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 followup 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 modelling.

Next-generation sequencing enables whole genome sequencing within a few hours at a minimum of cost, entailing advanced medical applications such as personalized treatments. However, this recent technology imposes new challenges to alignment and variant calling as subsequent analysis steps. Compared to former sequencing, both must deal with an increasing amount of data to process at a significantly lower data quality -and are currently not capable of that.In this work, we focus on addressing these challenges for identifying Single Nucleotide Polymorphisms, i.e. SNP calling, in genome data as one subtask of variant calling. We propose the application of a column-store in-memory database for efficient data processing and apply the statistical model that is provided by the Genome Analysis Toolkit's UnifiedGenotyper. Comparisons with the UnifiedGenotyper show that our approach can exploit all computational resources available and accelerates SNP calling up to a factor of 22x.

Next-generation sequencing enables whole genome sequencing within a few hours at a minimum of cost. However, this technology imposes new challenges to computational genome analysis tasks in terms of efficiently processing an increasing amount of error-prone data.In this work, we focus on addressing these challenges for identifying Single Nucleotide Polymorphisms as one type of genetic variants in genome data. We propose the application of a column-store in-memory database for efficient data processing to profit from built-in compression and parallelization techniques and accessing data directly from main memory instead of slower disk space. We provide a statistical model that is sensitive to input data quality and utilizes knowledge from language population studies. Comparisons with state-of-the-art tools show that our approach outperforms traditional procedures on average by magnitudes of speed whilst requiring less administration efforts.

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