Data to Insight’s post-COVID demand modelling tool for children’s social care
By: Local Digital Collaboration Unit
Tags: Local Government, Local Digital, Coronavirus, Digital Government, C19 Challenge

In this guest blog, Alistair Herbert, Data to Insight Manager, shares a progress update from the ‘Modelling demand for post COVID-19 surge in children’s services’ project. You can find out more about the #LocalDigitalC19Challenge and the other projects we have funded by visiting the Local Digital website.

Three months ago, Kent County Council shared a data modelling tool with our network, hoping that other councils could use it to help answer the key questions all Children Social Care teams are facing at the moment, ‘what is lockdown doing to our system?’, and ‘what’s going to happen next?’

Of course, a data model can’t tell you everything about the future. However, as a way of envisaging different scenarios it can enhance leaders’ abilities to think usefully about their local system, and helps prepare them for an uncertain future.

Developing the data model

The original model focused on schools’ referrals, and the likely ways in which we would eventually meet the “latent” social care demand which we believed had gone unseen by schools during their closures.

Since then, we’ve collaborated with Social Finance and the GMCA, with funding from MHCLG’s Local Digital C-19 Challenge to develop this original model further. We expanded the model’s scope to include key features requested by local authorities, including all social care referrals (not just schools), calculating normal seasonal variations in activity, and separating models for Child Protection Plans and Children Looked After. We also introduced more configuration options, including a whole range of new inputs on second lockdowns, “new normal” expectations, and likely volumes of latent demand.

With support from national organisations and around a dozen other councils, Social Finance rapidly iterated the core tool, building something which can easily be used by analysts in any English council, and using Data to Insight’s network of experts to feed in expectations, feature requests, bug reports, and design advice.

A truly collaborative project

The pace at which we’ve been able to move has only been possible because of the strong community that has contributed to the work by quickly testing the tool and providing feedback, combined with the dedicated central resource at Social Finance and Data to Insight. Local authorities contributed when they could, often at short notice, because they want the tool to work. Our small central team could then do the core development work in a way which suits everyone.

We have put a significant focus on ensuring the model is easy to understand for a first-time user, and that it can be tailored to the needs of any local authority. We know that we’re not trying to make the best model for a single local authority, but instead make a model that can inform decision making in any area it is used. The result is that any local authority can get started modelling demand for services, without having to do the data development work by themselves.

What we’re currently working on

We shared the updated tool with our network this week, and we’re currently working to help local authorities get the most from it. We know lots of our colleagues are busy tweaking, breaking or entirely overhauling the tool to suit their own needs. We hope that people with less time to allocate to this work can still get a direct benefit by simply dropping their local data into the tool, configuring inputs according to their local context and seeing what happens.

Download the tool

The tool is available now from the ChAT Knowledge Hub’s tool library, which is free to join for local authority children’s services data professionals.

For further information about Data to Insight, and for support in using our tools or interacting with our community, email chat.teaminbox@gmail.com.

Credit for this collaborative project is due to colleagues in the following organisations: