Abstract

Disasters and disease outbreaks have long been a catalyst for innovative applications of emerging technologies. The urgent need to respond to an emergency leads to resourceful uses of the technologies at hand. However, the best and most cost-effective use of new technologies is to prevent disease and improve resilience. In this paper, the authors present a range of approaches through which both opportunities can be grasped. Global connectedness enables more data to be collected and processed in emergencies, especially with the rise of open-source data, including social media. In general, the poorest and most remote populations are most vulnerable to disaster. However, with smaller, faster, smarter, cheaper and more connected technology, reliable, efficient, and targeted response and recovery can be provided. Initially, crowdsourcing was used to find people, map affected areas, and determine resource allocation. This led to the generation of an overwhelming amount of data, and the need to extract valuable information from that data in a timely manner. As technology evolved, organisations started outsourcing many tasks, first to other people, then to machines. Since the volume of data generated outpaces human capacity, data analysis is being automated using artificial intelligence and machine learning, which furthers our abilities in predictive analytics. As we move towards prevention rather than remediation, information collection and processing must become faster and more efficient while maintaining accuracy. Moreover, these new strategies and technologies can help us to move forwards, by integrating layers of human, veterinary, public, and environmental health data for a One Health approach.

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