Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study
The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all coun...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Tropical Medicine and Infectious Disease |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2414-6366/8/2/75 |
| _version_ | 1797617907821182976 |
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| author | Dawei Wang Andrea Guerra Frederick Wittke John Cameron Lang Kevin Bakker Andrew W. Lee Lyn Finelli Yao-Hsuan Chen |
| author_facet | Dawei Wang Andrea Guerra Frederick Wittke John Cameron Lang Kevin Bakker Andrew W. Lee Lyn Finelli Yao-Hsuan Chen |
| author_sort | Dawei Wang |
| collection | DOAJ |
| description | The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data. |
| first_indexed | 2024-03-11T08:02:40Z |
| format | Article |
| id | doaj.art-3f04466230274af8aa9de6258b3a266d |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-03-11T08:02:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-3f04466230274af8aa9de6258b3a266d2023-11-16T23:39:34ZengMDPI AGTropical Medicine and Infectious Disease2414-63662023-01-01827510.3390/tropicalmed8020075Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case StudyDawei Wang0Andrea Guerra1Frederick Wittke2John Cameron Lang3Kevin Bakker4Andrew W. Lee5Lyn Finelli6Yao-Hsuan Chen7Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USAClinical Development, MSD, Kings Cross, London EC2M 6UR, UKClinical Development, MSD, CH-6005 Luzern, SwitzerlandHealth Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USAHealth Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USAClinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USAClinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USAHealth Economic and Decision Sciences, MSD, Kings Cross, London EC2M 6UR, UKThe COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.https://www.mdpi.com/2414-6366/8/2/75RSVGoogle TrendsepidemiologyMoving Epidemic Methodnowcastreal-time |
| spellingShingle | Dawei Wang Andrea Guerra Frederick Wittke John Cameron Lang Kevin Bakker Andrew W. Lee Lyn Finelli Yao-Hsuan Chen Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study Tropical Medicine and Infectious Disease RSV Google Trends epidemiology Moving Epidemic Method nowcast real-time |
| title | Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study |
| title_full | Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study |
| title_fullStr | Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study |
| title_full_unstemmed | Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study |
| title_short | Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study |
| title_sort | real time monitoring of infectious disease outbreaks with a combination of google trends search results and the moving epidemic method a respiratory syncytial virus case study |
| topic | RSV Google Trends epidemiology Moving Epidemic Method nowcast real-time |
| url | https://www.mdpi.com/2414-6366/8/2/75 |
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