JMIR Publications

ABSTRACT

Background:

Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to irreproducible assessments of high-consequence, public-health events and in turn poor disease response prioritization, misallocation of resources, and ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about a large number of factors at the local level and disease case counts for the entire world. Collection and systematic analysis of this data may be time consuming.

Objective:

This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. It uses machine learning and visual analytics to help detect potential local disease re-emergence, identify potential factors contributing the local re-emergence, and assess potential for the global disease re-emergence.

Methods:

RED Alert collects and stores various disease-related data (e.g., case counts, vaccination rates, and related indicators) and provides machine learning and visual analytics to help detect and understand disease re-emergence through both local and global contextual data analysis.

Results:

RED Alert is a web-based, easy to use, and freely available (at https://redalert.bsvgateway.org/) tool that can help detect and understand disease re-emergence for following diseases at the country level and yearly time scale: measles, cholera, dengue, and yellow fever. We present a few case studies to show utility of the tool.

Conclusions:

To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.