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A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths
Author(s) -
O'Leary Daniel E.,
Storey Veda C.
Publication year - 2020
Publication title -
intelligent systems in accounting, finance and management
Language(s) - English
Resource type - Journals
eISSN - 1099-1174
pISSN - 1055-615X
DOI - 10.1002/isaf.1482
Subject(s) - pandemic , social media , coronavirus , covid-19 , outbreak , disease , computer science , internet privacy , data science , world wide web , geography , medicine , virology , infectious disease (medical specialty) , pathology
Summary Forecasting the number of cases and the number of deaths in a pandemic provides critical information to governments and health officials, as seen in the management of the coronavirus outbreak. But things change. Thus, there is a constant search for real‐time and leading indicator variables that can provide insights into disease propagation models. Researchers have found that information about social media and search engine use can provide insights into the diffusion of flu and other diseases. Consistent with this finding, we found that a model with the number of Google searches, Twitter tweets, and Wikipedia page views provides a leading indicator model of the number of people in the USA who will become infected and die from the coronavirus. Although we focus on the current coronavirus pandemic, other recent viruses have threatened pandemics (e.g. severe acute respiratory syndrome). Since future and existing diseases are likely to follow a similar search for information, our insights may prove fruitful in dealing with the coronavirus and other such diseases, particularly in the early phases of the disease. Subject terms : coronavirus, COVID‐19, unintentional crowd, Google searches, Wikipedia page views, Twitter tweets, models of disease diffusion.

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