrobial resistance (AMR) using our Geo-Tagged Natural Language Processing (GeoNLP) solution. The Trinity Challenge offers a unique platform that can help us overcome several key barriers:

1. Funding and Resources: One of our primary barriers is financial constraints. The Challenge offers funding, valuable resources, and tools essential for developing, refining, and scaling our solution.

2. Network and Collaboration Opportunities: The Challenge provides access to a network of experts, organisations, and potential partners. This network is crucial for overcoming technical challenges, enhancing data collection capabilities, and effectively ensuring our solution is adapted to local contexts.

3. Visibility and Credibility: Participation in The Trinity Challenge will increase our solution's visibility, helping us attract further funding and support. Additionally, being associated with such a prestigious challenge will lend credibility to our project, facilitating partnerships and stakeholder engagement.

4. Guidance on Policy and Ethical Challenges: The Challenge can provide guidance and expertise in navigating policy and ethical challenges, particularly data privacy and healthcare regulations.

To initiate, accelerate, and scale our solution effectively, we aim to collaborate with various organisations that bring specific expertise and resources. These include:

1. Public Health Institutions (e.g., WHO, CDC): Their expertise in global health challenges and policy-making can provide valuable insights for refining and scaling our solution.

2. Academic and Research Institutions (e.g., London School of Hygiene & Tropical Medicine, Johns Hopkins Bloomberg School of Public Health): Collaboration with these institutions would provide access to cutting-edge research, data, and technical expertise in public health and data science.

3. Technology Companies (e.g., IBM, Google): Their advanced AI and data processing capabilities can significantly enhance the technical aspects of our GeoNLP solution.

4. NGOs and Non-Profits in Public Health (e.g., Médecins Sans Frontières, Gates Foundation): Their on-ground experience and networks in healthcare can facilitate pilot testing and implementation in various regions, ensuring our solution meets the diverse needs of affected communities.

5. Local Health Authorities and Governments: Collaboration with these entities is crucial for effective implementation and alignment with national health strategies.

These collaborations will provide us with the necessary expertise, data, technology, and networks to overcome our current barriers and significantly enhance the impact of our solution in combating antimicrobial resistance.