Machine Learning and Python for Deep Learning in Public Health Surveillance

Sanimgul Sambaeva

2025

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.4103/wkpj.wkpj_18_26
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

The rapid evolution of artificial intelligence (AI) has significantly transformed public health surveillance, particularly through the application of machine learning (ML) and deep learning techniques. Traditional surveillance systems, which rely on manual reporting and conventional statistical models, often suffer from reporting delays, limited scalability, and reduced sensitivity to emerging health threats. In contrast, ML-based approaches enable automated, real-time analysis of large and heterogeneous datasets, including epidemiological records, medical imaging, textual data, mobility patterns, and digital traces from the web. This minireview summarizes recent advances (2021–2026) in the use of ML and deep learning for disease surveillance, with a specific emphasis on Python-based model development and the use of Google Colab as a cloud-based computational platform. Deep learning architectures such as convolutional neural networks and recurrent neural networks have demonstrated strong performance in outbreak detection, epidemic forecasting, syndromic surveillance, and infodemiology. Python’s extensive ecosystem of open-source libraries, combined with the accessibility and reproducibility offered by Google Colab, has lowered technical barriers and facilitated collaborative and transparent AI research in public health. Despite these advances, challenges remain, including data quality, algorithmic bias, model interpretability, and privacy protection. Emerging solutions such as explainable AI and federated learning offer promising pathways to address ethical and governance concerns. Overall, the integration of ML, Python programming, and cloud-based development environments represents a powerful and evolving framework for strengthening public health surveillance and improving preparedness for future disease outbreaks.