Dr. Amy Marie Campbell
Key Publications
First Author Academic Papers
ORCID: 0000-0003-4111-8286
Machine learning potential for identifying and forecasting complex environmental drivers of Vibrio vulnificus infections in the USA
Environmental Health Perspectives,
January 2025
This paper used the unique opportunity offered by the USA Vibrio epidemiological surveillance database, and machine learning (in the form of random forest classifiers) to forecast V. vulnificus infections and characterise complex non-linear environmental drivers. The highest-performing model had an Area Under the Curve score of 0.984 and a sensitivity of 0.971. This study accentuates the potential of machine learning and robust surveillance for forecasting environmentally-associated marine infections.
Identifying gene-level mechanisms of successful dispersal of Vibrio parahaemolyticus during El Niño events
Microbial Genomics,
November 2024
This paper focuses on the role of wider climate cycles, particularly the El Niño Southern Oscillation, on Vibrio dynamics, and tested the potential of state-of-the-art methodologies (in time series analysis and machine learning) to characterise this. It quantifies the previously-hypothesized lagged impact of strong El Niño events on Vibrio populations, and explored gene-level dispersal mechanisms related to plankton attachment that underline such associations.
A machine learning approach to identify eco-evolutionary drivers of Vibrio parahaemolyticus ST3 expansion
JMIR Bioinformatics and Biotechnology,
November 2024
This paper characterises the extent to which eco-evolutionary drivers were involved in VpST3 expansion process, using genomic data from clinical and environmental VpST3 sequences collected globally over the period of expansion. A range of ecological and evolutionary drivers are tested for their potential in predicting VpST3 expansion
dynamics within a machine learning approach.
Evolutionary dynamics of the successful expansion of pandemic Vibrio parahaemolyticus ST3 in Latin America
Nature Communications, September 2024
This paper uncovers the emergence of the pandemic clone (VpST3) into Latin America, identifying the mechanisms of VpST3 expansion into a distinct marine climate, as the first detection outside its endemic region of tropical Asia. This paper identified signatures of successful adaptation to local environmental conditions that contribute to the emergence and establishment of a variant into new, geographic regions with distinct climates.
Forecasting climate-associated non-tuberculous mycobacteria (NTM) infections in the UK using international surveillance data and machine learning
PLOS Global Public Health,
August 2024
This paper assessed the burden of NTM infections in the UK under projected climate change, by examining the relationship between climate variables and available NTM surveillance data internationally, and training a machine learning model with these associations to provide forecasts for the UK.
An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens
Current Opinion in Biotechnology,
February 2023
This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change.
Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables
International Journal of Environment and Public Health,
December 2020
In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. 89.5% of coastal cholera outbreaks are correctly identified using the model.