Predictive Modelling of Antimicrobial Resistance Spread

A predictive model for forecasting potential Antimicrobial Resistance (AMR) organisms’ sources and transmission routes in the environment to help guide public health interventions.

Atijosan Abimbola (Primary Investigator)

In Nigeria, the escalating threat of antimicrobial resistance (AMR) and bacterial infections poses a grave public health concern. Globally, AMR leads to approximately 700,000 deaths annually. With Nigeria facing significant challenges such as insufficient healthcare infrastructure, rampant antibiotic misuse, and inadequate sanitation, the situation becomes dire, affecting millions locally and exacerbating healthcare burdens. Our solution deploys data, Artificial Intelligence and spatial analytics to help mitigate this crisis. By monitoring environmental factors we target specific causes of bacterial growth and resistance. AI and GIS techniques are deployed to build predictive models to help forecast potential AMR sources and transmission routes, allowing for preemptive and tailored interventions. 

Our target audience primarily comprises policy-makers and public health practitioners concerned with combating antimicrobial resistance (AMR). We aim to support them by providing AMR risk maps. By identifying high-risk locations and risk factors for AMR emergence, our work facilitates rapid and tailored intervention programs, ensuring efficient allocation of resources and effective response to emerging threats. To understand the needs of our target audience, we will engage in dialogue and collaboration with policymakers, public health experts, and community stakeholders within the study area. Through citizen science, interviews, surveys, and workshops, we will gather insights into their challenges, priorities, and requirements for addressing AMR effectively. Additionally, we will conduct literature reviews and analyze existing data to help inform our approach and ensure alignment with the evolving needs of the community.   As we develop the solution, we will maintain active engagement with our target audience through regular updates and feedback sessions. Fostering a participatory approach will ensure that our solution addresses the real-world challenges faced by policy-makers, public health practitioners, and vulnerable communities. Ultimately, our project aims to prevent the spread of AMR across borders and focus on the most vulnerable and impoverished populations where the risk of epidemics is highest.

Our solution serves a crucial public good by advancing our understanding and management of antimicrobial resistance thereby, enabling early identification of potential sources and transmission routes of AMR organisms and contributing significantly to proactive public health measures. By leveraging AI, IoT, and geospatial technologies, the project enhances surveillance capabilities, aids in targeted intervention strategies, and empowers policymakers and public health practitioners to make informed decisions. Ultimately, the project helps mitigate the impact of antimicrobial resistance, protect communities from infectious diseases, and contribute to the global effort to preserve the effectiveness of antimicrobial treatments.

This project is poised to create a tangible impact by significantly advancing our understanding of the dynamics between antimicrobial resistance (AMR) and the environment. Through its focus on early detection, hotspot mapping, and forecasting using Artificial Intelligence, IoT, and geospatial technologies, the initiative will provide critical insights into potential sources and transmission routes of AMR organisms. The tangible impact is twofold: firstly, it will empower policy-makers with comprehensive data to inform evidence-based policies, ensuring more effective strategies for combatting AMR. Secondly, health practitioners will benefit from improved monitoring systems, enabling them to respond proactively to emerging threats and tailor interventions to specific risk areas. By strengthening the link between research and actionable outcomes, this project will have a direct and tangible impact on both policy-making and health practices, contributing to the global effort to mitigate the spread of antimicrobial resistance.

From the initial focus on monitoring and forecasting AMR hotspots in Nigeria in Year One, we will scale up impact along two main axes over three years. First, taking advantage of our close links to other countries in West Africa, we will increase the forecasting and monitoring geographic scope. We plan to collect data across several locations in West Africa (Ghana and Sierra Leone). By forecasting AMR across the whole of West Africa we can directly impact the lives of millions of people. Second, working with our public health partners and stakeholders in these countries we hope to increase the effectiveness of our model. Utilizing AI and Geospatial techniques, our system is ideally placed to create risk hotspot maps of AMR. Utilizing IoT and autonomous data collection systems alongside conventional data collection systems we can then continually update to produce predictions with increased accuracy and resolution as more information becomes available thus impacting the lives of many millions more people both across Africa and the rest of the world.

Measuring success against the impact goals of the project will entail a multi-faceted approach. Firstly, success will be gauged by the accuracy and reliability of the AI-driven models in forecasting AMR hotspots and transmission routes. Metrics such as predictive accuracy, sensitivity, and specificity of the models will be crucial indicators of success. Additionally, the effectiveness of geospatial and IoT technologies in monitoring and collecting real-time data on AMR organisms in the environment will be evaluated. Success will also be measured by the project's ability to inform and improve policy and monitoring systems, as well as its impact on public health outcomes. Reductions in the incidence of AMR infections, improved response times to outbreaks, and enhanced resource allocation strategies based on the project's insights will serve as tangible markers of success. Furthermore, the scalability and replicability of the project's methodologies across different geographic regions and environmental contexts will be assessed to ensure long-term sustainability and global impact in combating antimicrobial resistance.

The project faces several barriers that may hinder goal accomplishment in the next year and the next three years, notably infrastructure challenges in the health sector and the non-availability and restricted access to data. Insufficient healthcare infrastructure may impede efficient data collection and analysis related to antimicrobial resistance (AMR), while the absence of comprehensive datasets could limit the project's effectiveness.

Financial barriers encompass the costs associated with acquiring and maintaining advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), and geospatial tools. Technical challenges involve ensuring interoperability and overcoming potential issues in integrating diverse datasets.

Legal and policy hurdles may arise concerning data access and sharing, potentially restricting the project's ability to gather critical information for analysis. Cultural barriers, such as a lack of awareness about AMR or resistance to technological adoption, may impede community engagement and participation.

To address these challenges, the project aims to secure funding through grants, partnerships, and collaborations to overcome financial barriers. Investing in technical training programs will enhance the capabilities of personnel operating advanced technologies. Engaging with legal experts and advocacy efforts will help navigate regulatory challenges and ensure data access. Community outreach programs will address cultural barriers, promoting awareness and collaboration. 

We are aware that we cannot address a problem of this scale alone and the Trinity Challenge can provide the resources to enable us to integrate and scale up our solution. The barriers we face include financial constraints, health infrastructure deficits, access to data, technical challenges in deploying advanced technologies, and the need for interdisciplinary collaboration. The Trinity Challenge's network and support can assist in securing funding, fostering collaborations, and providing guidance on ethical and legal considerations. Furthermore, the Trinity Challenge provides a platform for global collaboration, enabling us to leverage the diverse expertise and resources essential for effective AMR surveillance and intervention. By participating in The Trinity Challenge, we also aim to amplify our impact by gaining access to a global community committed to addressing health challenges.

We aim to collaborate with organizations that bring expertise in health, technology, and environmental research to initiate, accelerate, and scale our solution addressing antimicrobial resistance (AMR) through predictive modeling. Potential collaborators include the World Health Organization (WHO), the African Centers for Disease Control and Prevention (Africa CDC), and regional and national health agencies like the West African Health Organization (WAHO) and centers for disease control and prevention. Collaborating with these entities will provide essential insights, guidance, and support, aligning our solution with global and regional health priorities.

Additionally, partnerships with technology providers, such as leading AI and IoT companies, will enhance our technological capabilities. Academic institutions with a focus on AMR research and environmental science will bring valuable knowledge, validating and strengthening our predictive models.

Engaging with organizations dedicated to environmental conservation and public health advocacy will help amplify our impact. The mentorship and resources offered by The Trinity Challenge's network of collaborators will be instrumental in refining our solution, ensuring it aligns with best practices and global standards in combating AMR.