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Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study
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 continu...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429048/ https://www.ncbi.nlm.nih.gov/pubmed/30763255 http://dx.doi.org/10.2196/13142 |
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author | Bragazzi, Nicola Luigi Mahroum, Naim |
author_facet | Bragazzi, Nicola Luigi Mahroum, Naim |
author_sort | Bragazzi, Nicola Luigi |
collection | PubMed |
description | 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 (R(2)=0.549), suspected (R(2)=0.265), and probable (R(2)=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. |
format | Online Article Text |
id | pubmed-6429048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-64290482019-04-17 Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study Bragazzi, Nicola Luigi Mahroum, Naim JMIR Public Health Surveill Original Paper 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 (R(2)=0.549), suspected (R(2)=0.265), and probable (R(2)=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. JMIR Publications 2019-03-08 /pmc/articles/PMC6429048/ /pubmed/30763255 http://dx.doi.org/10.2196/13142 Text en ©Nicola Luigi Bragazzi, Naim Mahroum. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 08.03.2019. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Bragazzi, Nicola Luigi Mahroum, Naim Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title | Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title_full | Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title_fullStr | Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title_full_unstemmed | Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title_short | Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study |
title_sort | google trends predicts present and future plague cases during the plague outbreak in madagascar: infodemiological study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429048/ https://www.ncbi.nlm.nih.gov/pubmed/30763255 http://dx.doi.org/10.2196/13142 |
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