Ondrikova, Nikola 
ORCID: 0000-0003-4061-2901, Harris, John P, Douglas, Amy, Hughes, Helen E, Iturriza-Gomara, Miren ORCID: 0000-0001-5816-6423, Vivancos, Roberto, Elliot, Alex J ORCID: 0000-0002-6414-3065, Cunliffe, Nigel A ORCID: 0000-0002-5449-4988 and Clough, Helen E
(2023)
Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study.
Journal of medical Internet research, 25.
e37540-e37540.
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Abstract
<h4>Background</h4>Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control.<h4>Objective</h4>This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England.<h4>Methods</h4>We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region.<h4>Results</h4>Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources.<h4>Conclusions</h4>Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information-seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.
| Item Type: | Article |
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| Uncontrolled Keywords: | syndromic data, syndromic surveillance, surveillance, infodemiology, norovirus, Google Trends, Wikipedia, prediction, variable importance, mental model, infoveillance, trend, gastroenteritis, gastroenterology, gastroenterologist, internal medicine, viral disease, viral, virus, communicable disease, infection prevention, infection control, infectious disease, viral infection, disease spread, big data, Granger causality framework, predict, model, web-based data, internet data, transmission |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 May 2023 07:28 |
| Last Modified: | 26 Jul 2023 22:50 |
| DOI: | 10.2196/37540 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3170336 |
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