Big Data and the Global Public Health Intelligence Network (GPHIN)

Unveiling the Threat of Disease X: Preparing for the Next Global Pandemic

The term "Disease X", first introduced by the World Health Organization (WHO) in 2018, symbolizes the threat of an unknown pathogen capable of causing a global pandemic. Classified as a "priority pathogens," Disease X stands alongside well-known threats like SARS, Ebola, and ZIKV due to its potential for widespread outbreaks. SARS-CoV-2 is considered the first "Disease X" to fulfill this prediction, demonstrating the devastating impact such pathogens can have. A future pathogen X could pose an even greater threat, with catastrophic consequences. This paper examines the potential origins of such pathogens, drawing lessons from outbreaks like SARS, MERS, and SARS-CoV-2. It also highlights strategic approaches to detect, prevent, and respond effectively to mitigate the risk of future pandemics.

Drawing on the Development Experiences of Infectious Disease Surveillance Systems Around the World

High-quality infectious disease surveillance systems are foundational to infectious disease prevention and control. Current major infectious disease surveillance systems globally can be categorized as either indicator-based, which are more specific, or event-based, which are more timely. Modern surveillance systems commonly utilize multi-source data, strengthened information sharing, advanced technology, and improved early warning accuracy and sensitivity. International experience may provide valuable insights for China. China's existing infectious disease surveillance systems require urgent enhancements to monitor emerging infectious diseases and improve the integration and learning capabilities of early warning models. Methods such as establishing multi-stage surveillance systems, promoting cross-sectoral and cross-provincial data sharing, applying advanced technologies like artificial intelligence, and cultivating professional talent should be adopted to enhance the development of intelligent and multipoint-triggered infectious disease surveillance systems in China.

Greater agreement is required to harness the potential of health intelligence: a critical interpretive synthesis

OBJECTIVES

To synthesize existing knowledge on the features of, and approaches to, health intelligence, including definitions, key concepts, frameworks, methods and tools, types of evidence used, and research gaps.

STUDY DESIGN AND SETTING

We applied a critical interpretive synthesis methodology, combining systematic searching, purposive sampling, and inductive analysis to explore the topic. We conducted electronic and supplementary searches to identify records (papers, books, websites) based on their potential relevance to health intelligence. The key themes identified in the literature were combined under each of the compass subquestions and circulated among the research team for discussion and interpretation.

RESULTS

Of the 290 records screened, 40 were included in the synthesis. There is no clear definition of health intelligence in the literature. Some records describe it in similar terms as public health surveillance. Some focus on the use of artificial intelligence, while others refer to health intelligence in a military or security sense. And some authors have suggested a broader definition of health intelligence that explicitly includes the concepts of synthesis of research evidence for informed decision making.

CONCLUSION

Rather than developing a new or all-encompassing definition, we suggest incorporating the concept and scope of health intelligence within the evidence ecosystem.

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends

Introduction

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system.

Methods

The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence.

Results

Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche.

Conclusion

Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Animal disease surveillance: How to represent textual data for classifying epidemiological information

The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the early warning and preparedness context is recognized. This study evaluates machine learning methods for classifying information from animal disease-related news at a fine-grained level (i.e., epidemiological topic). We compare two textual representations, the bag-of-words method and a distributional approach, i.e., word embeddings. Both representations performed well for binary relevance classification (F-measure of 0.839 and 0.871, respectively). Bag-of-words representation was outperformed by word embedding representation for classifying sentences into fine-grained epidemiological topics (F-measure of 0.745). Our results suggest that the word embedding approach is of interest in the context of low-frequency classes in a specialized domain. However, this representation did not bring significant performance improvements for binary relevance classification, indicating that the textual representation should be adapted to each classification task.

Emerging data inputs for infectious diseases surveillance and decision making

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

Constructing big data prevention and control model for public health emergencies in China: A grounded theory study

Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the “DSA” model. (2) The “DSA” model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by “information island”. (3) The “DSA” model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The “DSA” model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.

Governing digital health for infectious disease outbreaks

ABSTRACTHow can governing digital health for infectious disease outbreaks be enhanced? In many ways, the COVID-19 pandemic has simultaneously represented both the potential and marked limitations of digital health practices for infectious disease outbreaks. During the pandemic's initial stages, states along with Big Data and Big Tech actors unleashed a scope of both established and experimental digital technologies for tracking infections, hospitalisations, and deaths from COVID-19 - and sometimes exposure to the virus SARS-CoV-2. Despite the proliferation of these technologies at the global level, transnational and cross-border integration, and cooperation within digital health responses to COVID-19 often faltered, while digital health regulations were fragmented, contested, and uncoordinated. This article presents a critiquing reflection of approaches to conceptualising, understanding, and implementing digital health for infectious disease outbreaks, observed from COVID-19 and previous examples. In assessing the strengths and limitations of existing practices of governing digital health for infectious disease outbreaks, this article particularly examines 'informal' digital health to build upon and consider how digitised responses to addressing and governing infectious disease outbreaks may be reconceptualised, revisited, or revised.

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review

Background

Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.

Methods

We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.

Results

Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.

Conclusions

Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

A survey of data element perspective: Application of artificial intelligence in health big data

Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples’ daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.

Improving Pandemic Response With Military Tools: Using Enhanced Intelligence, Surveillance, and Reconnaissance

The coronavirus disease 2019 (COVID-19) pandemic rocked the world, spurring the collapse of national commerce, international trade, education, air travel, and tourism. The global economy has been brought to its knees by the rapid spread of infection, resulting in widespread illness and many deaths. The rise in nationalism and isolationism, ethnic strife, disingenuous governmental reporting, lockdowns, travel restrictions, and vaccination misinformation have caused further problems. This has brought into stark relief the need for improved disease surveillance and health protection measures. National and international agencies that should have provided earlier warning in fact failed to do so. A robust global health network that includes enhanced cooperation with Military Intelligence, Surveillance, and Reconnaissance (ISR) assets in conjunction with the existing international, governmental, and nongovernment medical intelligence networks and allies and partners would provide exceptional forward-looking and early-warning and is a proactive step toward making our future safe. This will be achieved both by surveilling populations for new biothreats, fusing and disseminating data, and then reaching out to target assistance to reduce disease spread in unprotected populations.

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries.

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Automated prediction of public health intervention from COVID-19 news reports

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Inferred 42 country-specific temporal topic trends to monitor interventions

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Learned interpretable topics that predict interventions from news reports

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Transfer learning to predict interventions for each country on weekly basis

Accurate, scalable detection of the timing of changes to public health interventions for COVID-19 is an important step toward automating evaluation of the effectiveness of interventions. We show that it is possible to train an interpretable deep-learning model called EpiTopics on media news data to predict (1) the interventions mentioned in individual news articles and (2) the temporal change of intervention status at the country level. We addressed a main challenge of label scarcity among the news reports. Using EpiTopics, we modeled the latent semantics from 1.2 million unlabeled news reports on COVID-19 over 42 countries recorded from November 1, 2019 to July 31, 2020, identifying 25 interpretable topics under 4 COVID-related themes. Using the learned topic model, we inferred topic mixture membership for each labeled article, which allowed us to learn an accurate connection between the topics and the public health interventions at both the document level and country level.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

"Digital public health" has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O'Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to "digital" and "public health." We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

The role of AI technology in prediction, diagnosis and treatment of colorectal cancer

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

Evolutionary clustering and community detection algorithms for social media health surveillance

The prominent rise of social networks within the past decade have become a gold mine for data mining operations seeking to model the real world through these virtual worlds. One of the most important applications that has been proposed is utilizing information generated from social networks as a supplemental health surveillance system to monitor disease epidemics. At the time this research was conducted in 2020, the COVID-19 virus had evolved into a global pandemic, forcing many countries to implement preventative measures to halt its expanse. Health surveillance has been a powerful tool in placing further preventative measures, however it is not a perfect system, and slowly collected, misidentified information can prove detrimental to these efforts. This research proposes a new potential surveillance avenue through unsupervised machine learning using dynamic, evolutionary variants of clustering algorithms DBSCAN and the Louvain method to allow for community detection in temporal networks. This technique is paired with geographical data collected directly from the social media Twitter, to create an effective and accurate health surveillance system that grows as time passes. The experimental results show that the proposed system is promising and has the potential to be an advancement on current machine learning health surveillance techniques.

Digital and technological innovation in vector-borne disease surveillance to predict, detect, and control climate-driven outbreaks

Vector-borne diseases are particularly sensitive to changes in weather and climate. Timely warnings from surveillance systems can help to detect and control outbreaks of infectious disease, facilitate effective management of finite resources, and contribute to knowledge generation, response planning, and resource prioritisation in the long term, which can mitigate future outbreaks. Technological and digital innovations have enabled the incorporation of climatic data into surveillance systems, enhancing their capacity to predict trends in outbreak prevalence and location. Advance notice of the risk of an outbreak empowers decision makers and communities to scale up prevention and preparedness interventions and redirect resources for outbreak responses. In this Viewpoint, we outline important considerations in the advent of new technologies in disease surveillance, including the sustainability of innovation in the long term and the fundamental obligation to ensure that the communities that are affected by the disease are involved in the design of the technology and directly benefit from its application.

Defining the Scope of Digital Public Health and Its Implications for Policy, Practice, and Research: Protocol for a Scoping Review

BACKGROUND

There has been rapid development and application of digital technologies in public health domains, which are considered to have the potential to transform public health. However, this growing interest in digital technologies in public health has not been accompanied by a clarity of scope to guide policy, practice, and research in this rapidly emergent field.

OBJECTIVE

This scoping review seeks to determine the scope of digital health as described by public health researchers and practitioners and to consolidate a conceptual framework of digital public health.

METHODS

The review follows Arksey and O'Malley's framework for conducting scoping reviews with improvements as suggested by Levac et al. The search strategy will be applied to Embase, Medline, and Google Scholar. A grey literature search will be conducted on intergovernmental agency websites and country-specific websites. Titles and abstracts will be reviewed by independent reviewers, while full-text reviews will be conducted by 2 reviewers to determine eligibility based on prespecified inclusion and exclusion criteria. The data will be coded in an iterative approach using the best-fit framework analysis methodology.

RESULTS

This research project received funding from the British Columbia Centre for Disease Control Foundation for Population and Public Health on January 1, 2020. The initial search was conducted on June 1, 2020 and returned 6953 articles in total. After deduplication, 4523 abstracts were reviewed, and 227 articles have been included in the review. Ethical approval is not required for this review as it uses publicly available data.

CONCLUSIONS

We anticipate that the findings of the scoping review will contribute relevant evidence to health policy makers and public health practitioners involved in planning, funding, and delivering health services that leverage digital technologies. Results of the review will be strategically disseminated through publications in scientific journals, conferences, and engagement with relevant stakeholders.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/27686.

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01-4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00-0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology

Background

Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform.

Objective

This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities.

Methods

We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks.

Results

Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225).

Conclusions

The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study

BACKGROUND

A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future.

OBJECTIVE

This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media.

METHODS

WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020.

RESULTS

WeChat Index hits for "Feidian" (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority's outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords "SARS," "coronavirus," "novel coronavirus," "shortness of breath," "dyspnea," and "diarrhea," but these terms were not as meaningful for the early detection of the outbreak as the term "Feidian".

CONCLUSIONS

By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

Application of natural language processing algorithms for extracting information from news articles in event-based surveillance

The focus of this article is the application of natural language processing (NLP) for information extraction in event-based surveillance (EBS) systems. We describe common information extraction applications from open-source news articles and media sources in EBS systems, methods, value in public health, challenges and emerging developments.

Challenges and opportunities for public health made possible by advances in natural language processing

Natural language processing (NLP) is a subfield of artificial intelligence devoted to understanding and generation of language. The recent advances in NLP technologies are enabling rapid analysis of vast amounts of text, thereby creating opportunities for health research and evidence-informed decision making. The analysis and data extraction from scientific literature, technical reports, health records, social media, surveys, registries and other documents can support core public health functions including the enhancement of existing surveillance systems (e.g. through faster identification of diseases and risk factors/at-risk populations), disease prevention strategies (e.g. through more efficient evaluation of the safety and effectiveness of interventions) and health promotion efforts (e.g. by providing the ability to obtain expert-level answers to any health related question). NLP is emerging as an important tool that can assist public health authorities in decreasing the burden of health inequality/inequity in the population. The purpose of this paper is to provide some notable examples of both the potential applications and challenges of NLP use in public health.

Better preventing and mitigating the effects of Covid-19

Currently, there are no proven medical treatments against SARS-CoV-2, the virus responsible for Covid-19. In addition to the all important public health measures needed to prevent the spread of this disease, a number of strategies related to our exposome are recommended herein, to better prevent and mitigate the effects of a SARS-CoV-2 infection through enhancement of our immune system and reduction of inflammation.

Foodborne viral outbreaks associated with frozen produce

Over the past decade, frozen fruits have been a major vehicle of foodborne illnesses mainly attributed to norovirus (NoV) and hepatitis A virus (HAV) infections. Fresh produce may acquire viral contamination by direct contact with contaminated surface, water or hands, and is then frozen without undergoing proper decontamination. Due to their structural integrity, foodborne viruses are able to withstand hostile conditions such as desiccation and freezing, and endure for a long period of time without losing their infectivity. Additionally, these foods are often consumed raw or undercooked, which increases the risk of infection. Herein, we searched published literature and databases of reported outbreaks as well as the databases of news articles for the viral outbreaks associated with the consumption of frozen produce between January 2008 and December 2018; recorded the worldwide distribution of these outbreaks; and analysed the implication of consumption of different types of contaminated frozen food. In addition, we have briefly discussed the factors that contribute to an increased risk of foodborne viral infection following the consumption of frozen produce. Our results revealed that frozen fruits, especially berries and pomegranate arils, contributed to the majority of the outbreaks, and that most outbreaks were reported in industrialised countries.

PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System

A map-based infectious disease outbreak information system, called PEACOCK, that provides three types of necessary infectious disease outbreak information is presented. The system first collects the infectious disease outbreak statistics from the government agencies and displays the number of infected people and infection indices on the map. Then, it crawls online news articles for each infectious disease and displays the number of mentions of each disease on the map. Users can also search for news articles regarding the disease. Finally, it retrieves the portal search query data and plots the graphs of the trends. It divides the risk into three levels (i.e., normal, caution, and danger) and visualizes them using different colors on the map. Users can access infectious disease outbreak information accurately and quickly using the system. As the system visualizes the information using both a map and various types of graphs, users can check the information at a glance. This system is in live at http://www.epidemic.co.kr/map.

Brexit and health security: why we need to protect our global networks

As the United Kingdom (UK) negotiates its separation from the European Union (EU), it is important to remember the public health mechanisms that are directly facilitated via our relationship with the EU. One such mechanism is the UK’s role within the European Centre for Disease Prevention and Control (ECDC). Global health protection is an area that is currently experiencing an unprecedented wave of innovation, both technologically and ideologically, and we must therefore ensure that our future relationship with ECDC is one that facilitates full involvement with the global health security systems of the future.

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases.

Policy and Science for Global Health Security: Shaping the Course of International Health

The global burden of infectious diseases and the increased attention to natural, accidental, and deliberate biological threats has resulted in significant investment in infectious disease research. Translating the results of these studies to inform prevention, detection, and response efforts often can be challenging, especially if prior relationships and communications have not been established with decision-makers. Whatever scientific information is shared with decision-makers before, during, and after public health emergencies is highly dependent on the individuals or organizations who are communicating with policy-makers. This article briefly describes the landscape of stakeholders involved in information-sharing before and during emergencies. We identify critical gaps in translation of scientific expertise and results, and biosafety and biosecurity measures to public health policy and practice with a focus on One Health and zoonotic diseases. Finally, we conclude by exploring ways of improving communication and funding, both of which help to address the identified gaps. By leveraging existing scientific information (from both the natural and social sciences) in the public health decision-making process, large-scale outbreaks may be averted even in low-income countries.

Emerging infectious diseases and biological invasions: a call for a One Health collaboration in science and management

The study and management of emerging infectious diseases (EIDs) and of biological invasions both address the ecology of human-associated biological phenomena in a rapidly changing world. However, the two fields work mostly in parallel rather than in concert. This review explores how the general phenomenon of an organism rapidly increasing in range or abundance is caused, highlights the similarities and differences between research on EIDs and invasions, and discusses shared management insights and approaches. EIDs can arise by: (i) crossing geographical barriers due to human-mediated dispersal, (ii) crossing compatibility barriers due to evolution, and (iii) lifting of environmental barriers due to environmental change. All these processes can be implicated in biological invasions, but only the first defines them. Research on EIDs is embedded within the One Health concept-the notion that human, animal and ecosystem health are interrelated and that holistic approaches encompassing all three components are needed to respond to threats to human well-being. We argue that for sustainable development, biological invasions should be explicitly considered within One Health. Management goals for the fields are the same, and direct collaborations between invasion scientists, disease ecologists and epidemiologists on modelling, risk assessment, monitoring and management would be mutually beneficial.

User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection

Objectives

To review user signal rating activity within the Canadian Network for Public Health Intelligence's (CNPHI's) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating?

Methods

KIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff's alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating.

Results

The 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal's 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once.

Discussion

A diverse community of raters participated in rating the signals. Krippendorff's Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals.

Conclusions

Diversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal's 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.

Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel

Greater human mobility, largely driven by air travel, is leading to an increase in the frequency and reach of infectious disease epidemics. Air travel can rapidly connect any two points on the planet, and this has the potential to cause swift and broad dissemination of emerging and re-emerging infectious diseases that may pose a threat to global health security. Investments to strengthen surveillance, build robust early-warning systems, improve predictive models, and coordinate public health responses may help to prevent, detect, and respond to new infectious disease epidemics.

DOVE: An Infectious Disease Outbreak Statistics Visualization System

Humans are susceptible to various infectious diseases. However, humanity still has limited responses to emergent and recurrent infectious diseases. Recent developments in medical technology have led to various vaccines being developed, but these vaccines typically require a considerable amount of time to counter infectious diseases. Therefore, one of the best methods to prevent infectious diseases is to continuously update our knowledge with useful information from infectious disease information systems and taking active steps to safeguard ourselves against infectious diseases. Some existing infectious disease information systems simply present infectious disease information in the form of text or transmit it via e-mail. Other systems provide data in the form of files or maps. Most existing systems display text-centric information regarding infectious disease outbreaks. Therefore, understanding infectious disease outbreak information at a glance is difficult for users. In this paper, we propose the infectious disease outbreak statistics visualization system, called to DOVE, which collects infectious disease outbreak statistics from the Korea Centers for Disease Control & Prevention and provides statistical charts with district, time, infectious disease, gender, and age data. Users can easily identify infectious disease outbreak statistics at a glance by simply entering the district, time, and name of an infectious disease into our system. Additionally, each statistical chart allows users to recognize the characteristics of an infectious disease and predict outbreaks by investigating the outbreak trends of that disease. We believe that our system provides effective information to help prevent infectious disease outbreaks. Our system is currently available on the web at http://www.epidemic.co.kr/statistics.

Responding to New Psychoactive Substances in the European Union: Early Warning, Risk Assessment, and Control Measures

New psychoactive substances (NPS) are drugs that are not controlled by the United Nations international drug control conventions of 1961 and 1971 but that may pose similar threats to public health. Many of them are traded as "legal" replacements to controlled drugs such as cannabis, heroin, benzodiazepines, cocaine, amphetamines, and 3,4-methylenedioxymethamphetamine (MDMA). Driven by globalization, there has been a large increase in the availability and, subsequently, harms caused by these substances over the last decade in Europe. The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) is monitoring more than 670 NPS that have appeared on Europe's drug market in the last 20 years, of which almost 90% have appeared in the last decade. While some recent policy responses have been successful in reducing availability and sales of these substances in some settings - such as "legal highs" and "research chemicals" sold openly in the high street and online - and there are signs that growth in the market is slowing, new challenges have emerged. This includes monitoring a growing number of highly potent substances - including 179 synthetic cannabinoid receptor agonists and 28 fentanils - that can pose a high risk of life-threatening poisoning to users and can cause explosive outbreaks. This chapter briefly traces the origins of NPS, provides an overview of the situation in Europe, and discusses the work of the EMCDDA as part of a legal framework of early warning, risk assessment, and control measures that allows the European Union to rapidly detect, assess, and respond to public health and social threats caused by these substances.

Emerging infectious diseases: prediction and detection

Emerging infectious diseases (EIDs), including West Nile virus, severe acute respiratory syndrome (SARS) and Lyme disease, have had a direct effect within Canada, while many more EIDs such as Zika, chikungunya and Ebola are a threat to Canadians while travelling. Over 75% of EIDs affecting humans are, or were originally, zoonoses (infectious diseases transmitted from animals to humans). There are two main ways by which infectious diseases can emerge: by changes in their geographical ranges and by adaptive emergence, a genetic change in a microorganism that results in it becoming capable of invading a new niche, often by jumping to a new host species such as humans. Diseases can appear to emerge simply because we become capable of detecting and diagnosing them. Management of EID events is a key role of public health globally and a considerable challenge for clinical care. Increasingly, emphasis is being placed on predicting EID occurrence to "get ahead of the curve" - that is, allowing health systems to be poised to respond to them, and public health to be ready to prevent them. Predictive models estimate where and when EIDs may occur and the levels of risk they pose. Evaluation of the internal and external drivers that trigger emergence events is increasingly considered in predicting EID events. Currently, global changes are driving increasing occurrence of EIDs, but our capacity to prevent and deal with them is also increasing. Web-based scanning and analysis methods are increasingly allowing us to detect EID outbreaks, modern genomics and bioinformatics are increasing our ability to identify their genetic and geographical origins, while developments in geomatics and earth observation will enable more real-time tracking of outbreaks. EIDs will, however, remain a key, global public health challenge in a globalized world where demographic, climatic, and other environmental changes are altering the interactions between hosts and pathogen in ways that increase spillover from animals to humans and global spread.

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.

Public health surveillance for the Toronto 2015 Pan/Parapan American Games

Background

Public health surveillance for previous Olympic and Paralympic Games have been described in the literature, but surveillance for regional, multisport events on a smaller scale have rarely been explored.

Objective

To describe the public health surveillance planning, implementation, results, and lessons learned from the 2015 Pan/Parapan American Games in Toronto, Ontario, Canada.

Intervention

Public health surveillance planning for the Games began two years in advance and involved local, provincial and federal partners, primarily focusing on infectious disease. From June to August, 2015, enhanced public health surveillance was conducted to support situational awareness and to facilitate the detection of infectious diseases and outbreaks, environmental health hazards and impacts and other major health events.

Outcomes

No major public health incidents occurred that were associated with or a result of hosting the Games. There were two cases of reportable infectious diseases associated with the Games, and 18 public health investigations involving Games-accredited individuals (six related to vaccine-preventable diseases and 12 related to gastrointestinal illnesses or food/water safety violations). Enhanced communication mechanisms, rather than routine and syndromic surveillance systems, were the primary sources of initial notification to surveillance partners on investigations.

Conclusion

Working with its partners, Ontario created a robust public health surveillance system for the 2015 Pan/Parapan American Games. Lessons learned, as well as the relationships and capacity developed through this experience, will be applied towards public health surveillance planning for future events.

KIWI: A technology for public health event monitoring and early warning signal detection

OBJECTIVES

To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.

METHODS

A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.

RESULTS

KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).

DISCUSSION

Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.

CONCLUSIONS

KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

Web-based infectious disease surveillance systems and public health perspectives: a systematic review

BACKGROUND

Emerging and re-emerging infectious diseases are a significant public health concern, and early detection and immediate response is crucial for disease control. These challenges have led to the need for new approaches and technologies to reinforce the capacity of traditional surveillance systems for detecting emerging infectious diseases. In the last few years, the availability of novel web-based data sources has contributed substantially to infectious disease surveillance. This study explores the burgeoning field of web-based infectious disease surveillance systems by examining their current status, importance, and potential challenges.

METHODS

A systematic review framework was applied to the search, screening, and analysis of web-based infectious disease surveillance systems. We searched PubMed, Web of Science, and Embase databases to extensively review the English literature published between 2000 and 2015. Eleven surveillance systems were chosen for evaluation according to their high frequency of application. Relevant terms, including newly coined terms, development and classification of the surveillance systems, and various characteristics associated with the systems were studied.

RESULTS

Based on a detailed and informative review of the 11 web-based infectious disease surveillance systems, it was evident that these systems exhibited clear strengths, as compared to traditional surveillance systems, but with some limitations yet to be overcome. The major strengths of the newly emerging surveillance systems are that they are intuitive, adaptable, low-cost, and operated in real-time, all of which are necessary features of an effective public health tool. The most apparent potential challenges of the web-based systems are those of inaccurate interpretation and prediction of health status, and privacy issues, based on an individual's internet activity.

CONCLUSION

Despite being in a nascent stage with further modification needed, web-based surveillance systems have evolved to complement traditional national surveillance systems. This review highlights ways in which the strengths of existing systems can be maintained and weaknesses alleviated to implement optimal web surveillance systems.

Media content about vaccines in the United States and Canada, 2012-2014: An analysis using data from the Vaccine Sentimeter

BACKGROUND

A system for monitoring vaccine-related media content was previously developed and studied from an international perspective. This monitoring approach could also have value at a regional level, but it has yet to be evaluated at this scale. We examined regional patterns of vaccine-related media topics and sentiment in the US and Canada.

METHODS

We extracted vaccine-relevant US and Canadian online media reports between June 2012 and October 2014 from the Vaccine Sentimeter, a HealthMap-based automated media monitoring system for news aggregators and blogs. We analyzed regional distributions of reports about vaccines, categories (i.e., topics), sentiment, and measles outbreaks.

FINDINGS

The Vaccine Sentimeter captured 10,715 reports during the study period. Negative sentiment was highest in reports about vaccine safety (47%), Hepatitis B (19%), and Vermont (18%). Analyses of measles outbreaks revealed geographical variation in media content. For example, religious beliefs were mentioned in 27% of measles reports in Texas and 22% of British Columbia reports, but there were no references to religion in media on measles from California.

INTERPRETATIONS

A regional analysis of online sentiment towards vaccine can provide insights that may give US and Canadian public health practitioners a deeper understanding of media influences on vaccine choices in their regions and consequently lead to more effective public health action.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

Big Data is changing the battle against infectious diseases

Big Data has traditionally been associated with computer geeks and commercial enterprises, but it has become entrenched in many scientific disciplines including the prevention and control of infectious diseases. The use of Big Data has allowed disease trends to be identified and outbreak origins to be tracked and even predicted. Big Data is not getting smaller. The challenges we face are to hone our analytical capacity to address the huge "signal-to-noise" ratio with adequate computing power and multidisciplinary teams that can handle ever-increasing amounts of data. Big Data will also create the opportunity for future applications of bespoke (or personalized) treatment.

Pandemic influenza (H1N1): our Canadian response

The emergence of pandemic influenza (H1N1) 2009 in spring 2009 has provided a real test to the pandemic preparations that Canada, other countries and the World Health Organization have undertaken. Although formidable challenges remain, Canada is as well prepared as any country to address the second wave of the pandemic expected in the fall.