DOI: 10.25934/pr00010426
title: A LONGITUDINAL STUDY FOR FORECASTING TRENDS IN AMR- A ONE-HEALTH APPROACH

description: A LONGITUDINAL STUDY FOR FORECASTING TRENDS IN AMR- A ONE-HEALTH APPROACH

Project Overview
 AMR is a significant global health problem, with an estimated 10 million people projected to die of AMR each year by 2050 if not addressed. Pathogens are becoming increasingly resistant to traditional antibiotics, they become harder to treat and can lead to more severe illnesses and even death, requiring innovative solutions that bridge human and animal domains. This project leverages the One Health approach to predict AMR trends by integrating data from various sources, including human clinical data from the Vivli AMR register, animal data from cattle AMR studies and sewage AMR data. By utilising advanced machine learning (ML) techniques, the project aims to generate comprehensive insights into AMR dynamics, ultimately informing more effective public health strategies.

Objectives

The core objective of this study is to integrate data from humans and animals to forecast future AMR trends. By adopting a One Health approach, we aim to understand the interconnected dynamics of AMR across humans, animals and the environment.. This interdisciplinary strategy is essential for addressing the multifaceted nature of AMR and ensuring effective and sustainable solutions. 

Data Sources

The project will draw on multiple data sources to achieve its objectives:
1. Vivli AMR Register: This dataset includes extensive information on human pathogens, antibiotic resistance profiles, and temporal and geographical trends. It provides a robust foundation for analysing AMR patterns in human populations.
2. Cattle AMR Metadata: This dataset encompasses data on various cattle pathogens, their resistance profiles, types of samples collected, geographic locations, and temporal information. This data is crucial for understanding AMR in animal populations and its potential crossover to human health.
3. Sewage AMR data: This dataset encompasses metagenomic sequenced data from sewage samples from different locations. Sewage collects the general composition of the environmental microbiome. This data will enable us to understand AMR in the environment and its connectedness to humans.

Methodology

The methodology of this project involves several key steps, beginning with data standardisation and cleaning. This initial step ensures that the datasets are harmonised in terms of formats, terminologies, and units, which is essential for accurate analysis. Missing values and outliers will be addressed to maintain data quality.

Next, exploratory data analysis (EDA) will be conducted to visualise data distributions, identify key trends, and uncover correlations between human and animal AMR data. EDA will provide the groundwork for more advanced analysis by highlighting significant patterns and potential areas of interest.

Feature engineering will follow, where relevant features such as antibiotic usage patterns, geographic hotspots, and temporal trends will be developed. These features will serve as inputs for the predictive models.

The core of the project lies in model development. Using ML models like Random Forest among others, the project will predict AMR trends based on historical data. These models will be trained and validated using cross-validation techniques to ensure their robustness and accuracy.

Incorporating environmental data will add another layer of complexity and depth to the analysis. Factors such as climate, soil quality, and water quality will be integrated to understand their influence on AMR trends. This holistic approach aligns with the One Health framework, recognizing that environmental health is intrinsically linked to human and animal health.

A critical component of the project is the One Health analysis. This analysis will assess how animal health and environmental practices influence human AMR trends, providing insights into the interconnectedness of different health domains.

Innovation and Impact

The innovative core of this project lies in developing predictive models that incorporate a One Health approach. By integrating diverse datasets from human and animal health domains and considering environmental factors, the project provides a comprehensive understanding of AMR dynamics in human, environmental and  animal data, ensuring they are effective and sustainable.

Expected Outcomes

The project is expected to yield several significant outcomes. First, it will enhance our ability to predict future AMR trends in both human and animal populations, providing a valuable tool for public health planning and response. Second, it will deepen our understanding of the One Health relationship in AMR dynamics, highlighting the interconnectedness of human, animal and environmental  health. 

By integrating diverse datasets and applying advanced ML techniques, this project aims to comprehensively understand AMR trends. The insights generated will inform targeted interventions and support the development of sustainable, effective public health policies. This holistic, One Health approach recognizes that addressing AMR requires a coordinated effort across multiple domains, making this project a vital contribution to the fight against AMR.