Göran Kirchner

Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review

Context: The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century. Methods: This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance. Findings: Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events. Conclusions: The acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.

Breaking the Waves: Modelling the Potential Impact of Public Health Measures to Defer the Epidemic Peak of Novel Influenza A/H1N1

Background On June 11, 2009, the World Health Organization declared phase 6 of the novel influenza A/H1N1 pandemic. Although by the end of September 2009, the novel virus had been reported from all continents, the impact in most countries of the northern hemisphere has been limited. The return of the virus in a second wave would encounter populations that are still nonimmune and not vaccinated yet. We modelled the effect of control strategies to reduce the spread with the goal to defer the epidemic wave in a country where it is detected in a very early stage. Methodology/Principal Findings We constructed a deterministic SEIR model using the age distribution and size of the population of Germany based on the observed number of imported cases and the early findings for the epidemiologic characteristics described by Fraser (Science, 2009). We propose a two-step control strategy with an initial effort to trace, quarantine, and selectively give prophylactic treatment to contacts of the first 100 to 500 cases. In the second step, the same measures are focused on the households of the next 5,000 to 10,000 cases. As a result, the peak of the epidemic could be delayed up to 7.6 weeks if up to 30% of cases are detected. However, the cumulative attack rates would not change. Necessary doses of antivirals would be less than the number of treatment courses for 0.1% of the population. In a sensitivity analysis, both case detection rate and the variation of R0 have major effects on the resulting delay. Conclusions/Significance Control strategies that reduce the spread of the disease during the early phase of a pandemic wave may lead to a substantial delay of the epidemic. Since prophylactic treatment is only offered to the contacts of the first 10,000 cases, the amount of antivirals needed is still very limited.

Event-driven architecture for health event detection from multiple sources.

Early detection of potential health threats is crucial for taking actions in time. It is unclear in which information source an event is reported first and, information from various sources can be complementing. Thus, it is important to search for information in a very broad range of sources. Furthermore, real-time processing is necessary to deal with the huge amounts of incoming data in time. Event-driven architectures are designed to address such challenges. This will be shown in this paper by presenting the architecture of a public health surveillance system that follows this style. Starting from concrete user requirements and scenarios, we introduce the architecture with its components for content collection, data analysis and integration. The system will allow for the monitoring of events in real-time as well as retrospectively.

Ebola Outbreak Containment: Real-Time Task and Resource Coordination With SORMAS

Background: Since the beginning of the Ebola outbreak in West Africa in 2014, more than 11,000 people died. For outbreaks of infectious diseases like this, the rapid implementation of control measures is a crucial factor for containment. In West African countries, outbreak surveillance is a paper-based process with significant delays in forwarding outbreak information, which affects the ability to react adequately to situational changes. Our objective therefore was to develop a tool that improves data collection, situation assessment, and coordination of response measures in outbreak surveillance processes for a better containment. Methods: We have developed the Surveillance and Outbreak Response Management System (SORMAS) based on findings from Nigerias 2014 Ebola outbreak. We conducted a thorough requirements engineering and defined personas and processes. We also defined a data schema with specific variables to measure in outbreak situations. We designed our system to be a cloud application that consists of interfaces for both mobile devices and desktop computers to support all stakeholders in the process. In the field, health workers collect data on the outbreak situation via mobile applications and directly transmit it to control centers. At the control centers, health workers access SORMAS via desktop computers, receive instant updates on critical situations, react immediately on emergencies, and coordinate the implementation of control measures with SORMAS. Results: We have tested SORMAS in multiple workshops and a field study in July 2015. Results from workshops confirmed derived requirements and implemented features, but also led to further iterations on the systems regarding usability. Results from the field study are currently under assessment. General feedback showed high enthusiasm about the system and stressed its benefits for an effective outbreak containment of infectious diseases. Conclusions: SORMAS is a software tool to support health workers in efficiently handling outbreak situations of infectious diseases, such as Ebola. Our tool enables a bi-directional exchange of situational data between individual stakeholders in outbreak containment. This allows instant and seamless collection of data from the field and its instantaneous analysis in operational centers. By that, SORMAS accelerates the implementation of control measures, which is crucial for a successful outbreak containment.

Infektionskrankheiten und ihre Codierung

ZusammenfassungDie Revision der Internationalen statistischen Klassifikation der Krankheiten und verwandter Gesundheitsprobleme (International Classification of Diseases – ICD) geht mit grundlegenden Änderungen der Morbiditäts- und Mortalitätsstatistik einher, die auch den Bereich der Infektionskrankheiten betreffen. Die Zuordnung der einzelnen Infektionskrankheiten zu den Kapiteln in der aktuellen ICD-10 erfolgt aufgrund unterschiedlicher Konzepte, teilweise nach auslösendem Agens, nach betroffenem Organsystem oder nach Lebensperiode. Besondere Herausforderungen der Klassifizierung der Infektionskrankheiten bestehen u. a. darin, dass regelmäßig ein Anpassungsbedarf durch neu auftretende Erreger entstehen kann. Außerdem reichen die Angaben hinsichtlich Umfang und Tiefe in der ICD-10 teilweise nicht aus, um epidemiologische Auswertungen der Daten durchzuführen.Die ICD ermöglicht den weltweiten Vergleich von Statistiken zu Infektionskrankheiten. Zunehmend wird die ICD jedoch auch für die Erhebung von Surveillance- und Forschungsdaten eingesetzt, z. B. im Rahmen des Meldewesens (Identifizierung von Meldetatbeständen), aber auch in der syndromischen Surveillance akuter Atemwegsinfektionen und für den Aufbau neuer Surveillance-Systeme sowie der Evaluation der Datenqualität durch Abgleich mit Sekundärdaten.Die Chancen der ICD-11 liegen vor allem darin, dass Infektionskrankheiten eindeutiger codiert werden können und ihre Codierung mehr relevante Informationen für die epidemiologische Bewertung enthält. Durch die hohe Komplexität können jedoch Verzerrungen in den Daten entstehen, die die Fortschreibung der Morbiditäts- und Mortalitätsstatistiken erschweren.AbstractThe revision of the International Classification of Diseases (ICD) could change morbidity and mortality statistics significantly, which also affects the area of infectious diseases. Infectious diseases are classified according to their etiology, affected body system or the life period during which the episode occurs. Specific challenges arise from emerging pathogens and the respective necessary adaptation. For epidemiologic analysis ICD-10 does not always offer enough additional information.ICD provides the basis for international comparison of infectious disease morbidity and mortality statistics, but it is also used to collect data for surveillance and research purposes, e. g. the notification system for infectious diseases, syndromic surveillance systems and the evaluation of data quality by using secondary data sources.ICD-11 offers the chance to better represent epidemiological concepts of infectious diseases by adding more relevant information as affected body system or manifestation. Due to the complexity of coding, ensuring continuity of morbidity and mortality statistics could be challenging.The revision of the International Classification of Diseases (ICD) could change morbidity and mortality statistics significantly, which also affects the area of infectious diseases. Infectious diseases are classified according to their etiology, affected body system or the life period during which the episode occurs. Specific challenges arise from emerging pathogens and the respective necessary adaptation. For epidemiologic analysis ICD-10 does not always offer enough additional information.ICD provides the basis for international comparison of infectious disease morbidity and mortality statistics, but it is also used to collect data for surveillance and research purposes, e. g. the notification system for infectious diseases, syndromic surveillance systems and the evaluation of data quality by using secondary data sources.ICD-11 offers the chance to better represent epidemiological concepts of infectious diseases by adding more relevant information as affected body system or manifestation. Due to the complexity of coding, ensuring continuity of morbidity and mortality statistics could be challenging.

Linked Data and Ontology Reference Model for Infectious Disease Reporting Systems

Linked data and ontologies are already in wide use in many fields. Especially systems based on medical data can be valuably improved by enhancing their contents and meta-models semantically, using ontologies in their backbone. This semantic enhancement brings an add-on value to standard systems, enabling an overall better data management and allowing a more intelligent data processing. In our work we focus on such a standard system, which we enhance semantically, transferring its classic relational models together with its data into a semantic model. This information system processes and analyzes data related to infectious disease reports in Germany. Data from reports on infectious diseases not only contains specific parts of microbiological and medical information, but also a combination of various aspects of contextual knowledge, that is needed in order to take measures preventing a wider spread and reducing further transmissions. In this paper we describe our practical approach for transferring the relational data models into ontologies, establishing an improved data standard for the current system in use. Moreover, we propose a semantic reference model based on different contexts, covering the requirements of semantified data from infectious disease reports.