Mathematical models are widely used in the study of infectious diseases, providing valuable theoretical insights into the logic of pathogen spread, as well as guiding public health strategies. Digital technologies have the potential to transform healthcare, and “big data” approaches already show promise in improving the diagnosis and treatment of chronic diseases such as diabetes, heart conditions, cancer, and Alzheimer’s. Yet the large amounts of health data collected by hospitals and healthcare facilities are not used as effectively in the study and management of infectious diseases.
The objective of this collection is to explore new ways to analyze local clinical data, methodologies that will help unify various local data sources to reach more general conclusions, and novel approaches that may be useful in real-life clinical settings.
Examples of types of studies to be included in this collection are: characterizing transmission networks within healthcare facilities and the community they serve, methods to combine multiple local data sources while maintaining patient privacy, computational methods to diagnose the presence of specific pathogens in the absence of specific molecular test data, using non-traditional data sources in epidemiology (e.g. GPS or cellphone location data), and developing more accessible and intuitive methodologies that could be used by the general medical community.