AirQo makes use of “low cost” air quality sensors to increase the coverage of air quality montioring in Kampala, Uganda. It applies machine learning methods to data to better inform descision making. Its mission to to provide this technology to other cities in sub-saharan Africa.

AirQo has a strong team of academics and software engineers, mostly based at Makerere University in Kampala, but with some at the University of Sheffield and elsewhere. University of Sheffield RSE involvement is helping with software quality in the implementation of cutting edge machine learning methods developed by researchers within live “production” web software, hosted in the cloud. This is achieved through engagement with AirQo’s Scrum (agile) project management approach.

AirQo shares its code via its GitHub organisation. In addition to live air quality data, blog posts describe some recent milestones.

The project outputs are hoped to include improved descisions based on newly available air quality data and information derived from machine learning. Ultimately this will improve air quality and health in Kampala and other African cities.

Statistical and machine learning approaches are able to make excellent predictions of an output based on several pieces of input data. However, the extent to which each of the inputs contribute to causing the output is generally unclear. Causal inference is a method that seeks to quantify causal relationships between inputs and outputs often by using (or with reference to) a “causal graph”, informed by someone expert in the data and the subject being analysed. This project, CITCoM, will democratise access to causal inference, bringing this powerful technique to a broader range of researchers in academia, government and the public sector.

This project is in its early stages, and has no outputs at present. It is anticipated that RSE will help with version control of code, good practice in python development, software testing and deployment (including on the DAFNI platform).

Understanding causality in predictive models is key to understanding which of the inputs are leading to changes in outputs. This is essential to anyone wishing to enact a policy to change the output in future. In the private sector, this might mean understanding what features of a web page (inputs) make it more likely for a customer to buy a product (output). In the public sector, it might mean understanding which public health interventions lead to behavioral change promoting better health.

The University operates several high-performance computing (HPC) clusters available for use by all research students and staff and facilitates access to specialist, multi-institution (HPC) clusters.

The RSE team has collaborated with IT Services on improving and maintaining these computing facilities over several years. Some outputs of this work include:

• A JupyterHub on the ShARC HPC cluster, which allows researchers to run Jupyter Notebooks on
• A means for ring-fencing GPU resources (and avoiding GPU memory contention issues) when running GPU jobs on HPC systems that use the Son of Grid Engine job scheduler / distributed resource manager.
• Other aspects of provisioning and management of GPU resources in the University’s HPC clusters
• Improvements to automated configuration management, resource monitoring and alerting capabilities.

This partnership has also resulted in greater visibility of certain HPC user needs through the RSE team being involved in a number of projects across the University.

Following work on developing covid-19 models as part of the Royal Society Rapid Assistance in Modelling the Pandemic (RAMP) initiative, a need was identified to be able to track the provenance of epidemiological model outputs in order to build trust from politicians, academics, the media and members of the public. This entails building on the Scottish Covid Response Consortium’s (SCRC) data pipeline which comprises a database and APIs to allow epidemiological modelling software to:

• Access version-controlled data needed to inform simulations.
• Record the output of model runs such that software version and input data provenance is traceable.

RSE involvement contributes to consortium strategy and leadership of the software API development work in an Agile mindset. RSE will also be involved with developing the Python data pipeline API.

The output of this project will enable epidemiologists to manage data better and deliver improved advice based on more traceable software and data versioning. This will extend beyond the current covid epidemic into future human and animal disease outbreaks.