  The World Health Organization added Disease X to their shortlist of blueprint
priority diseases to represent a hypothetical, unknown pathogen that could
cause a future epidemic. During different virus outbreaks of the past, such as
COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various
disciplines utilized Google Trends to mine multimodal components of web
behavior to study, investigate, and analyze the global awareness, preparedness,
and response associated with these respective virus outbreaks. As the world
prepares for Disease X, a dataset on web behavior related to Disease X would be
crucial to contribute towards the timely advancement of research in this field.
Furthermore, none of the prior works in this field have focused on the
development of a dataset to compile relevant web behavior data, which would
help to prepare for Disease X. To address these research challenges, this work
presents a dataset of web behavior related to Disease X, which emerged from
different geographic regions of the world, between February 2018 and August
2023. Specifically, this dataset presents the search interests related to
Disease X from 94 geographic regions. The dataset was developed by collecting
data using Google Trends. The relevant search interests for all these regions
for each month in this time range are available in this dataset. This paper
also discusses the compliance of this dataset with the FAIR principles of
scientific data management. Finally, an analysis of this dataset is presented
to uphold the applicability, relevance, and usefulness of this dataset for the
investigation of different research questions in the interrelated fields of Big
Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific
focus on Disease X.
