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Does Online Search Behavior Coincide with Candida auris Cases? An Exploratory Study
Katja Saris1,2,3, Jacques F Meis4,5,6, Jesús Rodriguez Baño7
1Department of Medical Microbiology and Infectious Diseases, Canisius-Wilhelmina Hospital (CWZ), 6532SZ Nijmegen, The Netherlands. katjasaris@gmail.com.
Abstract:
Candida auris is an emerging multidrug resistant infectious yeast which is challenging to eradicate and despite available laboratory methods is still difficult to identify especially in less developed countries. To limit the rapid spread of C. auris, quick and accurate detection is essential. From the perspective of disease surveillance, additional methods of tracking this yeast are needed. In order to increase global preparedness, we explored the use of online search behavior to monitor the recent global spread of C. auris. We used Google Trends to assess online search behavior on C. auris from January 2016 until August 2018. Weekly Google Trends results were counted as hits and compared to confirmed C. auris cases obtained via publications and a global expert network of key opinion leaders. A total of 44 countries generated a hit, of which 30% (13/44) were confirmed known cases, 34% (15/44) were missed known cases, 34% (15/44) were hits for unknown cases, and 2% (1/44) were confirmed unknown cases. Conclusions: Google Trends searches is rapidly able to provide information on countries with an increased search interest in C. auris. However, Google Trends search results do not generally coincide with C. auris cases or clusters. This study did show that using Google Trends provides both insight into the known and highlights the unknown, providing potential for surveillance and tracking and hence aid in taking timely precautionary measures.
Insights
Online search trends can offer early warnings for Candida auris (a multidrug-resistant yeast) spread. While Google Trends data doesn't always align with confirmed cases, it aids in identifying potential hotspots for surveillance.
Area of Science:
- Infectious Diseases
- Public Health Surveillance
- Computational Epidemiology
Background:
- Candida auris is a challenging multidrug-resistant yeast requiring rapid identification and surveillance.
- Current laboratory methods for C. auris detection can be slow and difficult, especially in resource-limited settings.
- Effective disease surveillance requires innovative methods to track the spread of emerging infectious agents.
Purpose of the Study:
- To explore the utility of online search behavior, specifically Google Trends, for monitoring the global spread of Candida auris.
- To assess the correlation between Google Trends data and confirmed C. auris cases for surveillance purposes.
- To evaluate the potential of Google Trends in identifying both known and unknown C. auris outbreaks.
Main Methods:
- Utilized Google Trends data to analyze online search interest for Candida auris from January 2016 to August 2018.
- Collected weekly Google Trends 'hits' and compared them with confirmed C. auris case data from publications and expert networks.
- Analyzed the concordance between search trends and reported cases across 44 countries.
Main Results:
- Google Trends identified search interest in 44 countries, with varying degrees of correlation to confirmed C. auris cases.
- A significant portion of search trends (34%) represented missed known cases, and another 34% indicated searches for unknown cases.
- While search interest often highlighted areas with increased attention to C. auris, direct correlation with case clusters was not consistently observed.
Conclusions:
- Google Trends can rapidly indicate heightened public and professional interest in Candida auris in specific regions.
- The data provides valuable insights into both recognized and unrecognized geographical areas of concern for C. auris.
- Online search behavior analysis offers a supplementary tool for disease surveillance, potentially aiding in timely public health interventions and preparedness.
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