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.

[1]  P. Rollin,et al.  The Epi Info Viral Hemorrhagic Fever (VHF) Application: A Resource for Outbreak Data Management and Contact Tracing in the 2014-2016 West Africa Ebola Epidemic. , 2016, The Journal of infectious diseases.

[2]  K Denecke,et al.  Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa. , 2015, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[3]  G. Krause,et al.  Taking Digital Innovation into the Field of Infectious Diseases: The Case of SORMAS® , 2017 .

[4]  David M. Aanensen,et al.  EpiCollect: Linking Smartphones to Web Applications for Epidemiology, Ecology and Community Data Collection , 2009, PloS one.

[5]  F. Shuaib,et al.  Innovative Technological Approach to Ebola Virus Disease Outbreak Response in Nigeria Using the Open Data Kit and Form Hub Technology , 2015, PloS one.

[6]  Ivar Jacobson,et al.  Unified Modeling Language , 2020, Definitions.

[7]  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.

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

[9]  K. Denecke,et al.  Implementing Surveillance and Outbreak Response Management and Analysis System (SORMAS) for Public Health in West Africa- Lessons Learnt and Future Direction , 2017 .

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

[11]  C. Sindato,et al.  A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance , 2017, JMIR public health and surveillance.

[12]  Thomas Allweyer,et al.  BPMN 2.0 : introduction to the standard for business process modeling , 2016 .