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.

[1]  Matthieu-Patrick Schapranow.  Real-time Security Extensions for EPCglobal Networks , 2014 .

[2]  J. Benzler,et al.  SurvNet Electronic Surveillance System for Infectious Disease Outbreaks, Germany , 2007, Emerging infectious diseases.

[3]  Matthieu-P. Schapranow.  Real-time security extensions for EPCglobal networks: case study for the pharmaceutical industry , 2013 .

[4]  D. Flannanghan.  JavaScript: The definitive guide , 1999 .

[5]  F. Mahoney,et al.  Ebola Virus Disease Outbreak — Nigeria, July–September 2014 , 2014, MMWR. Morbidity and mortality weekly report.

[6]  W. Haas,et al.  [Pandemic preparedness planning. What did we learn from the influenza pandemic (H1N1) 2009?]. , 2010, Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz.

[7]  Hasso Plattner,et al.  A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases , 2013 .

[8]  A. Sanchez,et al.  The reemergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995. Commission de Lutte contre les Epidémies à Kikwit. , 1999, The Journal of infectious diseases.

[9]  P. Effler,et al.  EbolaTracks: an automated SMS system for monitoring persons potentially exposed to Ebola virus disease. , 2015, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  Peter Tabeling,et al.  Fundamental Modeling Concepts: Effective Communication of It Systems , 2006 .

[11]  Hasso Plattner,et al.  High-Performance In-Memory Genome Data Analysis: How In-Memory Database Technology Accelerates Personalized Medicine , 2013 .

[12]  J. Sachs,et al.  Controlling Ebola: next steps , 2014, The Lancet.

[13]  Hasso Plattner,et al.  High-Performance In-Memory Genome Data Analysis , 2014, In-Memory Data Management Research.

[14]  Christoph Meinel,et al.  Design Thinking Research , 2012 .

[15]  William Lidwell,et al.  Universal Principles of Design , 2003 .

[16]  G. Poggensee,et al.  Development of a risk assessment tool for contact tracing people after contact with infectious patients while travelling by bus or other public ground transport: a Delphi consensus approach , 2013, BMJ Open.