Outbreaks of infectious diseases are unpredictable and can have major detrimental impacts and long-lasting effects on human society and public health. The digitalization and digital surveillance systems applied to develop infectious disease control measures have emerged as a transformative approach in public health, potentially capable to improve the way we monitor, diagnose, control and communicate about infectious diseases. From digital epidemiology and real-time monitoring to the use of computational modeling and machine learning for predicting disease spread and prevention strategies, the integration of technology and laboratory research represents a powerful tool to understand the epidemiology of infectious diseases.
Emerging technological developments in laboratory and epidemiologic methods, combined with the use of big data, increasing computational power and applications of machine learning/artificial intelligence can significantly improve how we study and control the spreading of infectious diseases and zoonoses. In support of the UN Sustainable Development Goal 3 (SDG 3), ‘Good health and well-being’, BMC Microbiology welcomes submissions to the collection, Digitalization in infectious disease control measures. The collection aims to explore the innovative use of digital technologies in infectious disease surveillance and control, as well as diagnosis. Research must focus on infectious diseases and be in scope for the journal. Manuscripts focusing exclusively on digital technologies and tools, as well as on digital communication, will not be considered. We invite researchers to submit research articles that cover, but are not limited to, the following topics:
- Infectious disease digital surveillance to prevent and control outbreaks
- Development and use of digital data and technology for epidemiological research and infectious disease tracking
- Big data and digital platforms for infectious disease surveillance and modeling
- Computational modeling to study the complex behavior of infectious diseases
- Machine learning and artificial intelligence applications/tools for predicting and preventing the spreading of infectious diseases
- Development and applications of digital tools for the diagnosing of infectious diseases
- Advances in real-time monitoring of infectious diseases for early detection and response
- Development and use of cloud computing for infectious disease surveillance and control, including data security and federated data governance
- Digital technologies applied to One Health approach
- Potential barriers and solutions for the adaptation of digital technologies applied to infectious disease surveillance and control by decision-makers
- Applications of digital twin technology towards infectious diseases
- Incorporating socio-behavioral mechanisms to study transmission and disease risks
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