INTRODUCTION: The primary aim of this study is to evaluate the temporal correlation between Google Trends and the data on measles infection arising from the conventional surveillance system, reported by the Istituto Superiore di Sanità's (ISS) bulletin. Moreover, this study is also aimed at forecasting the trends of the reported infectious diseases cases over time. MATERIAL AND METHODS: The reported cases of measles were selected from January 2013 until October 2018. The data on Internet searches have been obtained from Google Trends; the research data referred to the first 48 weeks of year 2017 have been aggregated on a weekly basis. The search volume provided by Google Trends has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. The statistical analyses have been performed by using the Spearman's rank correlation coefficient (rho). The statistical significance level for such analyses has been fixed in 0.05. OUTCOMES: We have observed a strong correlation at a lag of 0 to -4 weeks (rho > 0.70) with the cases reported by ISS with the strongest correlation at a lag of -3 weeks (rho > 0.80 both for measles than for the symptoms of the measles). The database containing monthly data has shown a moderate correlation at a lag of -1 to +1 months and a strongest correlation at a lag of -1 (rho = 0.6152 for measles and rho = 0.5039 for symptoms of the measles). CONCLUSIONS: The surveillance systems based on Google Trends have a potential role in public health in order to provide near real-time indicators of the spread of infectious diseases. Therefore the huge potential of this approach could be used in the immediate future as a support of the traditional surveillance systems.
INTRODUCTION: The primary aim of this study is to evaluate the temporal correlation between Google Trends and the data on measles infection arising from the conventional surveillance system, reported by the Istituto Superiore di Sanità's (ISS) bulletin. Moreover, this study is also aimed at forecasting the trends of the reported infectious diseases cases over time. MATERIAL AND METHODS: The reported cases of measles were selected from January 2013 until October 2018. The data on Internet searches have been obtained from Google Trends; the research data referred to the first 48 weeks of year 2017 have been aggregated on a weekly basis. The search volume provided by Google Trends has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. The statistical analyses have been performed by using the Spearman's rank correlation coefficient (rho). The statistical significance level for such analyses has been fixed in 0.05. OUTCOMES: We have observed a strong correlation at a lag of 0 to -4 weeks (rho > 0.70) with the cases reported by ISS with the strongest correlation at a lag of -3 weeks (rho > 0.80 both for measles than for the symptoms of the measles). The database containing monthly data has shown a moderate correlation at a lag of -1 to +1 months and a strongest correlation at a lag of -1 (rho = 0.6152 for measles and rho = 0.5039 for symptoms of the measles). CONCLUSIONS: The surveillance systems based on Google Trends have a potential role in public health in order to provide near real-time indicators of the spread of infectious diseases. Therefore the huge potential of this approach could be used in the immediate future as a support of the traditional surveillance systems.
Entities:
Keywords:
Big Data; Internet; Italy; Measles; Measles Vaccine; Medical Informatics; Medical Informatics Computing; Vaccine-preventable diseases
Authors: Thomas S Higgins; Arthur W Wu; Dhruv Sharma; Elisa A Illing; Kolin Rubel; Jonathan Y Ting Journal: JMIR Public Health Surveill Date: 2020-05-21