%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 5
%N 1
%P e13142
%T Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study
%A Bragazzi,Nicola Luigi
%A Mahroum,Naim
%+ Department of Health Sciences, Postgraduate School of Public Health, University of Genoa, Via Antonio Pastore 1, Genoa, 16132, Italy, 39 0103538508, robertobragazzi@gmail.com
%K plague
%K infodemiology
%K infoveillance
%K infectious outbreaks
%K Google Trends
%K nowcasting and forecasting models
%K digital surveillance
%D 2019
%7 08.03.2019
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X Background: Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective: In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public’s interest. Methods: We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results: Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions: An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread. 
%M 30763255
%R 10.2196/13142
%U http://publichealth.jmir.org/2019/1/e13142/
%U https://doi.org/10.2196/13142
%U http://www.ncbi.nlm.nih.gov/pubmed/30763255