Principal Investigator: Simon Hay, University of Oxford
The study aimed to develop and apply new mathematical models and software to quantify human movement patterns in the region to predict the risk of EVD importation between and within countries, in order to guide targeting of surveillance and interventions. These predictions are of greatest use during the growth phase of an outbreak, when the disease’s geographic extent is expanding. Because the EVD outbreak transitioned in December 2014-January 2015 from such a growth phase into a period of retraction, as the outbreak was brought under control, the need for regional predictive maps for the current outbreak lessened.
Whilst the project maintained the objective of providing continuously updated importation risk maps, there was an increased focus on developing these tools so that they can be implemented rapidly for future outbreaks. The study also adapted the outputs of this work to facilitate longer-term risk assessment and planning.
It hasn’t been done before, to rapidly run predictive models and share the results with people who need to make urgent decisions. Moving from static maps to automatically updated maps that really use the step change in data availability and digital tools. This gives us an online automated system that predicts where cases will pop up next.
Predictive statistical models of population movements in West Africa were developed which enabled predictive statistical models of the risk of the EVD importation into new regions. The models allowed for automatic updates as soon as new EVD data were available and the risk maps immediately disseminated via a dedicated website. Underlying this work was the development of three core pieces of software which were designed for ease of re-use and disseminated as open-source on the code-sharing website GitHub.
Python scripts and modules to automate downloading up-to-date Ebola data and statistics, preprocessing and handing off to predictive models using observed movement data.
Using the Step Change in Data Availability and Digital Tools for Predictive MappingView
Python scripts and modules to automate downloading up-to-date Ebola data and statistics, preprocessing and handing off to predictive models using observed movement dataView
R package containing useful functions for the analysis of movement data in disease modelling and mappingView
In August 2014, the Ebola Outbreak in West Africa was declared an International Health Emergency by WHO and within a couple of weeks ELRHA launched a rapid-response call for research to combat the crisis. The UK Department for International Development (DFID), the Wellcome Trust and ELRHA opened a special funding window through the Research for Health in Humanitarian Crises (R2HC) programme.
The aim of this emergency call was both to produce robust research findings that could contribute to the effectiveness of the response to the current outbreak and help to draw lessons for future outbreaks of Ebola and other communicable diseases. The projects funded will strengthen the evidence base for the Ebola response in topics ranging from diagnostics to anthropology, surveillance and disease control.
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