A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

Nirmalya Thakur (), Shuqi Cui, Kesha A. Patel, Isabella Hall and Yuvraj Nihal Duggal
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Nirmalya Thakur: Department of Computer Science, Emory University, Atlanta, GA 30322, USA
Shuqi Cui: Department of Computer Science, Emory University, Atlanta, GA 30322, USA
Kesha A. Patel: Department of Mathematics, Emory University, Atlanta, GA 30322, USA
Isabella Hall: Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
Yuvraj Nihal Duggal: Department of Computer Science, Emory University, Atlanta, GA 30322, USA

Data, 2023, vol. 8, issue 11, 1-24

Abstract: The World Health Organization (WHO) 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. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. 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.

Keywords: Disease X; big data; data science; data analysis; dataset development; database; google trends; data mining; healthcare; epidemiology (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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