Abstract
BackgroundCOVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future.ObjectiveIn this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends.MethodsWe assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns.ResultsIn response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance.ConclusionsDuring 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 that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.
Highlights
During the 2020 Chinese Lunar New Year, massive measures for reducing the spread of the new COVID-19 disease were first enacted by authorities in China [1]
We aimed to determine whether Google Trends data that are collected for searches using many different keywords that the public has entered into Google's internet search engine during the COVID-19 outbreak period can predict the number of cases reported by the National Center of Epidemiology in Spain (Centro Nacional de Epidemiología [Centro Nacional de Epidemiología polymerase chain reaction (PCR) (CNE)])
This study showed that the data obtained from Google Trends searches for Spanish keywords related to COVID-19 correlated with data published by the CNE on the daily incidence of laboratory PCR-confirmed COVID-19 cases, hospitalization, intensive care unit admissions, and deaths from COVID-19, going from R=0.635 for “fatigue” to a maximum of R=0.908 for “fever”
Summary
Background During the 2020 Chinese Lunar New Year, massive measures for reducing the spread of the new COVID-19 disease were first enacted by authorities in China [1]. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020 [3]. Spain has the fifth highest number of detected COVID-19 cases in the world, behind the United States, Brazil, Russia, and the United Kingdom [4]. As a consequence, developing a forecasting tool to predict the spread of the epidemic has become critical. This information can help us understand the evolution of COVID-19 and how it affects our health. Such information can even be useful in preparing for possible future COVID-19 waves and other pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future
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