Event-based surveillance: Providing early warning for communicable disease threats.

The coronavirus disease 2019 pandemic served as a compelling modern-day reminder of the value of early warning against communicable disease threats in public health. As countries exit the acute phase of the pandemic, there remains a continued need to be vigilant for potential communicable disease threats, particularly as the risk of animal-to-human spillover events is increasing due to climate and land use change. Early warning of emerging threats facilitates earlier public health response, which affords more time to implement public health measures that can help minimize the impact of a particular health threat and protect the health and well-being of the population. One approach to providing early warning for communicable disease and other threats is through event-based surveillance (EBS). However, EBS is not often discussed in the context of public health surveillance. This overview introduces EBS and how it might contribute to providing early warning for communicable disease threats.

Application of natural language processing algorithms for extracting information from news articles in event-based surveillance.

The focus of this article is the application of natural language processing (NLP) for information extraction in event-based surveillance (EBS) systems. We describe common information extraction applications from open-source news articles and media sources in EBS systems, methods, value in public health, challenges and emerging developments.