[The 2011 HUS epidemic in Germany. Challenges for disease control: what should be improved?]

Infection surveillance measures during the COVID-19 pandemic in Germany

Introduction: To address the question as to which infection surveillance measures are used during the ongoing COVID-19 pandemic in Germany and how they differ from pre-existing approaches. Methods: In accordance with the systematic approach of a scoping review, a literature search was conducted in national and international medical literature databases using a search string. The search in the databases was limited to the period from 01.01.2000 to 15.11.2020 and has been subsequently completed by hand search until 08.03.2021. A hand search, even beyond 15.11.2020, seemed necessary and reasonable, since due to the dynamics of the ongoing COVID-19 pandemic, a large number of articles and regulations are being published very quickly at short notice. Results: The literature search resulted in the following number of hits in the databases listed below: PubMed: 165 articlesCochrane: 1 review and 35 studiesWeb of Science: 217 articlesRobert Koch Institute: 49 articles Thus, a total of 467 hits were identified, with a total of 124 hits being duplicates. From these, 138 articles were considered relevant to the COVID-19 infection surveillance situation in Germany based on established criteria. After reading the full texts, 92 articles and websites were ultimately included in the scoping review. Discussion: Many of the lessons learned from previous outbreaks seem to have been implemented in the infection surveillance measures during the ongoing COVID-19 pandemic in Germany. Most of the changes compared with previous measures were based on technological streamlining of existing procedures and changes and more inclusion of the population in different infection surveillance measures.

[Intensive care back up for infectious disease disasters]

BACKGROUND

Outbreaks of infectious diseases pose particular challenges for hospitals and intensive care units.

OBJECTIVES

Typical infectiological scenarios and their significance for modern intensive care medicine are presented.

MATERIALS AND METHODS

Selected pathogens/infectious diseases that have significantly strained the resources of intensive care units are described.

RESULTS

Intensive medical care is necessary in severe cases of many infectious diseases. In the context of epidemics/pandemics, many critically ill patients have to be admitted within a short time. Examples are the 2009 H1N1 influenza pandemic, the 2011 enterohemorrhagic Escherichia coli (EHEC) outbreak in northern Germany, the 2014/2015 Ebola fever outbreak and the 2020 coronavirus disease 19 (COVID-19) pandemic. Multidisciplinary teams, protocol development, adequate staffing, and training are required to achieve optimal treatment outcomes, including prevention of healthcare worker infections.

CONCLUSIONS

Pandemics and epidemics are unique challenges for intensive care unit preparedness planning.

Public Health Research Resulting from One of the World's Largest Outbreaks Caused by Entero-Hemorrhagic Escherichia coli in Germany 2011: A Review

In 2011, Germany experienced one of the largest outbreaks of entero-hemorrhagic Escherichia coli (EHEC) ever reported. Four years thereafter, we systematically searched for scientific publications in PubMed and MEDPILOT relating to this outbreak in order to assess the pattern of respective research activities and to assess the main findings and recommendations in the field of public health. Following PRISMA guidelines, we selected 133 publications, half of which were published within 17 months after outbreak onset. Clinical medicine was covered by 71, microbiology by 60, epidemiology by 46, outbreak reporting by 11, and food safety by 9 papers. Those on the last three topics drew conclusions on methods in surveillance, diagnosis, and outbreak investigation, on resources in public health, as well as on inter-agency collaboration, and public communication. Although the outbreak primarily affected Germany, most publications were conducted by multinational cooperations. Our findings document how soon and in which fields research was conducted with respect to this outbreak.

Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa

In the context of controlling the current outbreak of Ebola virus disease (EVD), the World Health Organization claimed that 'critical determinant of epidemic size appears to be the speed of implementation of rigorous control measures', i.e. immediate follow-up of contact persons during 21 days after exposure, isolation and treatment of cases, decontamination, and safe burials. We developed the Surveillance and Outbreak Response Management System (SORMAS) to improve efficiency and timeliness of these measures. We used the Design Thinking methodology to systematically analyse experiences from field workers and the Ebola Emergency Operations Centre (EOC) after successful control of the EVD outbreak in Nigeria. We developed a process model with seven personas representing the procedures of EVD outbreak control. The SORMAS system architecture combines latest In-Memory Database (IMDB) technology via SAP HANA (in-memory, relational database management system), enabling interactive data analyses, and established SAP cloud tools, such as SAP Afaria (a mobile device management software). The user interface consists of specific front-ends for smartphones and tablet devices, which are independent from physical configurations. SORMAS allows real-time, bidirectional information exchange between field workers and the EOC, ensures supervision of contact follow-up, automated status reports, and GPS tracking. SORMAS may become a platform for outbreak management and improved routine surveillance of any infectious disease. Furthermore, the SORMAS process model may serve as framework for EVD outbreak modeling.

Practising Uncertainty in the Face of Large-Scale Disease Outbreaks

In contrast to calculable risks, uncertainty—understood here as the unexpected and non-calculable—is under-researched despite its societal and organizational relevance and omnipresence. Against this backdrop, the present study investigates how organizations practise uncertainty in the face of the numerous large-scale outbreaks of disease in Germany between 2000 and 2012. I position this study in the body of knowledge on disasters and crises, normal accident theory, and high-reliability organizations, and propose a practising uncertainty perspective that focuses on how to deal with unexpected external events and/or their latent dangers. I identify two overarching forms of practice, namely, reducing (i.e., coping with unforeseen incidents) and inducing (i.e., championing an overarching cause) uncertainty. I show that actors use both these forms of practice, which constitute the basis of the framework introduced herein, intentionally or unintentionally depending on the differing and sometimes conflicting objectives of the organizations involved.

Identification of meat-associated pathogens via Raman microspectroscopy

The development of fast and reliable sensing techniques to detect food-borne microorganisms is a permanent concern in food industry and health care. For this reason, Raman microspectroscopy was applied to rapidly detect pathogens in meat, which could be a promising supplement to currently established methods. In this context, a spectral database of 19 species of the most important harmful and non-pathogenic bacteria associated with meat and poultry was established. To create a meat-like environment the microbial species were prepared on three different agar types. The whole amount of Raman data was taken as a basis to build up a three level classification model by means of support vector machines. Subsequent to a first classifier that differentiates between Raman spectra of Gram-positive and Gram-negative bacteria, two decision knots regarding bacterial genus and species follow. The different steps of the classification model achieved accuracies in the range of 90.6%-99.5%. This database was then challenged with independently prepared test samples. By doing so, beef and poultry samples were spiked with different pathogens associated with food-borne diseases and then identified. The test samples were correctly assigned to their genus and for the most part down to the species-level i.e. a differentiation from closely-related non-pathogenic members was achieved.