NoFightAMR: vel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining
Lead Research Organisation:
UNIVERSITY OF NOTTINGHAM
Department Name: School of Veterinary Medicine and Science
Abstract
Understanding the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and developing of interventions to limit the spread of AMR within and between humans, animals, environment and food is a significant challenge, requiring a 360-degree investigation of a complex, interconnected system of humans, animals, environment on one hand and geographical, societal and climate-related variables on the other.
This project will develop a monitoring system using AI and advanced tech to detect AMR spread in the interconnected human-animal-environment-food system ('One Health').
First, we will analyse the heterogeneous corpus of historical AMR-related public data. This will improve our understanding of what data (monitorable biomarkers) should be collected to identify the conditions leading to a higher risk of AMR spread. This knowledge will be used to guide a large-scale multi-country sampling collection campaign of a large amount of heterogeneous and interconnected data from farms, wet markets, food, environment. Data will include results of microbiological analysis, whole-genome sequencing, metagenomics, phenotyping, documentation of on-farm management practices, and environmental sensor data (temperature, humidity, etc). An innovative AI-powered data mining pipeline will be used to unravel previously unknown correlations between observable animal, human, environment, food variables and a core set of resistome, microbiome, and microbial genomics variables, highlighting new routes for surveillance deployable in low-to-high-income countries.
This project will develop a monitoring system using AI and advanced tech to detect AMR spread in the interconnected human-animal-environment-food system ('One Health').
First, we will analyse the heterogeneous corpus of historical AMR-related public data. This will improve our understanding of what data (monitorable biomarkers) should be collected to identify the conditions leading to a higher risk of AMR spread. This knowledge will be used to guide a large-scale multi-country sampling collection campaign of a large amount of heterogeneous and interconnected data from farms, wet markets, food, environment. Data will include results of microbiological analysis, whole-genome sequencing, metagenomics, phenotyping, documentation of on-farm management practices, and environmental sensor data (temperature, humidity, etc). An innovative AI-powered data mining pipeline will be used to unravel previously unknown correlations between observable animal, human, environment, food variables and a core set of resistome, microbiome, and microbial genomics variables, highlighting new routes for surveillance deployable in low-to-high-income countries.
Technical Summary
This project aims to develop new AI-powered surveillance solutions to identify increased risk of AMR emergence and direction of spread through the food-borne route based on the 'One Health' concept, and capable to detect the appearance of known and novel AMR traits. The solutions will be suitable for deployment in low-to-high income countries.
While there have been many studies on AMR in livestock, environment, and human, they often focus on a specific sector and/or rely on a specific analysis (antimicrobial usage or whole genome sequencing) but not necessarily on integrated data analysis. Our goal is to strengthen big data approaches to integrate surveillance across human, animal, and environment with the food chain to assist interventions to prevent AMR caused by resistant enteric bacterial pathogens.
We plan to devise a real-time monitoring method capable of pinpointing geographical locations and routes which -at any point in time- may be at higher risk of developing AMR. This will be achieved by a triangulated approach: i) Understand the conditions leading to higher risk of AMR, by developing a cloud-solution embedding auto-adaptive learning to integrate and mine public data from different sources (metagenomics, phenotypes, satellite, etc) at different scales (region/setting/country, etc) and subjects (humans, animals etc); ii) Perform an AI-guided experimental sampling collection campaign of unprecedented scale and coverage; iii) Identify monitorable biomarkers indicating increased risk of AMR and direction of spread, embedded in deployable surveillance solutions.
While there have been many studies on AMR in livestock, environment, and human, they often focus on a specific sector and/or rely on a specific analysis (antimicrobial usage or whole genome sequencing) but not necessarily on integrated data analysis. Our goal is to strengthen big data approaches to integrate surveillance across human, animal, and environment with the food chain to assist interventions to prevent AMR caused by resistant enteric bacterial pathogens.
We plan to devise a real-time monitoring method capable of pinpointing geographical locations and routes which -at any point in time- may be at higher risk of developing AMR. This will be achieved by a triangulated approach: i) Understand the conditions leading to higher risk of AMR, by developing a cloud-solution embedding auto-adaptive learning to integrate and mine public data from different sources (metagenomics, phenotypes, satellite, etc) at different scales (region/setting/country, etc) and subjects (humans, animals etc); ii) Perform an AI-guided experimental sampling collection campaign of unprecedented scale and coverage; iii) Identify monitorable biomarkers indicating increased risk of AMR and direction of spread, embedded in deployable surveillance solutions.
Organisations
- UNIVERSITY OF NOTTINGHAM (Lead Research Organisation)
- Jembi Health Systems (Collaboration)
- Addis Ababa City Administration Health Bureau (Collaboration)
- Chinese Academy of Sciences (Collaboration)
- University of Perugia (Collaboration)
- Makerere University College of Health Sciences (Collaboration)
- ANSES (Project Partner)
- Fondazione IRCCS Ca' Granda Ospedale (Project Partner)
- Agroscope (Project Partner)
- FLOX AI (Project Partner)
- University of Pretoria (Project Partner)
Publications
Related Projects
| Project Reference | Relationship | Related To | Start | End | Award Value |
|---|---|---|---|---|---|
| MR/Y034422/1 | 31/05/2024 | 30/11/2025 | £364,638 | ||
| MR/Y034422/2 | Transfer | MR/Y034422/1 | 01/12/2025 | 30/11/2028 | £349,502 |
| Description | Organised and chaired The CHINA-UK bilateral AMR Workshop "Tackling the Pandemic of Antimicrobial Resistance and Infection: Developing a Novel Approach to Antimicrobial Surveillance and Early Warning in the UK and China - A Collaborative Approach Between the UK and China" |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Impact | The CHINA-UK bilateral AMR Workshop "Tackling the Pandemic of Antimicrobial Resistance and Infection: Developing a Novel Approach to Antimicrobial Surveillance and Early Warning in the UK and China - A Collaborative Approach Between the UK and China" Hosts: Institute of Microbiology, Chinese Academy of Sciences and University of Nottingham UK and China Day 1 26/11/2024 8:00-8:30 Registration 8:30-9:00 Opening Session Welcome remarks (Chair Dr Zhou Tong): 9:00-9:10 Welcome Speech, Prof. Linqi Wang, deputy director, Institute of Microbiology, CAS 9:10-9:20 Welcome Speech, Dr Zhitao Ru, Deputy Director, Division of Europe, Bureau of International Cooperation, CAS 9:20-9:30 Welcome Speech, Minister Counsellor (Innovation) Matt Moody, British Embassy Beijing 9:30-9:40 Welcome Speech, Dr Ying Lai, Director, Division of International Fund, Bureau of International Cooperation, NSFC 9:40-9:50 UKRI Health Collaboration in China, China UKRI, Dr. Sophie Durrans 9:50-10:00 Welcome Speech, Prof. Laura Bishop, Vice Provost for Research and Knowledge Exchange, The University of Nottingham, Ningbo, China (online) Session 1- When and How Policy and Research Intersect in the UK and China (Chair: Tania Dottorini) 10:00-10:25 Carriage and potential spread of AMR Salmonella in heathy people, Prof. Biao Kan, Director of Institute of infectious Disease, China CDC, Beijing, China 10:25-10:50 Supervision in Whole Process of Veterinary Antimicrobials to Combat AMR in Animals, Dr. Hejia Wang Director of National Reference Laboratory of Veterinary Drugs Residues, China Institute of Veterinary Drug Control, Beijing, China 10:50-11:15 FAO's work on AMR in China. Dr. Le Dong Food and Agriculture Organization of the United Nations (FAO), Beijing, China 11:15-11:35 Coffee Break (photo) Session 2 - Infection, AMR, One Health and Policy Landscape - National and Global Initiatives (Chair: Prof. Baoli) 11:35-12:00 Accurate and timely quantitative methodologies and support to the implementation and evaluation of public health policies, Prof. Daniela De Angelis, University of Cambridge, Deputy Chair MRC-BSU, UK 12:00-12:25 AMR prevalence of the important foodborne pathogens in China, Dr. Yinping Dong, Deputy Director of Microbiology lab China National Centre for Food Safety Risk Assessment (CFSA), Beijing, China 12:25-12:50 The surveillance network of AMR in China, Prof. Yonghong Xiao, Director of China AMR surveillance network, Beijing, China 12:50-13:15 From data to regulations, Integrating AMR with NGS analysis into food safety standard, Dr. Wei Wang China National Center for Food Safety Risk Assessment, Beijing, China 13:15-14:00 Lunch Session 3 - Infection and AMR, One Health research landscape approaches and emerging trends in China and the UK (Chair: Tania Dottorini) 14:00-14:25 Exploring the emergence, spread and control of AMR via One Health Approach, Prof. Wang Yang, China Agricultural University 14:25-14:50 The Dark side of Metagenomics, Prof. John Wain, Quadram Institute, UK 14:50-15:15 AI, machine learning and genomics to tackle the pandemic of AMR in One Health, Prof. Tania Dottorini, University of Nottingham, UK and China Ningbo Campus 15:15-15:40 UK Poultry Antibiotic Stewardship: A Success Story, Dr. Michael Clark, University of Nottingham and President of the British Veterinary Poultry Association 15:40-16:00 Coffee break 16:00-16:25 Addressing AMR in the UK: Insights from National Action Plan, ESPAUR, and Collaborative Research on Healthcare-Associated Infections, Dr. Colin Brown, Deputy Director of Clinical & Emerging Infections at UK Health Security Agency (Talk online) 16:25-17:00- How to undertake analyses on sensitive data whilst maintaining data sovereignty Prof. Phil Quinlan Research Director of Health Data Research UK (HDRUK) and Director of the UKCRC Tissue Directory and Coordination Centre UK Biobank, UK (Talk online recorded) 17:00-17:25 AMR policy and surveillance in animals in the UK. Dr. Kitty Healey Head of Antimicrobial Resistance Policy and Surveillance Team at Veterinary Medicines Directorate, UK (Talk online) Day 2, 27/11/2024 Session 4 - Addressing AMR and virulence in Clinical Settings: Challenges and Strategies (Chair: Baoli Zhu) 09:00-09:25 Epidemiology and evolution for carbapenem-resistant Klebsiella pneumoniae, Prof. Hui Wang, Peking University, Beijing, China 09:25-09:50 Understanding the interface between AMR and virulence, Prof. Jose Bengoechea, Queen's Belfast University, UK 9:50-10:15 Fungal genetic and phenotypic resistance, Prof. Linqi Wang, Professor in Institute of Microbiology, CAS, China 10:15-11:00 Coffee Break 11:00-11:25 Emergence and spread of hv-CRKP and CR-hvKP, Prof. Minggui Wang, Fudan University, Huashan Hospital, Shanghai, China 11:25-11:50 The Status Report on Monitoring Capacity and Management of Mycosis in Chinese Medical Institutions, Prof. Ying Zhao, Peking Union Medical College Hospital, Chinese Medical Academy of Sciences, Beijing, China Session 5 - industry representatives can share their work, discuss collaborations, and exchange ideas, what is needed. The role of rapid diagnostics, new technologies, and AI in detecting infections and AMR. (Chair; Baoli Zhu) 12:00-13:00 12:00 - 12:20 Dr. Shaun Robertson CEO from MiDx (UK) Prof. Hongli Ling, Weilan Biotech Company, working on AMR in food animal (China); Dr. Shunguo Shi, CEO of Zhongkezhuteng Company, bioinformatics in food industry (China) Dr. Xibin Zhang CEO of New Hope Company, foodborne pathogens (China) Dr. Imtiaz Sham CEO of Flox-AI, AI for livestock, AMR, food safety (UK) 13:00-14:20 Lunch Session 6 - New technologies- Opportunities and Challenges (Chair: Tania Dottorini) 14:25-14:50 The prospects and challenges of engineered bacteriophages, Prof. Jie Feng, Institute of Microbiology, CAS, China 14:50-15:15 - Genomics alone can't make it without AI; Prof. Tania Dottorini, University of Nottingham, UK and China 15:15-15:40 NBIC and alternatives to target AMR in biofilm-mediated infections, Prof. Miguel Camara, UK NBIC and University of Nottingham, UK (Talk online) 15:45-16:10 AMR and biofilm: are we doing the right thing? Dr. Enrico Marsili, University of Nottingham, Ningbo, China 16:10-16:30 Coffee break 16:30-17:30 Session 8: Wrap up and Actionable Solutions - Developing Roadmaps for AMR Control (Chairs: Baoli Zhu and Tania Dottorini) • Breakout Groups: Attendees (grouped by sectors) work on creating actionable strategies for the next 5-10 years. o Industry: Research and innovation priorities. o Policymakers: Policy reforms and global cooperation. o Funders: Sustainable funding models for AMR solutions. o Researchers: Research gaps and collaborative opportunities. Workshop place: ??????????? E301 ???, ??: ?????????????3?? E? Contact person: ??? 13910396012 Please register to the event following the links below: Tue, 26 Nov 2024 08:00 - 18:30 (UTC+08:00) Beijing, Chongqing, Hong Kong, Urumqi https://events.teams.microsoft.com/event/f9b225c8-df55-4712-9440-6e8de4c94514@67bda7ee-fd80-41ef-ac91-358418290a1e Wed, 27 Nov 2024 08:00 - 18:00 (UTC+08:00) Beijing, Chongqing, Hong Kong, Urumqi https://events.teams.microsoft.com/event/b7d19a92-32b1-435b-a9d9-277d55e7ba04@67bda7ee-fd80-41ef-ac91-358418290a1e |
| Description | Policy Brief On antimicrobial resistance: we know enough to act. The Policy brief was coordinated by the UK Academy of Medical Science. My contribution was on global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining. Addressing antimicrobial resistance with a One Health approach Symposium. The Academy of Medical Science |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | We organised a workshop and engaged in YK-India discussion about AMR and the outcome was to write a Policy brief on antimicrobial resistance: we know enough to act. This was coordinated by the UK Academy of Medical Science Brief. |
| URL | https://acmedsci.ac.uk/file-download/70131697?utm_source=createsend&utm_medium=email&utm_campaign=am... |
| Description | The "M.Curie and R. Franklin Club," providing bioinformatics and machine learning training |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Impact | To empower women and ECRs in STEM, I founded the "M.Curie and R. Franklin Club," providing bioinformatics and machine learning training |
| Title | We have developed first global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increas |
| Description | AMR is triggered by an intricate interplay between multidrug resistant (MDR) traits, mobile genetic elements (MGEs), cross-species and multi-hosts transmission and key social determinants of health (socioeconomic, environmental, antibiotic consumption, mortality, health etc) all of which collectively shape current AMR and future trends. Consequently, a deep understanding of these interactions is crucial not only to understand these complex networks but for building accurate forecasting models. In this study, we developed a novel ML method, coupled with genomics, phenotyping, and predictive modelling, to achieve three key objectives: Firstly, we aimed to globally identify genomic traits and their associated MGEs that are strongly associated with observed AMR phenotypes.Secondly, we aimed to investigate which ML-selected resistant traits, is projected to rise over the next 30 years, along with the main drivers shaping these trends.Thirdly, we aimed to identify which of the AMR traits projected to rise by 2050 poses the highest risk as global health concern |
| Type Of Material | Data analysis technique |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| Impact | By understanding the interplay of biological and social factors in shaping AMR trends to 2050, we provide a roadmap for targeted AMR mitigation. |
| Title | We have developed first global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increas |
| Description | The AI methods to predict the resistance genes, mobile genetic elements predicted to spread by 2050 worldwide |
| Type Of Material | Data analysis technique |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| Impact | use of AI powered methods to forecast global health threats (under review in Cell Genomics) |
| URL | https://www.researchsquare.com/article/rs-6129213/v1 |
| Description | British Embassy Beijing GPF Call for Concept Bids, the Global Partnership Fund (GPF) for projects |
| Organisation | Chinese Academy of Sciences |
| Country | China |
| Sector | Public |
| PI Contribution | Tackling the pandemic of antimicrobial resistance. Development of a novel approach to surveillance and early warning in the UK and China based on climate monitoring, omics, big data mining and machine learning to produce safer food, decrease AMR infections and protect workers |
| Collaborator Contribution | This project plans to enhance the UK-China scientific collaboration and innovation and by fostering a dialogue on policy issues. Our goal is to develop novel big data approaches to integrate data (biological, climate, social, behavioural, and economic, policy) to capture the AMR transmission among human, animal, environments, and food. Proposed Activities: (i) Consortium Meetings to shape future UK-China research/policy applications, publications, data sharing and joint-labs, policy reports, to sustain long-term collaboration and enabling the development of innovative solutions. Prepare for funding applications (Dottorini has been selected for the UKRI UK-China-US workshop in London 2024, to shape the next round of collaborative research). The GFP funding will help the UK-China-US to meet and shape the proposal to be submitted. (ii) Conduct workshops in China on One Health AMR and policy, inviting industry, governmental, diplomatic, funders, policy makers representatives, enabling the dissemination of our solutions, research and outcome. (iii) Collaborative research on AMR and capacity building training on AI AMR. Publications will be essential to provide evidence-based results on AMR emergence in food and its spread to workers. This evidence will be essential to: i) Submit a funding application to the UKRI-NSFC-NSF Ecology and evolution of infectious diseases call (expected to be in November 2024)- publications and pre-prints will support the funding application; ii) develop robust and evidence-based reports to be shared with policy makers and industry to make an impact on AMR, by supporting policy changes and hence interventions prioritising policy actions; ii) disseminate and reach out to the scientific community to collaborate; iii) reach out to the wider populations via interviews, press-release, blogs etc. Our recent publications provided evidence that relevant transmission is happening not just between households and pets (Wang W et al, Antimicrob Agents Chemother. 2020) but importantly we showed that farm workers and their households have the same resistant pathogens and clinically relevant mobile cassette (i.e., resistances transmissible to humans) in their nose and hands as the animals they handle (Peng et al, 2022). This evidence is key to present it to policy makers and to develop interventions that the UK and China could take that are the lowest common denominator against AMR. Moreover, using a data mining approach based on machine learning, we analysed hundreds of microbiomes from chickens, carcasses, humans and environments, identifying mobile antibiotic resistance genes (ARGs) shared between chickens, humans and environments. A core set of microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR. Understanding that temperature and humidity are impacting AMR is crucial suggesting multiple routes to improving AMR surveillance in livestock production and this is an important route to reach the attention of different stakeholders including policy makers and industry. Please consider that the results and methods we published, attracted the attention of WHO, and I was asked, as leading expert, to advise on future potential and application of digital health for AMR prevention and control, for the AMR roadmap 2023-2030 that was adopted by Member States at the Regional Committee 73. Publications are essential for dissemination, evidence-based science and to achieve an impact and reach out to policy makers. Long term real world difference: - Promote long-term UK-China collaboration among researchers, policymakers, governmental and non-governmental agencies, and diplomatic representatives to co-develop solutions of national relevance to fight AMR - Development of evidence-based policies and regulations related to AMR - Improve healthcare by reducing AMR - Produce safer food and water - Enhance safety of agri-food and healthcare workers - Financial impact: cost-savings in healthcare and agri-food sectors by reducing losses in livestock, improving import/export, reducing hospitalizations |
| Impact | Conduct workshops in China on One Health AMR and policy, inviting industry, governmental, diplomatic, funders, policy makers representatives, enabling the dissemination of our solutions, research and outcome. |
| Start Year | 2024 |
| Description | Partnership with Spain, Italy, Ethiopia, Uganda and South Africa research organisations, academics and companies that led to the funded EU project Horizon EDTCP Type of Action: HORIZON-JU-RIA Acronym: CARE-Africa Project ID: 101248902 |
| Organisation | Addis Ababa City Administration Health Bureau |
| Country | Ethiopia |
| Sector | Public |
| PI Contribution | The models developed in the BBSRC and MRC projects were used to gather preliminary data to support the funding. Likewise, the research results achieved with the BBSRC and MRC funding fostered new research insights, building the new grant. |
| Collaborator Contribution | Consortium - The project sees the involvement of six countries/partners: - KCL: The bioinformatics and machine learning research team led by Tania Dottorini (PI), King's College London, UK. Inventor of machine-learning and big-data mining methods successfully deployed in China, Bangladesh, EU and Africa, dedicated to support data-driven understanding, diagnostics and treatment selection for infection and AMR. The methods and models developed by KCL will be at the core of this project; - IDI: The Infectious Disease Institute (IDI), Makerere University, Uganda. Prominent institution dedicated to the study of infectious diseases, in a large network of Ugandan healthcare facilities and regional referral hospitals, with direct connection with the Ministry of Health. Francis Kakooza is the scientific leader of this project; - SA: Jembi Health Systems, Cape Town, South Africa. Non-profit organization, developer of health information systems (HIS) deployed in multiple SSA countries, and creator of the Open Health Information Mediator (OpenHIM) for interoperability between health information systems. They have previously deployed TRL 8/9 software products in SSA (data management systems). AACAHB: The Addis Ababa City Administration Health Bureau, Addis Ababa, Ethiopia. Pivotal, governmental institution in Ethiopia's public health, receiving the national DHIS2 and PHEM data streams, and with direct access to more than 50 healthcare facilities. They have collaborated with SA to deploy TRL 8/9 software. - UNIPG: The machine learning and data-mining research team at the Department of Engineering, University of Perugia, Italy. Long-time collaborating with the KCL team on the use of AI to develop data mining technologies, data-driven simulations, digital twins and spatio-temporal statistical models; - Causal Foundry: Causal Foundry Inc., Barcelona, Spain. Start-up supported by the Gates Foundation. Developer of AI-driven technology for medicine and healthcare, with strong skills in reinforcement learning and explainable AI. and success stories of deployment in Africa. They have previously deployed TRL 8/9 software products based on mobile hardware (like the DSS) in SSA. |
| Impact | The output is a 4.8M Eur EU funding |
| Start Year | 2025 |
| Description | Partnership with Spain, Italy, Ethiopia, Uganda and South Africa research organisations, academics and companies that led to the funded EU project Horizon EDTCP Type of Action: HORIZON-JU-RIA Acronym: CARE-Africa Project ID: 101248902 |
| Organisation | Jembi Health Systems |
| Country | South Africa |
| Sector | Charity/Non Profit |
| PI Contribution | The models developed in the BBSRC and MRC projects were used to gather preliminary data to support the funding. Likewise, the research results achieved with the BBSRC and MRC funding fostered new research insights, building the new grant. |
| Collaborator Contribution | Consortium - The project sees the involvement of six countries/partners: - KCL: The bioinformatics and machine learning research team led by Tania Dottorini (PI), King's College London, UK. Inventor of machine-learning and big-data mining methods successfully deployed in China, Bangladesh, EU and Africa, dedicated to support data-driven understanding, diagnostics and treatment selection for infection and AMR. The methods and models developed by KCL will be at the core of this project; - IDI: The Infectious Disease Institute (IDI), Makerere University, Uganda. Prominent institution dedicated to the study of infectious diseases, in a large network of Ugandan healthcare facilities and regional referral hospitals, with direct connection with the Ministry of Health. Francis Kakooza is the scientific leader of this project; - SA: Jembi Health Systems, Cape Town, South Africa. Non-profit organization, developer of health information systems (HIS) deployed in multiple SSA countries, and creator of the Open Health Information Mediator (OpenHIM) for interoperability between health information systems. They have previously deployed TRL 8/9 software products in SSA (data management systems). AACAHB: The Addis Ababa City Administration Health Bureau, Addis Ababa, Ethiopia. Pivotal, governmental institution in Ethiopia's public health, receiving the national DHIS2 and PHEM data streams, and with direct access to more than 50 healthcare facilities. They have collaborated with SA to deploy TRL 8/9 software. - UNIPG: The machine learning and data-mining research team at the Department of Engineering, University of Perugia, Italy. Long-time collaborating with the KCL team on the use of AI to develop data mining technologies, data-driven simulations, digital twins and spatio-temporal statistical models; - Causal Foundry: Causal Foundry Inc., Barcelona, Spain. Start-up supported by the Gates Foundation. Developer of AI-driven technology for medicine and healthcare, with strong skills in reinforcement learning and explainable AI. and success stories of deployment in Africa. They have previously deployed TRL 8/9 software products based on mobile hardware (like the DSS) in SSA. |
| Impact | The output is a 4.8M Eur EU funding |
| Start Year | 2025 |
| Description | Partnership with Spain, Italy, Ethiopia, Uganda and South Africa research organisations, academics and companies that led to the funded EU project Horizon EDTCP Type of Action: HORIZON-JU-RIA Acronym: CARE-Africa Project ID: 101248902 |
| Organisation | Makerere University College of Health Sciences |
| Department | The Infectious Diseases Institute, Kampala |
| Country | Uganda |
| Sector | Hospitals |
| PI Contribution | The models developed in the BBSRC and MRC projects were used to gather preliminary data to support the funding. Likewise, the research results achieved with the BBSRC and MRC funding fostered new research insights, building the new grant. |
| Collaborator Contribution | Consortium - The project sees the involvement of six countries/partners: - KCL: The bioinformatics and machine learning research team led by Tania Dottorini (PI), King's College London, UK. Inventor of machine-learning and big-data mining methods successfully deployed in China, Bangladesh, EU and Africa, dedicated to support data-driven understanding, diagnostics and treatment selection for infection and AMR. The methods and models developed by KCL will be at the core of this project; - IDI: The Infectious Disease Institute (IDI), Makerere University, Uganda. Prominent institution dedicated to the study of infectious diseases, in a large network of Ugandan healthcare facilities and regional referral hospitals, with direct connection with the Ministry of Health. Francis Kakooza is the scientific leader of this project; - SA: Jembi Health Systems, Cape Town, South Africa. Non-profit organization, developer of health information systems (HIS) deployed in multiple SSA countries, and creator of the Open Health Information Mediator (OpenHIM) for interoperability between health information systems. They have previously deployed TRL 8/9 software products in SSA (data management systems). AACAHB: The Addis Ababa City Administration Health Bureau, Addis Ababa, Ethiopia. Pivotal, governmental institution in Ethiopia's public health, receiving the national DHIS2 and PHEM data streams, and with direct access to more than 50 healthcare facilities. They have collaborated with SA to deploy TRL 8/9 software. - UNIPG: The machine learning and data-mining research team at the Department of Engineering, University of Perugia, Italy. Long-time collaborating with the KCL team on the use of AI to develop data mining technologies, data-driven simulations, digital twins and spatio-temporal statistical models; - Causal Foundry: Causal Foundry Inc., Barcelona, Spain. Start-up supported by the Gates Foundation. Developer of AI-driven technology for medicine and healthcare, with strong skills in reinforcement learning and explainable AI. and success stories of deployment in Africa. They have previously deployed TRL 8/9 software products based on mobile hardware (like the DSS) in SSA. |
| Impact | The output is a 4.8M Eur EU funding |
| Start Year | 2025 |
| Description | Partnership with Spain, Italy, Ethiopia, Uganda and South Africa research organisations, academics and companies that led to the funded EU project Horizon EDTCP Type of Action: HORIZON-JU-RIA Acronym: CARE-Africa Project ID: 101248902 |
| Organisation | University of Perugia |
| Country | Italy |
| Sector | Academic/University |
| PI Contribution | The models developed in the BBSRC and MRC projects were used to gather preliminary data to support the funding. Likewise, the research results achieved with the BBSRC and MRC funding fostered new research insights, building the new grant. |
| Collaborator Contribution | Consortium - The project sees the involvement of six countries/partners: - KCL: The bioinformatics and machine learning research team led by Tania Dottorini (PI), King's College London, UK. Inventor of machine-learning and big-data mining methods successfully deployed in China, Bangladesh, EU and Africa, dedicated to support data-driven understanding, diagnostics and treatment selection for infection and AMR. The methods and models developed by KCL will be at the core of this project; - IDI: The Infectious Disease Institute (IDI), Makerere University, Uganda. Prominent institution dedicated to the study of infectious diseases, in a large network of Ugandan healthcare facilities and regional referral hospitals, with direct connection with the Ministry of Health. Francis Kakooza is the scientific leader of this project; - SA: Jembi Health Systems, Cape Town, South Africa. Non-profit organization, developer of health information systems (HIS) deployed in multiple SSA countries, and creator of the Open Health Information Mediator (OpenHIM) for interoperability between health information systems. They have previously deployed TRL 8/9 software products in SSA (data management systems). AACAHB: The Addis Ababa City Administration Health Bureau, Addis Ababa, Ethiopia. Pivotal, governmental institution in Ethiopia's public health, receiving the national DHIS2 and PHEM data streams, and with direct access to more than 50 healthcare facilities. They have collaborated with SA to deploy TRL 8/9 software. - UNIPG: The machine learning and data-mining research team at the Department of Engineering, University of Perugia, Italy. Long-time collaborating with the KCL team on the use of AI to develop data mining technologies, data-driven simulations, digital twins and spatio-temporal statistical models; - Causal Foundry: Causal Foundry Inc., Barcelona, Spain. Start-up supported by the Gates Foundation. Developer of AI-driven technology for medicine and healthcare, with strong skills in reinforcement learning and explainable AI. and success stories of deployment in Africa. They have previously deployed TRL 8/9 software products based on mobile hardware (like the DSS) in SSA. |
| Impact | The output is a 4.8M Eur EU funding |
| Start Year | 2025 |
| Title | The software allows to predict Genomic traits and social determinants of health drive bacterial antimicrobial resistance: current trends and projections to 2050 |
| Description | This software is to global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increase over the next 30 years. |
| Type Of Technology | Software |
| Year Produced | 2025 |
| Open Source License? | Yes |
| Impact | By predicting the biological and social factors in shaping AMR trends to 2050, we provide a roadmap for targeted AMR mitigation. |
| Description | Chair and organiser of the "Tackling the Pandemic of Antimicrobial Resistance and Infection: Developing a Novel Approach to Antimicrobial Surveillance and Early Warning in the UK and China - A Collaborative Approach Between the UK and China" funded by the UK FCDO, November 2024 China National Academy of Science, Beijing, China |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Chair and organiser of the "Tackling the Pandemic of Antimicrobial Resistance and Infection: Developing a Novel Approach to Antimicrobial Surveillance and Early Warning in the UK and China - A Collaborative Approach Between the UK and China" funded by the UK FCDO, November 2024 China National Academy of Science, Beijing, China |
| Year(s) Of Engagement Activity | 2024 |
| Description | Invited Speaker FightAMR project to develop the first EU-Africa AI powered surveillance solution. AMR INSIGHTS conference, June 2024 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | This is a Talk at an international conference with Universities and businesses to disseminate project results and foster collaboration |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.amr-insights.eu/adtca-2024/program/ |
| Description | Invited key note speaker: "Machine learning and bioinformatics to investigate antimicrobial resistance in host-pathogen interactions", East Midlands Microbiome Research Network (EMMRN) Research Day conference 2024 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited key note speaker: "Machine learning and bioinformatics to investigate antimicrobial resistance in host-pathogen interactions", East Midlands Microbiome Research Network (EMMRN) Research Day conference 2024. This was a Research Network among Scientists to disseminate research and network |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.medilinkmidlands.com/event/in-person-medilink-midlands-summer-networking-2/ |
| Description | Invited speaker: Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining. Addressing antimicrobial resistance with a One Health approach Symposium. The Academy of Medical Science |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited speaker: Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining. Addressing antimicrobial resistance with a One Health approach Symposium. The Academy of Medical Science. This activity was done to write a report on AMR in the UK and India. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://acmedsci.ac.uk/file-download/70131697?utm_source=createsend&utm_medium=email&utm_campaign=am... |