Detecting the norovirus season in Sweden using search engine data--meeting the needs of hospital infection control teams

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

The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: 'early warning', 'detection', 'gastrointestinal activity', 'gastrointestinal infections', 'syndrome monitoring', 'real-time monitoring', 'syndromic surveillance'. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.

The amount of SARS-CoV-2 RNA in wastewater relates to the development of the pandemic and its burden on the health system

Virus surveillance in wastewater can be a useful indicator of the development of the COVID-19 pandemic in communities. However, knowledge about how the amount of SARS-CoV-2 RNA in wastewater relates to different data on the burden on the health system is still limited. Herein, we monitored the amount of SARS-CoV-2 RNA and the spectrum of virus variants in weekly pooled wastewater samples for two years from mid-February 2020 and compared with several clinical data. The two-year monitoring showed the weekly changes in the amount of viral RNA in wastewater preceded the hospital care needs for COVID-19 and the number of acute calls on adult acute respiratory distress by 1-2 weeks during the first three waves of COVID-19. Our study demonstrates that virus surveillance in wastewater can predict the development of a pandemic and its burden on the health system, regardless of society's test capacity and possibility of tracking infected cases.

Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries

Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government's response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

Active syndromic surveillance of COVID-19 in Israel

Syndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID-19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID-19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ([Formula: see text] and [Formula: see text], respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: [Formula: see text], [Formula: see text]). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.

Investigating awareness, fear and control associated with norovirus and other pathogens and pollutants using best-worst scaling

Pollutants found in the water and air environment represent an ever-growing threat to human health. Contact with some air-, water- and foodborne pathogens (e.g. norovirus) results in gastrointestinal diseases and outbreaks. For future risk mitigation, we aimed to measure people's awareness of waterborne and foodborne norovirus relative to other environment-associated pollutants (e.g. pesticides, bioaerosols, antibiotic resistant bacteria) and well-known risks (e.g. diabetes, dementia, terrorist attack). We used an online survey, which included a best-worst scaling component to elicit personal levels of control and fear prompted by norovirus relative to 15 other risks. There was a negative correlation between levels of fear vs. control for all 16 measured risks. Perceived infection control levels were higher amongst women compared to men and correlated with age and the level of qualification in both groups. Participants who had sought advice regarding the symptoms caused by norovirus appeared to have more control over the risks. Norovirus is associated with high levels of fear, however, the levels of control over it is low compared to other foodborne illnesses, e.g. Salmonella. Addressing this deficit in the public's understanding of how to control exposure to the pathogen in an important health need.

Investigating novel approaches to tick-borne encephalitis surveillance in Sweden, 2010-2017

Tick-borne encephalitis (TBE) is a vaccine-preventable, high-priority disease in Sweden, with increasing incidence. However, surveillance is limited to case reports. We investigated relationships between reported TBE incidence and syndromic surveillance data to determine if these novel data sources could provide earlier indications of disease activity. We retrospectively compared national, weekly (2010-2017) reported TBE incidence to the percentage of TBE-related a) searches on the main Swedish healthcare information website and b) calls to its telehealth service using Spearman's ρ to determine the most strongly correlated lags. We conducted a sub-analysis (2012-2017) of TBE-related Google Trends queries and compared the number of TBE-related media stories to each novel surveillance dataset. Healthcare website searches for "tbe" and "vaccine" combined, "tbe", "tick", and "tick bite" led case data by 12, 8, 7, and 6 weeks, respectively (ρ = 0.87-0.89); telehealth calls led by 4 weeks (ρ = 0.92; all p < 0.001). Correlations and lags for Google Trends and healthcare website searches were fairly similar to each other. In comparison, correlation between the different syndromic surveillance datasets and the number of media stories was lower (ρ = 0.25-0.56). We observed volume discrepancies between TBE incidence and the novel surveillance datasets during some years, particularly for web searches. Syndromic surveillance data were strongly correlated with and preceded case data by 4-12 weeks. Syndromic data may provide advanced awareness and earlier indications of TBE activity, which can improve timing and specificity of public health communications. The use of these data as supplements to notifiable disease data for national planning and preparedness in real-time should be investigated.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Challenges in Infection Epidemiology: On the Underreporting of Norovirus Gastroenteritis Cases in Germany

It is commonly accepted that the number of officially reported incidences of norovirus (NoV) according to the German Protection against Infection Act (Infektionsschutzgesetz) does not reflect the ‘true’ incidence of NoV in Germany. This study aims to reveal the reasons for the underreporting of NoV cases by comparing secondary data. Methods: NoV incidence (cases per 100,000 reference persons) in the age group 18–65 was derived from register data of four different sources in the German public health system (2011–2015): Statutory health insurance in the federal state of Lower Saxony (AOK; in- and outpatient cases), the Research Institute of Ambulatory Health Care in Germany (ZI; outpatient cases), the German Federal Statistical Office (inpatient cases; DESTATIS), and the Robert Koch Institute (RKI SurvStat; health reporting data). Results: the incidence derived from the AOK in Lower Saxony varied between 49 and 66 NoV cases per 100,000 persons and was thus lower than at the federal level. Incidences of all inpatient and outpatient data were lower than the incidence according to the RKI in the last 2–3 years of the observation period. Conclusions: the disagreement between NoV incidences calculated from secondary inpatient and outpatient data and the respective numbers published by the RKI can be regarded as an indication that not all NoV cases were reported to public health authorities. This might be due to missed cases during the notification procedure or misclassification of gastroenteritis cases by general practitioners. Considering the limitations associated with analyzing secondary data, the appropriateness of these assumptions should be verified in future studies.

Strengthening Global Public Health Surveillance through Data and Benefit Sharing

Equitable sharing of public health surveillance data can help prevent or mitigate the effect of infectious diseases. Equitable data sharing includes working toward more equitable sharing of the public health benefits that data sharing brings and requires the engagement of those providing the data, those interpreting and using the data generated by others, those facilitating the data-sharing process, and those deriving and contributing to the benefit. An expert consultation conducted by Chatham House outlined 7 principles to encourage the process of equitable data sharing: 1) building trust; 2) articulating the value; 3) planning for data sharing; 4) achieving quality data; 5) understanding the legal context; 6) creating data-sharing agreements; and 7) monitoring and evaluation. Sharing of public health surveillance data is best done taking into account these principles, which will help to ensure data are shared optimally and ethically, while fulfilling stakeholder expectations and facilitating equitable distribution of benefits.

Temporal Relationship Between Healthcare-Associated and Nonhealthcare-Associated Norovirus Outbreaks and Google Trends Data in the United States

Healthcare-associated norovirus outbreaks increase later but have a more pronounced seasonality than nonhealthcare norovirus outbreaks. Healthcare-associated norovirus outbreaks had higher correlation with Google Trends activity than nonhealthcare outbreaks (R2=0.68 vs 0.39). Google Trends data may have the potential to supplement existing norovirus surveillance due to its real-time availability.

Infect Control Hosp Epidemiol 2018;39:355–358

Utility and potential of rapid epidemic intelligence from internet-based sources

OBJECTIVES

Rapid epidemic detection is an important objective of surveillance to enable timely intervention, but traditional validated surveillance data may not be available in the required timeframe for acute epidemic control. Increasing volumes of data on the Internet have prompted interest in methods that could use unstructured sources to enhance traditional disease surveillance and gain rapid epidemic intelligence. We aimed to summarise Internet-based methods that use freely-accessible, unstructured data for epidemic surveillance and explore their timeliness and accuracy outcomes.

METHODS

Steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist were used to guide a systematic review of research related to the use of informal or unstructured data by Internet-based intelligence methods for surveillance.

RESULTS

We identified 84 articles published between 2006-2016 relating to Internet-based public health surveillance methods. Studies used search queries, social media posts and approaches derived from existing Internet-based systems for early epidemic alerts and real-time monitoring. Most studies noted improved timeliness compared to official reporting, such as in the 2014 Ebola epidemic where epidemic alerts were generated first from ProMED-mail. Internet-based methods showed variable correlation strength with official datasets, with some methods showing reasonable accuracy.

CONCLUSION

The proliferation of publicly available information on the Internet provided a new avenue for epidemic intelligence. Methodologies have been developed to collect Internet data and some systems are already used to enhance the timeliness of traditional surveillance systems. To improve the utility of Internet-based systems, the key attributes of timeliness and data accuracy should be included in future evaluations of surveillance systems.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

Incidence of Hospital Norovirus Outbreaks and Infections Using 2 Surveillance Methods in Sweden

OBJECTIVE

To evaluate 2 different methods of surveillance and to estimate the incidence of norovirus (NoV) outbreaks in hospitals.

DESIGN

Prospective observational study.

SETTING

All 194 hospital wards in southern Sweden during 2 winter seasons (2010–2012).

METHODS

Clinical surveillance based on outbreak reports of 2 or more clinical cases, with symptom onset within 5 days, was compared with laboratory surveillance based on positive NoV results among inpatients. At least 2 NoV positive patients sampled within 5 days at a ward defined a cluster. Outbreak reports including at least 1 NoV positive case and clusters including at least 1 NoV positive patient with 5 or more days from ward admission to sampling were defined as NoV outbreaks.

RESULTS

During the study periods 135 NoV outbreaks were identified; 74 were identified by both clinical and laboratory surveillance, 18 were identified only by outbreak reports, and 43 were identified only by laboratory surveillance. The outbreak incidence was 1.0 (95% CI, 0.8–1.2) and 0.5 (95% CI, 0.3–0.6) per 1,000 admissions for the 2 different seasons, respectively. To correctly identify NoV outbreaks, the sensitivity and positive predictive value of the clinical surveillance were 68% and 88% and of the laboratory surveillance were 86% and 81%, respectively.

CONCLUSION

The addition of laboratory surveillance significantly improves outbreak surveillance and provides a more complete estimate of NoV outbreaks in hospitals. Laboratory surveillance can be recommended for evaluation of clinical surveillance.

Infect Control Hosp Epidemiol 2016;1–7