ArticleHealthcare (Basel, Switzerland)2023
Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Application of dimensionality reduction and clustering techniques for the analysis of Carrion's disease cases in the period 2000-2024.Frontiers in artificial intelligence · 2026Article
- Article
- Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- PLANES: Plausibility analysis of epidemiological signals.PloS one · 2025Article
- Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.Nature machine intelligence · 2024Article
- Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach.Malaria journal · 2024Article
- Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach.Frontiers in neuroscience · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.