Data Challenge_Anticipating the Threat: A Machine Learning approach to predicting antimicrobial resistance (AMR)

Anticipating the Threat: A Machine Learning approach to predicting antimicrobial resistance (AMR)
Antimicrobial resistance (AMR) is a growing global health threat, demanding innovative approaches for prediction and mitigation. Traditional surveillance methods are often reactive, limiting their effectiveness. We propose a novel, proactive approach: a multi-disciplinary machine learning (ML) model to predict seasonal trends and spatial variations in AMR development.
The proposed model aims to create a comprehensive risk assessment tool by integrating data from diverse sources. Clinical data, including AMR patterns, organism susceptibility and resistance mechanisms, and patient demographics, will be obtained from existing resources like the VIVLI AMR SMART surveillance heatmap and data request datasets, GEARS data set and the ATLAS data set. Environmental factors are crucial for understanding AMR emergence. The model will integrate climate data from the National Centers for Environmental Information (NCEI) [https://www.ncei.noaa.gov/access/search/data-search/global-hourly?bbox=75.672,-82.969,-85.051,159.609&pageNum=1] and the IMF climate change dashboard [https://climatedata.imf.org/pages/climatechange-data], alongside land use patterns retrieved from the Global Land Analysis and Discovery database (GLAD) [https://glad.umd.edu/dataset]. Antibiotic consumption is a well-established driver of AMR. Data on consumption patterns will be incorporated from the European Centre for Disease Prevention and Control (ECDC) [https://qap.ecdc.europa.eu/public/extensions/AMC2_Dashboard/AMC2_Dashboard.html#eu-consumption-tab].To understand the potential for future resistance acquisition, the model will delve into genetic data. Bacterial genomic data from the National Center for Biotechnology Information (NCBI) [https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/] and AMR genetic and antibiotic susceptibility data from the Database of Antibiotic-Resistant Organisms (DARO) will be integrated. This comprehensive dataset will empower the ML model to predict not only AMR emergence but also identify geographic hotspots at high risk for seasonal outbreaks.
By anticipating potential outbreaks, this model has the potential to revolutionize public health responses. Public health and government agencies can leverage the model's predictive capabilities to proactively allocate resources to high-risk areas, potentially preventing outbreaks before they occur.