Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design

Integrated Surveillance of Disparities in Vaccination Coverage and Morbidity during the COVID-19 Pandemic: A Cohort Study in Southeast Sweden

We aimed to use the digital platform maintained by the local health service providers in Southeast Sweden for integrated monitoring of disparities in vaccination and morbidity during the COVID-19 pandemic. The monitoring was performed in the adult population of two counties (n = 657,926) between 1 February 2020 and 15 February 2022. The disparities monitored were relocated (internationally displaced), substance users, and suffering from a psychotic disorder. The outcomes monitored were COVID-19 vaccination, SARS-CoV-2 test results, and hospitalization with COVID-19. Relocated residents displayed an increased likelihood of remaining unvaccinated and a decreased likelihood of testing as well as increased risks of primary SARS-CoV-2 infection and hospitalization compared with the general population. Suffering from a major psychiatric disease was associated with an increased risk of remaining unvaccinated and an increased risk of hospitalization but a decreased risk of SARS-CoV-2 infection. From the digital monitoring, we concluded that the relocated minority received insufficient protection during the pandemic, suggesting the necessity for comprehensive promotion of overall social integration. Persons with major psychiatric diseases underused vaccination, while they benefitted from proactively provided testing, implying a need for active encouragement of vaccination. Further research is warranted on legal and ethical frameworks for digital monitoring in vaccination programs.

A Predictive Model of the Start of Annual Influenza Epidemics

Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system has time to prepare for it. This study therefore aims to develop a statistical model capable of predicting the onset of influenza epidemics in Catalonia, Spain. Influenza seasons from 2011 to 2017 were used for model training, and those from 2017 to 2018 were used for validation. Logistic regression, Support Vector Machine, and Random Forest models were used to predict the onset of the influenza epidemic. The logistic regression model was able to predict the start of influenza epidemics at least one week in advance, based on clinical diagnosis rates of various respiratory diseases and meteorological variables. This model achieved the best punctual estimates for two of three performance metrics. The most important variables in the model were the principal components of bronchiolitis rates and mean temperature. The onset of influenza epidemics can be predicted from clinical diagnosis rates of various respiratory diseases and meteorological variables. Future research should determine whether predictive models play a key role in preventing influenza.

Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020

We report on local nowcasting (short-term forecasting) of coronavirus disease (COVID-19) hospitalizations based on syndromic (symptom) data recorded in regular healthcare routines in Östergötland County (population ≈465,000), Sweden, early in the pandemic, when broad laboratory testing was unavailable. Daily nowcasts were supplied to the local healthcare management based on analyses of the time lag between telenursing calls with the chief complaints (cough by adult or fever by adult) and COVID-19 hospitalization. The complaint cough by adult showed satisfactory performance (Pearson correlation coefficient r>0.80; mean absolute percentage error <20%) in nowcasting the incidence of daily COVID-19 hospitalizations 14 days in advance until the incidence decreased to <1.5/100,000 population, whereas the corresponding performance for fever by adult was unsatisfactory. Our results support local nowcasting of hospitalizations on the basis of symptom data recorded in routine healthcare during the initial stage of a pandemic.

Human Influenza Epidemiology

Influenza virus infections are common in people of all ages. Epidemics occur in the winter months in temperate locations and at varying times of the year in subtropical and tropical locations. Most influenza virus infections cause mild and self-limiting disease, and around one-half of all infections occur with a fever. Only a small minority of infections lead to serious disease requiring hospitalization. During epidemics, the rates of influenza virus infections are typically highest in school-age children. The clinical severity of infections tends to increase at the extremes of age and with the presence of underlying medical conditions, and impact of epidemics is greatest in these groups. Vaccination is the most effective measure to prevent infections, and in recent years influenza vaccines have become the most frequently used vaccines in the world. Nonpharmaceutical public health measures can also be effective in reducing transmission, allowing suppression or mitigation of influenza epidemics and pandemics.

New Approach to the Surveillance of Pediatric Infectious Diseases From Ambulatory Pediatricians in the Digital Era

BACKGROUND

Many ambulatory networks in several countries have established syndromic surveillance systems to detect outbreaks of different illnesses. Here, we describe a new Pediatric and Ambulatory Research in Infectious diseases network that combined automated data extraction from the computers of primary care pediatricians.

METHODS

Pediatricians who used the same software, AxiSanté 5-Infansoft for electronic medical records were specially trained in infectious diseases, encouraged to comply with French treatments' recommendations, use of point-of-care tests and vaccination guidelines. Infectious disease diagnoses in children <16 years old in the records triggered automatic data extraction of complete records. A quality control process and external validation were developed.

RESULTS

From September 2017 to February 2020, 107 pediatricians enrolled 57,806 children (mean age 2.9 ± 2.6 years at diagnosis) with at least one infectious disease diagnosis among those followed by the network. Among the 118,193 diagnoses, the most frequent were acute otitis media (n = 44,924, 38.0%), tonsillopharyngitis (n = 13,334, 11.3%), gastroenteritis (n = 12,367, 10.5%), influenza (n = 11,062, 9.4%), bronchiolitis (n = 10,531, 8.9%), enteroviral infections (n = 8474, 7.2%) and chickenpox (n = 6857, 5.8%). A rapid diagnostic test was performed in 84.7% of cases of tonsillopharyngitis and was positive in 44%. The antibiotic recommendations from French guidelines were strictly followed: amoxicillin was the most prescribed antibiotic and less than 10% of presumed viral infections were treated.

CONCLUSIONS

This "tailor-made" network set up with quality controls and external validation represents a new approach to the surveillance of pediatric infectious diseases in the digital era and could highly optimize pediatric practices.

Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008-2019

The timing of influenza case incidence during epidemics can differ between regions within nations and states. We conducted a prospective 10-year evaluation (January 2008–February 2019) of a local influenza nowcasting (short-term forecasting) method in 3 urban counties in Sweden with independent public health administrations by using routine health information system data. Detection-of-epidemic-start (detection), peak timing, and peak intensity were nowcasted. Detection displayed satisfactory performance in 2 of the 3 counties for all nonpandemic influenza seasons and in 6 of 9 seasons for the third county. Peak-timing prediction showed satisfactory performance from the influenza season 2011–12 onward. Peak-intensity prediction also was satisfactory for influenza seasons in 2 of the counties but poor in 1 county. Local influenza nowcasting was satisfactory for seasonal influenza in 2 of 3 counties. The less satisfactory performance in 1 of the study counties might be attributable to population mixing with a neighboring metropolitan area.

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review

Background

Mobile health (mHealth) app use is a major concern because of the possible dissemination of misinformation that could harm the users. Particularly, it can be difficult for health care professionals to recommend a suitable app for coronavirus disease (COVID-19) education and self-monitoring purposes.

Objective

This study aims to analyze and evaluate the contents as well as features of COVID-19 mobile apps. The findings are instrumental in helping health care professionals to identify suitable mobile apps for COVID-19 self-monitoring and education. The results of the mobile apps’ assessment could potentially help mobile app developers improve or modify their existing mobile app designs to achieve optimal outcomes.

Methods

The search for the mHealth apps available in the android-based Play Store and the iOS-based App Store was conducted between April 18 and May 5, 2020. The region of the App Store where we performed the search was the United States, and a virtual private network app was used to locate and access COVID-19 mobile apps from all countries on the Google Play Store. The inclusion criteria were apps that are related to COVID-19 with no restriction in language type. The basic features assessment criteria used for comparison were the requirement for free subscription, internet connection, education or advisory content, size of the app, ability to export data, and automated data entry. The functionality of the apps was assessed according to knowledge (information on COVID-19), tracing or mapping of COVID-19 cases, home monitoring surveillance, online consultation with a health authority, and official apps run by health authorities.

Results

Of the 223 COVID-19–related mobile apps, only 30 (19.9%) found in the App Store and 28 (44.4%) in the Play Store matched the inclusion criteria. In the basic features assessment, most App Store (10/30, 33.3%) and Play Store (10/28, 35.7%) apps scored 4 out of 7 points. Meanwhile, the outcome of the functionality assessment for most App Store apps (13/30, 43.3%) was a score of 3 compared to android-based apps (10/28, 35.7%), which scored 2 (out of the maximum 5 points). Evaluation of the basic functions showed that 75.0% (n=36) of the 48 included mobile apps do not require a subscription, 56.3% (n=27) provide symptom advice, and 41.7% (n=20) have educational content. In terms of the specific functions, more than half of the included mobile apps are official mobile apps maintained by a health authority for COVID-19 information provision. Around 37.5% (n=18) and 31.3% (n=15) of the mobile apps have tracing or mapping and home monitoring surveillance functions, respectively, with only 17% (n=8) of the mobile apps equipped with an online consultation function.

Conclusions

Most iOS-based apps incorporate infographic mapping of COVID-19 cases, while most android-based apps incorporate home monitoring surveillance features instead of providing focused educational content on COVID-19. It is important to evaluate the contents and features of COVID-19 mobile apps to guide users in choosing a suitable mobile app based on their requirements.

Syndromic surveillance: two decades experience of sustainable systems - its people not just data!

Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of ‘big data’, but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

The e-Psychonauts' 'Spiced' World; Assessment of the Synthetic Cannabinoids' Information Available Online

BACKGROUND

A wide range of novel psychoactive substances (NPS) is regularly searched and discussed online by web-based drug enthusiasts (i.e. the e-psychonauts). Among NPS, the range of synthetic cannabinoids (SC; 'Spice') currently represents a challenge for governments and clinicians.

METHODS

Using a web crawler (i.e. the NPS.Finder®), the present study aimed at assessing psychonauts' fora/platforms to better understand the online mentions of SC.

RESULTS

The open-web crawling/navigating software identified here some 1,103 synthetic cannabinoids. Of these, 863 molecules were not listed in either the international or the European NPS databases.

CONCLUSION

A web crawling approach helped here in identifying a large range of unknown SC likely to possess a misuse potential. Most of these novel/emerging molecules are still relatively unknown. This is a reason for concern; each of these analogues potentially presents different toxicodynamic profiles and there is a lack of docking, preclinical, and clinical observations. Strengthening multidisciplinary collaboration between clinicians and bioinformatics may prove useful in better assessing SC-associated public health risks.

A systematic review on integration mechanisms in human and animal health surveillance systems with a view to addressing global health security threats

BACKGROUND

Health surveillance is an important element of disease prevention, control, and management. During the past two decades, there have been several initiatives to integrate health surveillance systems using various mechanisms ranging from the integration of data sources to changing organizational structures and responses. The need for integration is caused by an increasing demand for joint data collection, use and preparedness for emerging infectious diseases.

OBJECTIVE

To review the integration mechanisms in human and animal health surveillance systems and identify their contributions in strengthening surveillance systems attributes.

METHOD

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Peer-reviewed articles were searched from PubMed, HINARI, Web of Science, Science Direct and advanced Google search engines. The review included articles published in English from 1900 to 2018. The study selection considered all articles that used quantitative, qualitative or mixed research methods. Eligible articles were assessed independently for quality by two authors using the QualSyst Tool and relevant information including year of publication, field, continent, addressed attributes and integration mechanism were extracted.

RESULTS

A total of 102 publications were identified and categorized into four pre-set integration mechanisms: interoperability (35), convergent integration (27), semantic consistency (21) and interconnectivity (19). Most integration mechanisms focused on sensitivity (44.1%), timeliness (41.2%), data quality (23.5%) and acceptability (17.6%) of the surveillance systems. Generally, the majority of the surveillance system integrations were centered on addressing infectious diseases and all hazards. The sensitivity of the integrated systems reported in these studies ranged from 63.9 to 100% (median = 79.6%, n = 16) and the rate of data quality improvement ranged from 73 to 95.4% (median = 87%, n = 4). The integrated systems were also shown improve timeliness where the recorded changes were reported to be ranging from 10 to 91% (median = 67.3%, n = 8).

CONCLUSION

Interoperability and semantic consistency are the common integration mechanisms in human and animal health surveillance systems. Surveillance system integration is a relatively new concept but has already been shown to enhance surveillance performance. More studies are needed to gain information on further surveillance attributes.

Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA

Influenza is one of the main causes of death, not only in the USA but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for forecasting of any upcoming influenza outbreaks. Most currently available methods for influenza prediction are based on parametric time series and regression models that impose restrictive and often unverifiable assumptions on the data. In turn, more flexible machine learning models and, particularly, deep learning tools whose utility is proven in a wide range of disciplines, remain largely under-explored in epidemiological forecasting. We study the seasonal influenza in Dallas County by evaluating the forecasting ability of deep learning with feedforward neural networks as well as performance of more conventional statistical models, such as beta regression, autoregressive integrated moving average (ARIMA), least absolute shrinkage and selection operators (LASSO), and non-parametric multivariate adaptive regression splines (MARS) models for one week and two weeks ahead forecasting. Furthermore, we assess forecasting utility of Google search queries and meteorological data as exogenous predictors of influenza activity. Finally, we develop a probabilistic forecasting of influenza in Dallas County by fusing all the considered models using Bayesian model averaging.

Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009-2014

The growing availability of big data in healthcare and public health opens possibilities for infectious disease control in local settings. We prospectively evaluated a method for integrated local detection and prediction (nowcasting) of influenza epidemics over 5 years, using the total population in Östergötland County, Sweden. We used routine health information system data on influenza-diagnosis cases and syndromic telenursing data for July 2009–June 2014 to evaluate epidemic detection, peak-timing prediction, and peak-intensity prediction. Detection performance was satisfactory throughout the period, except for the 2011–12 influenza A(H3N2) season, which followed a season with influenza B and pandemic influenza A(H1N1)pdm09 virus activity. Peak-timing prediction performance was satisfactory for the 4 influenza seasons but not the pandemic. Peak-intensity levels were correctly categorized for the pandemic and 2 of 4 influenza seasons. We recommend using versions of this method modified with regard to local use context for further evaluations using standard methods.

A systematic review of aberration detection algorithms used in public health surveillance

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.