Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

Efficacy of COVID-19 Vaccines in Patients with Hematological Malignancy Compared to Healthy Controls: A Systematic Review and Meta-analysis

“…Two different meta-analyses were performed: one for the outcome after the first dose and another for the outcome after the second dose. All analyses were performed using the programing language R (19) .…”

Early Detection and Monitoring of Gastrointestinal Infections Using Syndromic Surveillance: A Systematic Review

“…The 29 studies identified from the full screen included a range of different syndromic surveillance systems from ten countries and territories across Korea, United States, France, Sweden, Netherlands, Spain, Canada, United Kingdom, Japan, and Portugal. Just over half of the studies (52%, 15/29) were reported from the United States and United Kingdom [8,[12][13][14][15][16][17][18][19][20][21][22][23][24][25]. About 55% (16/29) [13,16,[22][23][24][25][26][27][28][29][30][31][32][33][34][35] of the studies indicated that syndromic surveillance exhibited the potential to detect GI infections earlier than traditional surveillance, while a further 10% (3/29) of studies indicated that syndromic surveillance exhibits the potential to detect GI infections or symptoms at an early stage but with limitations [15,20,36]; a further 3% (1/29) highlighted that syndromic surveillance exhibits potential to detect GI infections or symptoms at an early stage only when combined with traditional surveillance system [18].…”

“…The syndromic surveillance systems reported in the selected studies described different sources of syndromic data, of which the emergency department (ED) setting was the most frequently utilized [12,[15][16][17]19,24,27,33,34,36]. Of the included studies, 3/29 reported the use of multiple or combined syndromic surveillance systems [24,25,27]. The other systems reported in the included studies were National Health Service (NHS) Direct calls (a telehealth system) [13], GP surveillance [26,29,38], integrated medical records from inpatient, outpatient, laboratory, and pharmacy data [18,21,[23][24][25]27,28,35,39], electronic medical records [8,18,23,32], a nurse advice hotline [21], nursing home public health surveillance system [28], internet search queries [30], and nursery school absenteeism [31].…”