Alberto Jimenez Jimenez 1 , Rosa M Estevez-Reboredo 2 , Miguel A Santed 3 , Victoria Ramos 4 Show Affiliations »
Abstract
BACKGROUND: COVID-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. OBJECTIVE: In 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. METHODS: We 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. RESULTS: In 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. 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 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. ©Alberto Jimenez Jimenez, Rosa M Estevez-Reboredo, Miguel A Santed, Victoria Ramos. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.12.2020.
BACKGROUND: COVID-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. OBJECTIVE: In 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. METHODS: We 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. RESULTS: In 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. 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 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. ©Alberto Jimenez Jimenez, Rosa M Estevez-Reboredo, Miguel A Santed, Victoria Ramos. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.12.2020.
Entities: Chemical
Disease
Species
Keywords:
COVID-19; behavioral epidemiology; big data; forecast; infodemiology; infosurveillance; nowcasting; predict; smart data; tracking
Year: 2020
PMID： 33156803 DOI： 10.2196/23518
Source DB: PubMed Journal: J Med Internet Res ISSN： 1438-8871 Impact factor: 5.428