Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 14, 2020
Date Accepted: Oct 26, 2020
Date Submitted to PubMed: Nov 6, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Individuals' concerns, predict the spread of the coronavirus (COVID 19): the case of Spain
ABSTRACT
Background:
COVID 19 is the first pandemic that has led to a global health crisis. This study is a small contribution that tries to find contrasted formulas to alleviate this global suffering and guarantee a more manageable future.
Objective:
In this study, a statistical approach has been proposed that forecasts the incidence of the COVID 19 epidemic in Spain by means of correlation test and using information from search data provided by Google Trends.
Methods:
Our method consists of the linear correlation between the Google Trends search data and the data provided by the National Center of Epidemiology in Spain -dependent on the Instituto de Salud Carlos III- of cases of COVID 19 reported with a certain time lag, enabling the identification of anticipatory patterns.
Results:
In response to the ongoing outbreak, our results demonstrate that using this correlation test the evolution of the COVID 19 pandemic can be predicted in Spain, up to 11 days in advance.
Conclusions:
During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical (if not dramatic) need for complementary monitoring approaches, which can work on a population level, and inform public health decisions in real time. The study of Google search patterns motivated by the fears of individuals in the face of a pandemic can be useful in anticipating its development.
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Copyright
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