| Data Request ID | Researcher's Name | Researcher's Affiliation | Data Request Title | Data Contributors |
|---|---|---|---|---|
| 8010, 8011, 8013, 8014 | Moska Hellamand | Access To Medicine Foundation, Netherlands | Assessment of datasets shared on the AMR Register | GSK, Johnson & Johnson, Pfizer, Shionogi |
| 8055 | Jose Lopez Revilla | Instituto Nacional de salud del Niño San Borja, Peru | The impact of resistance on sepsis mortality in pediatrics | Pfizer |
| 8173 | Catrin Moore | St. George’s, University of London, UK | ADILA (AMR data for local action) | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 8235 | Megan Coffee | Mailman School of Public Health, USA | Nigeria AMR | Pfizer, Shionogi, Venatorx |
| 8430 | Victoria Savage | Infex Therapeutics, UK | Establishing the prevalence of metallo-B-lactamases and carbapenem-resistance in Gram negative pathogens | Merck, Pfizer |
| 8432 | Ghanshyam Lad | Independent researcher, India | The trend of antibiotic resistance of tetracycline in adults in recent years | Pfizer |
| 8447, 8594 8595, 8596 | Benn Sartorius | University of Oxford, UK | The Global Research on Antimicrobial Resistance (GRAM) Project | Paratek, Pfizer, Shionogi, Venatorx |
| 8544 | Gwen Knight | London School of Hygiene and Tropical Medicine, UK | Medical Research Council - Career Development Award (MRC CDA) fellowship project | GSK, Johnson & Johnson, Paratek, Shionogi, Venatorx |
| 8554 | Ying Huang | The Chinese University of Hong Kong, China | To explore pathogens and antibiotic susceptibility in hospital | Pfizer |
| 8612/8613 | Camille Andre | Harvard Medical School, USA | Epidemiology and antimicrobial resistance of bacteria causing eye infections | Paratek, Pfizer |
| 8745 | Ingrid Jacobson | University of Minnesota Twin Cities - Center for Infectious Disease Research and Prevention (CIDRAP), USA | Master's Thesis Project on Antifungal Resistance | Pfizer |
| 8811 | Naomi Waterlow | London School of Hygiene and Tropical Medicine, UK | AMR and age | GSK, Johnson & Johnson, Paratek, Pfizer, Venatorx |
| 8906 | Megan Coffee | NYU Langone | Diagnosis of Tuberculosis through computer-assisted detection | Johnson & Johnson |
| 8907 | Megan Coffee | NYU Langone | AMR Global Analysis of trends in genotypes of resistance | Paratek |
| 8911 | Dickson Aruhomukama | Makerere University College of Health Sciences | Data Challenge_Investigating the Evolution and Predicting the Future Outlook of Antimicrobial Resistance in sub-Saharan Africa Using Genotypic Data for Klebsiella pneumoniae: A 5-Year Analysis | Pfizer |
| 8920 | Omololu Fagunwa | Queens University Belfast | Insights into AMR trends and resistant pathogens in Middle-East and Africa | Johnson & Johnson, Merck, Paratek, Pfizer, Venatorx |
| 8923 | Robert Beardmore | University of Exeter/Universidad Carlos III/Westmead Hospital / University of Sydney | Data Challenge - Are antibiotic breakpoints globally consistent, does it matter if not? | Pfizer |
| 8928 | Mabel Aworh | North Carolina State University | Data Challenge - Phenotypic and Genomic Evolution of Antimicrobial Resistance among WHO Priority pathogens in LMICS | GSK, Paratek, Pfizer, Venatorx |
| 8930 | Kauke Bakari Zimbwe | The Benjamin Mkapa Hospital | Data challenge - Antimicrobial resistance trend and clinical implications of WHO priority pathogens from urine, blood and wounds | Johnson & Johnson, Pfizer, Venatorx |
| 8932 | Megan Coffee | NYU Grossman School of Medicine | Data Challenge - Building the macro from the micro: towards predictive analytic models for country-wide prediction of antimicrobial resistance | Pfizer, Shionogi |
| 8936 | Rik Oldenkamp | Universiteit Amsterdam | Data Challenge - Spatiotemporal modelling of antimicrobial resistance in key gram-negative pathogens: Deciphering global patterns using indirect predictors | Pfizer, Venatorx |
| 8938 | Ifeanyi Elibe | University of Ibadan | Data Challenge - Phenotypic assays analysis and Meta-analysis of whole genome sequencing studies of Klebsiella pneumonia, Escherichia coli, and Enterobacter spp. reveals multiple AMR patterns and diverse MDR lineages spread across Africa | Pfizer, Venatorx |
| 8939 | Saviour Atuheire | Makerere University College of Health Sciences | Data Challenge - Aeromonas resistance | Pfizer |
| 8940 | Nalugya Flavia | Makerere University | Data Challenge - correlation between antibiotic consumption patterns and the emergence of antimicrobial resistance (AMR) in specific bacterial strains in different regions | GSK, Pfizer |
| 8941 | Amusa Wamawobe | Makerere University | Data Challenge - Designing a PCR Test for Detecting Antimicrobial Resistance Markers in Gram-Negative Bacteria in Africa Using Genotypic AMR Data from Multiple Countries | Pfizer |
| 8943 | Muditha Hapudeniya | Ministry of Health, Sri Lanka | Data Challenge - Application of statistical and machine learning methods to predict antimicrobial susceptibility to blood borne microorganisms to support administrative and policy level decision making | Pfizer |
| 8945 | Obed Amponsah | Kwame Nkrumah University of Science and Technology | Antibiotic Resistance Patterns in Africa Using the Pfizer ATLAS Surveillance System _ Open Data Challenge | Pfizer |
| 8951 | Mugerwa Moses | Tufaayo Foundation | Data Challange: Exploring the Link Between Gram-Negative Pathogen Resistance and Omadacycline Effectiveness | Paratek, Venatorx |
| 8952 | Stephen Opiyo | Patira Data Science | Data Challenge - Unleashing the Power of AI: Harnessing Vivli Data from Kenya to Unlock the Untapped Potential of AMR Data from Uganda | Pfizer |
| 8961 | Ndugwa Henry | International center of insect physiology and ecology | Data Challenge: Predicting Antimicrobial resistance in different geographical locations, micobes, antimicrobial classes and infection types using spatial temporal analysis | Pfizer |
| 8964 | Bonface Onyango | International center of insect physiology and ecology | Data Challenge - Global Antimicrobial Resistance Trends and Patterns Identification using Machine Learning | Pfizer |
| 8990 | Babafela Awosile | Texas Tech University School of Veterinary Medicine | Data Challenge - Predictive modeling of phenotypic antimicrobial susceptibility of selected critical and high-priority antimicrobials from antimicrobial resistance genes | Pfizer |
| 8995 | Fredrick Mutisya | Narok County Government | Data Challenge: Application of artificial intelligence methods in AMR | Pfizer |
| 9005, 9020 | Kenneth Irungu | Ministry if Health, Kenya | Data challenge: respiratory tract infections; is it true resistance or does penicillin work | GSK, Venatorx |
| 9006 | Ivan Lumu | Infectious Diseases Institute, Makerere University | Data Challenge - Developing tools to improve empirical treatment in low and middle income countries | GSK, Paratek, Pfizer, Shionogi, Venatorx, |
| 9013 | Adebiyi Sefiyat Odunola | Stellenboch University | Data Challenge: Unlocking AMR Landscape: Decoding National, Regional and Global Trends and Predicting Hotspots for a Resilient Future using the ATLAS, GEAR and ResistanceMap Databases, 2004-2021 | Pfizer, Venatorx |
| 9017 | Samra Qadri | Shaheed Zulfikar Ali Bhutto Institute of Science and Technology | Data Challenge - Gram Negative Pathogen and Antimicrobial resistance | Venatorx |
| 9022 | Josephat Hema | Swansea University | Data Challenge - Development and impact of carbapenem-resistant pseudomonas aeruginosa (CRPA) in selected low- and middle-income countries. | Pfizer |
| 9025 | Elizabeth Akande | University of Ibadan | AMR challenge - LAST RESORT THREATENED, WHAT NEXT? | Venatorx |
| 9029 | Tunde Egunjobi | Igbinedion University | Data Challenge - Ten-year Trends In Antimicrobial Resistance Amongst Pathogens Isolated From Clinical Samples. | Paratek, Pfizer, Venatorx |
| 9035 | Kessendri Reddy | Stellenbosch University and National Health Laboratory Service | Data Challenge: Long term trends in antimicrobial susceptibility to therapeutic options for carbapenem-resistant A. baumannii, K. pneumoniae and P. aeruginosa in Africa | Pfizer, Shionogi, Venatorx |
| 9038 | Mapoloko Theresia Moholoholo | Ministry of Health Lesotho | Data Challenge: Title : Global scenario of beta-lactams resistance in extraintestinal E.coli among pediatric patients: 2018-2021 | Venatorx |
| 9039 | Areena Hoda Siddiqui | Integral Institute of Medical Sciences and Reasearch, Integral University | Data Challenge-Colistin Sparing Strategies To Overcome Resistance: Can We Save Future ! | Pfizer, Venatorx |
| 9040 | Aishwarya Das | Central Research Laboratory, KIMS | Genomic surveillance of Antimicrobial resistance Data challenge | Pfizer |
| 9043 | Walter Kiirya | Mbarara University of science and technology | Community health workers knowledge about AMR data challenge | Pfizer |
| 9044 | Arryn Craney | Petrified Bugs LLC | Data Challenge - Trending Antibiotic Resistance Profiles | Paratek, Pfizer, Shionogi, Venatorx |
| 9045 | Suchitra Thapa | Tribhuvan University | Data Challenge: Changes in Fluoroquinolone susceptibility for Enterobacteriaceae among adult population in Asia: A trend analysis over a decade | Pfizer |
| 9047 | Imran Sajid | University of the Punjab, Lahore | Data Challenge-Unveiling Novel Resistance Mechanisms: Genomic Insights into Carbapenem-Resistant Gram-Negative Bacteria | Shionogi |
| 9048 | Vuciri Jude Rugga | Ssingo Pharmaceuticals Limited | Comparative analysis of antibiotic susceptibility trends and bacterial resistance patterns among countries participating in the ATLAS program. - DATA CHALLENGE | Pfizer |
| 9049 | Shraddha Karve | Ashoka University | Data challenge - Novel approach to antibiogram analysis: looking at the composite resistance phenotype | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9050 | Yesmi Patricia Ahumada Santos | Universidad Autónoma de Sinaloa | Data Challenge-RESISTANCE TREND ANALYSIS OF MULTI-DRUG RESISTANCE REGISTERED FROM 2016 TO 2021 IN AMERICA COMPARED TO OTHER CONTINENTS | Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9052 | RACHAEL KANGUHA | THARAKA NITHI COUNTY GOVERNMENT | DATA CHALLENGE: PATHOGEN PRESCRIPTION PREDICAMENT: ARE TREATMENT ALGORITHMS FOR THE WHO CRITICAL AND HIGH PRIORITY PATHOGENS APPROPRIATE? | Pfizer |
| 9054 | Mayibongwe Mzingwane | National University of Science and Technology | Data challenge: A predictive Tool on the Geospatial Clinical and Economic Impact of Antimicrobial Resistance (AMR) on paediatric and adult populations. | Pfizer |
| 9055 | Rogers Nsubuga | KABConsult, School of Public Health | Data Challenge - BROAD SPECTRUM ANTIBIOTIC RESISTANCE AMONG NEONATES IN AFRICA | Pfizer |
| 9056 | Jacob Wildfire | LSHTM/SGUL | Data Challenge - Exploration of how the distributions in MIC vary by key population groupings (such as age or infection type) | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9057 | Quentin Leclerc | Institut Pasteur | Data Challenge - Combining datasets to address AMR surveillance gaps | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9058 | Gboeze Christian Tochukwu | University of Nigeria | DATA CHALLENGE- PREDICTIVE ANALYSIS OF AMR TRENDS FOR HEALTHCARE DECISION MAKING AND FORECASTING (PATHFINDER) | Pfizer |
| 9059 | Yanhong Jessika hu | Murdoch Children's Research Institute | Data Challenge - Global Geographic Patterns and Trends of WHO Priority Pathogens and AWaRe Antibiotic Resistances among Children: An Epidemiological Surveillance Study | Pfizer |
| 9060 | Innocent Oruru | Mbarara University of Science and Technology | ADMS(AI DRIVEN MALARIA DIAGNOSIS SYSTEM).data challenge | Venatorx |
| 9062 | SHADIA NAKALEMA | INFECTIOUS DISEASES INSTITUTE, Makerere University | Data Challenge - Driving Evidence-Based Antibiotic Prescribing: Machine Learning-Enabled Point of Care Antibiograms for Improved Patient Care | Pfizer |
| 9063 | Joseph Harwell | Clinton Health Access Initiative | Trends in resistance to AWaRe drugs in Africa Data Challenge | Pfizer |
| 9064 | Louise Cerdeira | LSHTM | Data Challenge - An Evaluation of Klebsiella pneumoniae Antimicrobial Resistance Trends in LMICs | Pfizer, Venatorx |
| 9065 | Julius Kyomya | Mbarara University of Science and Technology | AMRIS data challenge | Pfizer, Venatorx |
| 9066 | Wilson Mudaki | University of Cape Town | Data Challenge - MICRES - Bridging the Gap in Antimicrobial Resistance Decision-Making | Pfizer |
| 9068 | Roshan Naik | Carmel College | DPT Data Challenge | Pfizer, Venatorx |
| 9069 | Randa Elsheikh | University of Edinburgh | Data Challenge - Monitoring the changes in the susceptibility pattern of Pseudomonas aeruginosa to seven classes of antibiotics in the Eastern Mediterranean region from 2004 to 2021. | Pfizer |
| 9070 | Duc Nguyen | Hanoi Medical Hospital | AMR through the eyes of machine learning - Data Challenge | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9072 | Irshaad Hoosain | University of Cape Town | AMR Data Challenge: CAP Pathogen AMR Patterns in LMICs | GSK, Pfizer |
| 9073 | Namubiru Saudah Kizito | Ministry of Health, Uganda | Projecting the Development of Antimicrobial Resistance to Beta-lactam Antibiotics in Africa: A Mathematical Modelling Approach | Pfizer |
| 9074 | Victoria Oladosu | Binghamton University | Data Challenge - Investigating the antibiotics resistance pattern of Pseudomonas aeruginosa and Staphylococcus aureus in patients with co-infections versus patients with mono-infections. | Paratek, Pfizer, Shionogi, Venatorx |
| 9075 | Nguyen Tien Huy | School of Tropical Medicine and Global Health, Nagasaki University | Assessing the Interplay between Antimicrobial Resistant Rates, Government Policy Interventions Worldwide, Vaccine Coverage, and Climate Change: An Empirical Study Utilizing Global Data_AMR Data Challenge | GSK, Paratek, Pfizer |
| 9084 | Ezekiel Jacobs | Global Health Research Unit, University of Ibadan | AMR Data Challenge - modelling and tracking colistin resistance in Africa | Pfizer, Venatorx |
| 9100 | Esther Syombua Ndaka | United States International University- Africa | Trends analysis of AMR in Kenya | Pfizer |
| 9109 | Tanvir Ahmed | Ministry of Health and Family Welfare, Bangladesh | RESISTANT CANDIDA AURIS AT INTENSIVE CARE UNITS IN ASIAN COUNTRIES: EVIDENCE FROM SENTRY ANTIMICROBIAL SURVEILLANCE PROGRAM | Pfizer |
| 9110 | Cory Kromer-Edwards | Machine Translation, LLC | Build MIC predictor using genomic data and antibiotic compounds using bacterial isolates | Pfizer |
| 9112 | Biel Garcias | Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona | Comparation Human AMR vs pets in Spain | Pfizer |
| 9113 | Tanvir Ahmed | Ministry of Health and Family Welfare, Bangladesh | Antibiotic Susceptibility in Children in Asia: Evidence from ATLAS Surveillance Study | Pfizer |
| 9118 | Varsha Shridhar | Molecular Solutions Care Health LLP | India Colistin & Carbapenem resistance study | Venatorx |
| 9391 | Tavpritesh Sethi | Indraprastha Institute of Information Technology Delhi | Machine Learning Modeling for Global Antimicrobial Resistance | GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, Venatorx |
| 9398 | Raunak Shrestha | Nepal Applied Mathematics & Informatics Institute (NAAMII) | Systems Genomics Modelling of Drug Resistance in Mycobacterium tuberculosis | Johnson & Johnson |
| 9408 | Sneha Kotian | Amsterdam UMC, AIGHD | Gender trends in bacterial AMR | Pfizer |
| 9414 | Jaxen Bream | Oakton High School | Assessment of an AI Model to Predict Antibiotic Effectiveness Against Bacterial Pathogens | Pfizer |
| 9435 | Virginia Pierce | University of Michigan Medical School | Evaluation of ampicillin-sulbactam breakpoints for Acinetobacter spp. | Shionogi |
| 9475 | Peyton Tebon | Gryphon Scientific, LLC | AMR in Chemical, Biological, Radiological, and Nuclear (CBRN) Events | Pfizer, Venatorx |
| 9733 | Antoine Villié | Aurobac Therapeutics | Epidemiology research on Gram-bacteria | Venatorx |
| 9762 | Joseph Acolatse | Cape Coast Teaching Hospital | Country comparison of AMR data to local data | Pfizer |
Center for Global Research Data