Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

doi:10.3390/healthcare11131896

Peter U. Eze, Nicholas Geard, Ivo Mueller, Iadine Chades

Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

Health Information Management
Health Informatics
Health Policy
Leadership and Management

Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a case study, in this paper we explore the potential for unsupervised anomaly detection machine learning techniques to discover signals of epidemiological interest. We found that our models were able to provide an early indication of outbreak onset, outbreak peaks, and change points in the proportion of positive malaria cases. Specifically, the sustained rise in malaria in the Brazilian Amazon in 2016 was flagged by several models. We found that no single model detected all anomalies across all health regions. Because of this, we provide the minimum number of machine learning models top-k models) to maximise the number of anomalies detected across different health regions. We discovered that the top three models that maximise the coverage of the number and types of anomalies detected across the thirteen health regions are principal component analysis, stochastic outlier selection, and the minimum covariance determinant. Anomaly detection is a potentially valuable approach to discovering patterns of epidemiological importance when confronted with a large volume of data across space and time. Our exploratory approach can be replicated for other diseases and locations to inform monitoring, timely interventions, and actions towards the goal of controlling endemic disease.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

Web-based, modern reference management
Collaborate and share with fellow researchers
Integration with Overleaf
Comprehensive BibTeX/BibLaTeX support
Save articles and websites directly from your browser
Search for new articles from a database of tens of millions of references

Try out CiteDrive

Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

More from our Archive

Antimicrobial Resistance: A Growing Serious Threat for Global Public Health

Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers

Medical artificial intelligence ethics: A systematic review of empirical studies

Self-Compassion during COVID-19 in Non-WEIRD Countries: A Narrative Review

The Effect of Cardiopulmonary Resuscitation (CPR) Education on the CPR Knowledge, Attitudes, Self-Efficacy, and Confidence in Performing CPR among Elementary School Students in Korea

ChatGPT Knowledge Evaluation in Basic and Clinical Medical Sciences: Multiple Choice Question Examination-Based Performance

Perceptions of a machine learning-based lower-limb exercise training system among older adults with knee pain

Lessons for Medical and Health Education Learned from the COVID-19 Pandemic

Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

The Prevalence and Associated Factors of Academic Stress among Medical Students of King Khalid University: An Analytical Cross-Sectional Study