Toward a Unified Timestamp with explicit precision

Demographic and health surveillance (DS) systems monitor and document individual- and group-level processes in well-defined populations over long periods of time. The resulting data are complex and inherently temporal. Established methods of storing and manipulating temporal data are unable to adequately address the challenges posed by these data. Building on existing standards, a temporal framework and notation are presented that are able to faithfully record all of the time-related information (or partial lack thereof) produced by surveillance systems. The Unified Timestamp isolates all of the inherent complexity of temporal data into a single data type and provides the foundation on which a Unified Timestamp class can be built. The Unified Timestamp accommodates both point- and interval-based time measures with arbitrary precision, including temporal sets. Arbitrary granularities and calendars are supported, and the Unified Timestamp is hierarchically organized, allowing it to represent an unlimited array of temporal entities.demographic surveillance, standardization, temporal databases, temporal integrity, timestamp, valid time

Is there a duty to participate in digital epidemiology?

This paper poses the question of whether people have a duty to participate in digital epidemiology. While an implied duty to participate has been argued for in relation to biomedical research in general, digital epidemiology involves processing of non-medical, granular and proprietary data types that pose different risks to participants. We first describe traditional justifications for epidemiology that imply a duty to participate for the general public, which take account of the immediacy and plausibility of threats, and the identifiability of data. We then consider how these justifications translate to digital epidemiology, understood as an evolution of traditional epidemiology that includes personal and proprietary digital data alongside formal medical datasets. We consider the risks imposed by re-purposing such data for digital epidemiology and propose eight justificatory conditions that should be met in justifying a duty to participate for specific digital epidemiological studies. The conditions are then applied to three hypothetical cases involving usage of social media data for epidemiological purposes. We conclude with a list of questions to be considered in public negotiations of digital epidemiology, including the application of a duty to participate to third-party data controllers, and the important distinction between moral and legal obligations to participate in research

Results from the First 12 Months of the National Surveillance of Healthcare Associated Outbreaks in Germany, 2011/2012

Background: In August 2011, the German Protection against Infection Act was amended, mandating the reporting of healthcare associated infection (HAI) outbreak notifications by all healthcare workers in Germany via local public health authorities and federal states to the Robert Koch Institute (RKI). Objective: To describe the reported HAI-outbreaks and the surveillance system’s structure and capabilities. Methods: Information on each outbreak was collected using standard paper forms and notified to RKI. Notifications were screened daily and regularly analysed. Results: Between November 2011 and November 2012, 1,326 paper forms notified 578 HAI-outbreaks, between 7 and 116 outbreaks per month. The main causative agent was norovirus (n = 414/578; 72%). Among the 108 outbreaks caused by bacteria, the most frequent pathogens were Clostridium difficile (25%) Klebsiella spp. (19%) and Staphylococcus spp. (19%). Multidrug-resistant bacteria were responsible for 54/108 (50%) bacterial outbreaks. Hospitals were affected most frequently (485/578; 84%). Hospital outbreaks due to bacteria were mostly reported from intensive care units (ICUs) (45%), followed by internal medicine wards (16%). Conclusion: The mandatory HAI-outbreak surveillance system describes common outbreaks. Pathogens with a particular high potential to cause large or severe outbreaks may be identified, enabling us to further focus research and preventive measures. Increasing the sensitivity and reliability of the data collection further will facilitate identification of outbreaks able to increase in size and severity, and guide specific control measures to interrupt their propagation

Timeliness of Surveillance during Outbreak of Shiga Toxin–producing Escherichia coli Infection, Germany, 2011

In the context of a large outbreak of Shiga toxin–producing Escherichia coli O104:H4 in Germany, we quantified the timeliness of the German surveillance system for hemolytic uremic syndrome and Shiga toxin–producing E. coli notifiable diseases during 2003–2011. Although reporting occurred faster than required by law, potential for improvement exists at all levels of the information chain

A web-based app to provide personalized recommendations for COVID-19

Peer Reviewe

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 Nigeria's 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.Peer Reviewe

SurvNet Electronic Surveillance System for Infectious Disease Outbreaks, Germany

Electronic Surveillance System for Infectious Disease Outbreaks, German

Reliability of case definitions for public health surveillance assessed by Round-Robin test methodology

BACKGROUND: Case definitions have been recognized to be important elements of public health surveillance systems. They are to assure comparability and consistency of surveillance data and have crucial impact on the sensitivity and the positive predictive value of a surveillance system. The reliability of case definitions has rarely been investigated systematically. METHODS: We conducted a Round-Robin test by asking all 425 local health departments (LHD) and the 16 state health departments (SHD) in Germany to classify a selection of 68 case examples using case definitions. By multivariate analysis we investigated factors linked to classification agreement with a gold standard, which was defined by an expert panel. RESULTS: A total of 7870 classifications were done by 396 LHD (93%) and all SHD. Reporting sensitivity was 90.0%, positive predictive value 76.6%. Polio case examples had the lowest reporting precision, salmonellosis case examples the highest (OR = 0.008; CI: 0.005–0.013). Case definitions with a check-list format of clinical criteria resulted in higher reporting precision than case definitions with a narrative description (OR = 3.08; CI: 2.47–3.83). Reporting precision was higher among SHD compared to LHD (OR = 1.52; CI: 1.14–2.02). CONCLUSION: Our findings led to a systematic revision of the German case definitions and build the basis for general recommendations for the creation of case definitions. These include, among others, that testable yes/no criteria in a check-list format is likely to improve reliability, and that software used for data transmission should be designed in strict accordance with the case definitions. The findings of this study are largely applicable to case definitions in many other countries or international networks as they share the same structural and editorial characteristics of the case definitions evaluated in this study before their revision