Public health surveillance refers to the continuous and systematic process of collection, analysis, and interpretation of health-related data. Surveillance data are crucially important for different purposes such as recognizing disease trends and finding the factors associated with the increase or decrease of infections and/or deaths. Moreover, the data helps to identify high-risk groups as well as the geographical areas requiring control measures.

The spread of new diseases is becoming relevant to our lives due to unpredictable contexts such as climate change, making the environment more habitable to microorganisms, and permanent deforestation that exposes humans to new strains of viruses and bacteria. The appearance of SARS-CoV-2 has stressed the need to improve surveillance systems and protocols to increase the efficiency and effectiveness of public health systems. Therefore, the understanding of health issues such as etiology, distribution, and mechanism of infection is still an open challenge of utmost importance. It is necessary to improve the treatment of many diseases with appropriate medical data.

The aim of this Special Issue is to focus on the development and application of computational models, machine learning, data mining, and data analytics algorithms to public health surveillance data. We invite authors to contribute with original research and review articles discussing all aspects of public health surveillance.

Potential topics include but are not limited to the following:

• Machine learning techniques applied to public health surveillance data
• Exploratory data analysis of public health surveillance data
• Public health surveillance systems
• Knowledge discovery and pattern recognition from public health surveillance data
• Pre-processing of public health surveillance data