Local-to-global analysis of influenza-like-illness data

author: João Pita Costa, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Nov. 14, 2019,   recorded: October 2019,   views: 8


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The need for appropriate, robust and efficient epidemic intelligence tools is increasing in this age of a connected society. Global health initiatives, such as Influenzanet, potentially have a central role in the future of Public Health. This paper presents the contributions to the Influenzanet initiative, describing a new monitoring system for local hubs and their data sources, based on Elasticsearch. It is often the case that the exploration of internally generated data is prioritised by national public health institutions, and therefore cannot be addressed in the global Influenzanet platform. This platform can be used by health professionals without programming expertise to encourage and enhance their independence from busy in-house IT departments and further contribute to the effectiveness of their own research. The most meaningful data visualization modules can then be considered for integration into the full Influenzanet platform that will serve the complete network, thus collaborating at a global level. With this approach we also show the importance that an active hub in carrying out its own investigations towards its own priorities. In that regard and as an example, we also describe new results on the application of state-of-the-art approaches to a local data set, using the Portuguese ILI seasons between 2005 and 2013. This study is based on the application of the Streamstory approach. It aims to show the potential of this versatile approach in: (i) identifying data-driven ILI seasons; (ii) relating the ILI incidence to the dimensions of weather data; and (iii) comparing the incidence throughout four different ILI definitions.

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