There is a growing number of people affected by outbreaks within humanitarian settings. Standard tools for outbreak detection & control are less effective in these challenging environments. This often results in large & uncontrolled epidemics that threaten global health security. It is therefore critical to ensure that a sensitive & timely understanding of the health status of the affected population is available.
Outbreak analytics (OA) is an emerging domain of data science dedicated to informing effective responses to disease outbreaks. The application of these advanced analytical methods & tools has been slow & uneven in humanitarian settings.
Therefore, my research aims to demonstrate that outbreak prevention & response in humanitarian settings can be strengthened by reinforced surveillance activities, feeding enhanced OA, to ensure data-informed decision making. I begin by reviewing the emerging science, & identifying key research gaps & potential candidates for novel or adapted outbreak analytical techniques. I contribute to the development of some methodological enhancements to the outbreak analytical toolbox to address the needs & capacities of outbreak response programmes in- the-field. Finally, I apply these techniques in a variety of crisis-affected & resource- limited settings, demonstrating their utility to monitor & assess operational performance, & thereby guide real-time data-driven decision-making in humanitarian settings.
My research described how OA are not only feasible, they are central to the surveillance pillar of any outbreak response. Applied in resource-limited settings, OA is not just about performing advanced data analysis during epidemics, but should complement a broader public health information context. The role of the field epidemiologist has certain key responsibilities in the OA cycle, & recent technological developments & shifts in attitude have greatly increased the feasibility of embedding such a role within humanitarian response.
Consideration must be given to the specific ethical implications & obligations related to collecting data & conducting research in humanitarian settings, including community engagement & participations, ensuring that collected data are used for action, & that these data are managed responsibly.
Limitations to my research & the role of OA generally revolve around data quality, on-site capacity for data collection & analysis, & a paucity of evidence in the peer-reviewed literature.
As an emerging, innovative discipline within epidemiology focused on, & building upon recent advances within, the technological & methodological aspects of the data pipeline, OA holds great promise to help bridge the gap between available techniques & their application in humanitarian & other resource-limited settings. It sits at the crossroads of public health planning, field epidemiology, methodological development & information technologies, creating opportunities for specialists in these fields to collaborate to meet the needs for an epidemic response. Insofar as it can help field epidemiologists collect, visualise & analyse data, & subsequently provide decision-makers with actionable information, OA will likely occupy an increasing and ever more important space in field epidemiology.
