AI4M: Predicting Malaria outbreaks through Cardano and AI

[GENERAL] Name and surname of main applicant

CLEMENT MAURICE UMOH

[GENERAL] Are you delivering this project as an individual or as an entity (whether formally incorporated or not)

Entity (Not Incorporated)

[GENERAL] Co proposers and Additional applicants

[GENERAL] Requested funds in ada

70000

[GENERAL] Please specify how many months you expect your project to last (from 2-12 months)

6

[GENERAL] Please indicate if your proposal has been auto-translated into English from another language.

No

[GENERAL] Summarize your solution to the problem (200-character limit including spaces)

Our model provides a prediction system that uses advanced machine learning techniques & extensive analysis of epidemiological data to provide insights into the occurrence & progression of malaria.

[GENERAL] Does your project have any dependencies on other organizations, technical or otherwise?

No

[GENERAL] If YES, please describe what the dependency is and why you believe it is essential for your project’s delivery. If NO, please write “No dependencies.”

No Dependencies

[GENERAL] Will your project’s output/s be fully open source?

Yes

[GENERAL] Please provide here more information on the open source status of your project outputs

1. By opening up our prediction methodologies to the public, we allow for a more robust scrutiny and understanding of how our AI model work
2. This will also open the door to contributions from a diverse pool of experts. With these sort of contribution, we can harness into a wealth of knowledge leading to more robust algorithms and methodologies.
3. Open sourcing our product model encourages discussions about bias, fairness and transparency of our prediction model. This allows incorporation of these principles into the functionality of our AI model thereby reinforcing ethical AI practices in our modes of operation.
4. Open sourcing our product model would allow different regions and healthcare systems to customize and fine tune our product model to suit their specific requirements.

We would be utilizing the MIT Licensing for open sourcing our AI prediction model

[METADATA] SDG rating

>> SDG SELECTION:

• SDG goals:
• Ensure healthy lives and promote well-being for all at all ages
• Take urgent action to combat climate change and its impacts
• Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development

• SDG subgoals:
• By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being
• Support the research and development of vaccines and medicines for the communicable and non‑communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all

• Key Performance Indicator (KPI):
• Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease
• Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis

>> UHRI SELECTION:

The following Universal Human Rights Index(es) are applied:

• r) the Committee, referring also to the recommendations by the CEDAW (CEDAW) of 2008 (CEDAW/C/NGA/CO/6, paras. 31-34), urges the State party, as a matter or priority:

(a) to strengthen the capacity of its decentralized health care system to deliver, including by ensuring effective roll-out of the second and third phases of the IMNHC, while prioritizing the most disadvantaged northern states

(b) to strengthen the coverage of the National Immunization Programs, especially in rural areas

(c) to consider nutrition as a national priority and to provide appropriate resources for the implementation of nutrition programmes and to ensure their full integration into government health structures

(d) to develop ongoing efforts to ensure community participation and ownership, especially parents, regarding pre- and post-natal care, child health, nutrition and family planning

(e) to address the correlation between access to health care and girls’ education, with a view to combat maternal mortality and empower women in decision-making concerning their health care

(f) to adopt the National Health Bill, which provides for direct funding line for primary health care, at its earliest possible and ensure that it guarantees the right of the child to the best attainable state of physical and mental health, as stipulated in the Child Rights Act

(g) to amend the Constitution with a view to guarantee the right of the child to the best attainable state of physical and mental health as a constitutionally protected right, and with a view to specify the respective powers and responsibilities of federal, state and local governments in the delivery of health care

(h) to fulfil its commitment, as set out in the 2006 WHO Regional Committee for Africa resolution “Health Financing: A Strategy for the African Region”, to allocate a minimum of 15 per cent of its annual budget to improve the health sector, and continuing to seek technical cooperation and assistance from UNICEF and WHO

(i) to ensure the provision of free maternal and child health services to all states of the federation and take measures to ensure nation-wide coverage of the implementation of the National Health Insurance Scheme (NHIS)

>> PHDI SELECTION:

The following report is applied for the selected Planetary pressures-adjusted Human Development Index:

'''

Nigeria is ranked 163 (Human Development Index) and when adjusted for the Planetary pressures is ranked 160 a rise of 3. 

 In 2021 it had a Human Development Index (HDI) value of 0.535 that when adjusted for the Planetary pressures-adjusted HDI (PHDI) had a value of 0.524, a difference of 2.06. 

 The Planetary Pressures adjustment factor of 0.98 is made up of the SDG9.4 Carbon dioxide emissions per capita (production) 2020 of 0.61 tonnes (indexed value 0.99), and the SDG8.4 and 12.2 Material footprint per capita of 3.6 tonnes (indexed value 0.97). 

 For more information about the details of the PHDI please reference: 

 https://hdr.undp.org/planetary-pressures-adjusted-human-development-index#/indicies/PHDI 

 https://hdr.undp.org/system/files/documents/phdi2020technicalnotespdf.pdf 

 The specific SDG targets 

 https://indicators.report/targets/9-4/ 

 https://indicators.report/targets/8-4/ 

 https://indicators.report/targets/12-2/ 

 Source data: 

 https://hdr.undp.org/sites/default/files/2021-22_HDR/HDR21-22_Statistical_Annex_PHDI_Table.xlsx (table 7)

'''

#proposertoolsdg

[SOLUTION] Please describe your proposed solution.

Our AI4M Model provides a robust malaria prediction system that enhances malaria predictability among diverse demographics by integrating and analyzing the complex interplay of factors that precipitates it's occurrence and progression. These factors include; environmental conditions, socio-economic status, genetics, healthcare access, amongst other factors that influence malaria dynamics.

Our model utilizes advanced machine learning techniques and extensive analysis of epidemiological data to provide actionable insights into the occurrence and outcomes of malaria among different human groups. These actionable insights are then integrated into a real time database system that are then transmuted into API's. These API's are made accessible and can be integrated as functional tools in other epidemiological prediction systems or other specific prediction systems.

[IMPACT] Please define the positive impact your project will have on the wider Cardano community.

Our project has the potential to impact the Cardano ecosystem in a long-term. The open sourcing nature of our project, coupled with the integration of APIs in our project's functionality would mean that our project's framework will be made accessible to other developers and can be integrated as functional tools in other epidemiological prediction systems or other specific prediction systems that may spring up within the Cardano ecosystem. This will have lasting benefits and contribute positively to the ecosystem's growth.

We recognize the immense value of community support in driving the success of our AI model for malaria prediction. Engaging with the wider Cardano community offers us a unique opportunity to foster trust and collaboration. We have developed a structured framework where we can regularly share the success of our project especially as regards the positive impact of our AI model on malaria control and health outcomes. To further elevate the involvement of the wider Cardano community in our project’s successes, we will define clear progress metrics to allow for impact assessment. These could include (but are not limited to) lives saved or reduced cost in healthcare. Finally, we will also actively involve the Cardano community in decision making processes related to how shared benefits on our AI model are allocated and utilized.

[CAPABILITY & FEASIBILITY] What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?

For our team, ensuring high levels of trust and accountability in a project like this is crucial, especially when dealing with sensitive topics like healthcare and we are committed to ensuring transparency in all aspects. Here's how we plan to deliver on those fronts:

1. Transparent Project Management: We'll maintain transparent project management practices, providing regular updates on progress, challenges, and milestones. This includes detailed reports on how the allocated funds are being utilized at each stage of the project.
2. Expert Team and Data Security: Our team consists of experienced data scientists and professionals well-versed in healthcare analytics. Data security and privacy will be prioritized throughout the project, adhering to industry standards and regulatory requirements. Regular security audits will be conducted to ensure the integrity of the data.
3. Stakeholder Involvement: We believe in engaging stakeholders at every step. We will collaborate with healthcare professionals, community representatives, and potential end-users to gather insights, feedback, and ensure that our AI model is not only effective but also culturally sensitive and ethical.
4. Validation and Feasibility Studies: Before diving into the full-scale project, we'll conduct thorough validation and feasibility studies. This involves a detailed analysis of the available data, assessing the accuracy of our predictive models, and identifying potential challenges. This initial phase will help us refine our approach and ensure that our methods are not only effective but also practical.
5. Legal and Compliance Adherence: Legal and compliance consultation is a significant part of our plan. We'll work closely with legal experts to ensure that our approach aligns with regulations, both in terms of data usage and healthcare standards. This includes obtaining necessary approvals and permissions for handling sensitive health-related data.
6. Continuous Evaluation and Improvement: The project doesn't end with the initial deployment. We're committed to continuous evaluation and improvement. Regular assessments will be conducted to measure the model's effectiveness, and refinements will be made based on real-world feedback and evolving healthcare trends.
7. Public Engagement and Education: Building trust involves keeping the public informed and educated. We'll invest in public engagement efforts, sharing information about the project's goals, methodologies, and outcomes. We will also provide on-hand responsiveness to community concerns and questions.

By combining these strategies, we aim to deliver a project that not only meets its objectives but does so with the highest levels of trust, accountability, and ethical considerations. We're committed to making a positive impact on malaria predictability and outcomes, and we understand the responsibility that comes with such a crucial endeavor.

[Project Milestones] What are the key milestones you need to achieve in order to complete your project successfully?

Milestone 1

Milestone Name: Data Acquisition and Preparation

Milestone Description: This involves gathering diverse dataset encompassing demographic, environmental and health factors related to malaria occurrence and prevalence across our test population groups.

Milestone Output: The procurement of an organized dataset ready for AI model training and machine learning

Deliverable Description (Acceptance Criteria): We would have an organized data set that would form the foundational basis for the engagement of our AI model.

Milestone 2

Milestone Name: AI Model Development

Milestone Description: The development and training of our AI model using advanced learning machine algorithms

Milestone Deliverable: A Prototype AI model with initial predictive capabilities

Deliverable Description: A functional AI model demonstrating the potential to predict malaria occurrences and outcomes.

Deliverable Description (Acceptance Criteria): A prototype AI model that has undergone an initial phase of useability testing

Milestone 3

Milestone Name: AI Model Validation and Calibration

Milestone Description: This involves validating our AI model's performance against historical malaria data and calibrate it for accuracy

Milestone Deliverable: A validated and calibrated AI model

Deliverable Description (Acceptance Criteria): An AI model with proven accuracy is accurately predicting malaria occurrence and outcomes

Milestone 4

Milestone Name: Integration of AI model with Healthcare systems

Milestone Description: This involves the integration of our AI model with healthcare systems and data pipelines (pathways) that allow for real-time data analysis.

Milestone Deliverable: Operational integration of our AI model with healthcare systems Deliverable Description: Our AI model is seamlessly integrated into our test healthcare infrastructure for continuous monitoring.

Acceptance Criteria (Outcome): Our AI model would have proven its functionality within an environment that allows for its application in real time.

Milestone 5

Milestone Name: Pilot Implementation

Milestone Description: This involves the implementation of our AI model in a pilot project in collaboration with a healthcare provider or a humanitarian aid health agency.

Milestone Deliverable: Generating a successful pilot implementation report

Deliverable Description: A report detailing the outcomes and benefits of using our AI model in real-life settings

Acceptance Criteria (Outcome): Our AI model would have undergone its first field operational deployment in preparation for larger scale utility.

Final Milestone (Milestone 6)

Milestone Name: Impact Assessment

Milestone Description: The assessment of the impact of our AI model on malaria control and healthcare outcomes

Milestone Deliverable: Procurement of an Impact Assessment Report

Outcome (Acceptance Criteria): A comprehensive report that provides quantitative and qualitative data on the contributions of our AI model to malaria control and general healthcare.

[RESOURCES] Who is in the project team and what are their roles?

Here are the top 5 members of our project team, each of whom brings a wealth of expertise and experience that is vital for the development and implementation of our AI model.

1. Akwa Erim (Chief Data Scientist)

Mr. Akwa holds certifications in machine learning. As a senior lecturer in a reputable University, he has spent over 6 years in researching and developing AI-driven healthcare solutions. He has an in-depth knowledge of advanced statistical modelling and data analysis. This would be invaluable to the construction and fine tuning of our AI prediction model. With numerous published papers on AI application in healthcare, his expertise will guide the technical development of our AI model, ensuring its capability of handling sophisticated demographic and health data.

2. Mr. Hope Okon (Epidemiologist and Public Health Specialist)

Mr. Hope holds a Bachelor degree in clinical and applied biochemistry and a Masters Degree in Genomic Epidemiology. He is an epidemiologist with extensive experiences in infectious diseases such as COVID-19 and Malaria. He has worked on the frontlines of healthcare in malaria endemic regions, understanding the nuances of disease transmission and public health intervention. He has clinical working experiences with reputable health institutions and organizations such as PFIZER. His practical knowledge will be essential for translating our AI model’s prediction into effective interventions, bridging the gap between data-driven insights and real world implementation.

https://www.linkedin.com/in/hope-okon-060a651a1?trk=contact-info

3. Miss Janet Odey (Software Engineering Lead)

Miss Janet is a full stark developer and software engineer with over 5 years of experience in software development. Miss Jane has curated the creation of scalable and robust healthcare applications, most notably the development of an AI algorithm for early stage cancer predictability in female patients using estimated breast density. She is a a Ford Community Impact Fellow, a licensee of the William Davidson Research Institute and a TECHNOVATION regional ambassador. Her technical leadership will ensure the accuracy and practicability of our AI model to allow for healthcare providers to seamlessly integrate it into their system, maximizing its impact.

https://www.linkedin.com/in/jane-odey-127375159/

4. Dr. Emmanuel Edu (Epidemiologist and Public Health Specialist)

Dr. Edu holds a bachelor degree in human physiology, an MBBS from the University of Kharkiv, Ukraine and is currently undergoing studies for a Masters degree in Public Health. He also holds certifications in Global Health Policies and has worked with reputable international organizations to shape strategies for disease control and healthcare access. His insights into the policy landscape and understanding of the needs of different population groups will guide and ensure the alignment of our project with broader healthcare initiatives and ensure it meets the demands of our client community.

https://ua.linkedin.com/in/emmanuel-edu-2181031a9

5. Clement Umoh (Data Privacy and Ethics Specialist)

Mr. Umoh is a software penetration tester with background in cyber security. He has relevant experiences in handling data privacy and ethics in healthcare, ensuring responsible use of medical data. With increasing concerns about data privacy and ethical biases on our AI model, Mr. Umoh works to ensure our project adheres to the strictest ethical guidelines and regulatory standards, safeguarding patient information and boosting our user trust. 

[BUDGET & COSTS] Please provide a cost breakdown of the proposed work and resources.

Data procurement and database/storage infrastructure set up - 15,000 Ada

Engagement of data scientists and computational resources - 10,000 Ada

Data validation and model refinement - 5,000 Ada

System integration and IT support - 10,000 Ada

Pilot project execution, evaluation and documentation - 10,000 Ada

Legal and compliance consultation - 5,000 Ada

Procurement of monitoring tools and data analysis - 5,000 Ada

Impact assessment studies and reporting - 5,000 Ada

Publicity, market research & integration, partnership development - 5,000 Ada

Total Ask - 70,000 Ada

[VALUE FOR MONEY] How does the cost of the project represent value for money for the Cardano ecosystem?

In every sense of our cost breakdown, there is of course, value for money within the Cardano ecosystem as regards our proposed AI model development project. Let us consider the following points:

1. Our AI model's objectives align with the goals and principles of the Cardano blockchain ecosystem evident by its immense potential to contribute to healthcare solutions and community well-being which aligns with Cardano's mission.
2. Our proposed AI model is designed to be efficient and scalable within the Cardano ecosystem, having the ability to handle increased data and usage over time without significant increases in cost.
3. Our project incorporates robust data security and privacy measures. Cardano places a strong emphasis on security and privacy, and our project being within the Cardano ecosystem, adheres to these principles.
4. Our project also contributes to innovation and technological advancement within the Cardano ecosystem. Our project also incorporates APIs and real time database tools within its functionalities. This reflects the fact that our projects leverages on the latest technologies which is of immense value within the Cardano ecosystem.
5. Our project also involves and engages the Cardano community. This community involvement will contribute to the success and sustainability of our project within the ecosystem.
6. Furthermore, our AI project complies with regulatory standards and requirements within the Cardano ecosystem. We are keen on adherence to these standards, as they are crucial for the sustainability of our project.

In summary, to demonstrate our project's value for money within the Cardano ecosystem, our proposed AI model aligns with Cardano's goals, is efficient and scalable, prioritize data security and privacy, contributes to innovation, engages the wider Cardano community, promises a lasting impact, and adheres to regulatory standards. With all these criteria, our project can be reckoned as a valuable addition to the Cardano blockchain ecosystem.

[IMPORTANT NOTE] The Applicant agrees to Fund Rules and also that data in the submission form and other data provided by the project team during the course of the project will be publicly available.

I Accept