Policy, Science and the Convening Power of Data

Delve Timeline

• First contact 3rd April
• First meeting 7th April
• First working group 16th April

Data at the Heart

• Use data to answer policy questions.
• Make international comparisons for input.
• Challenges: around getting data.

Formal Background

Embodiment Factors

Human Communication

What is Machine Learning?

\[ \text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

Data as a Convener

• Data allows externalisation of cognition.
• Even when not existing, can ask: What data would we want?

Supply Chain of Ideas

The Supply Chain of Ideas

Coconut Scientists

Project Structure

Supply Chain of Ideas

Storming the Castle

• Policy is complex and uncertain.
• Requires a multi-disciplinary response.
• Scientists view themselves as Merlins but the reality differs.

Science not Scientists

• Individual scientists are subjective.
• The process of scientific consensus takes time.

Ogni scarrafone è bello a mamma soja

Neopolitan expression

• Just because “scientists say” it doesn’t mean “science says”.
• Decisions are still needed when facing uncertainty.

Delve

Information Coherence

Tenets

1. We are steered by policy need.

2. “It is amazing what you can accomplish if you do not care who gets the credit.” We’re here to be as useful as possible, as fast as possible, as a group.

3. This work is multidisciplinary - everyone should ask questions, and expect to answer them to people from other disciplines in ways they can understand and scrutinise.

Tenets

4. We do not reinvent the wheel: we use existing analysis techniques and datasets wherever possible to answer questions.

5. Being useful is more important than presentee-ism. We should be considerate of people who work different hours and have different commitments.

6. We steer a path between the Hedgehog (too attached to a particular model) and the Fox (always considering worst case outcome).

Multidisciplinarity

• Multidisciplinarity requires courage.
• The Sir Lancelots need to appreciate new skills.
• Motto There are no stupid questions.

The Avatar Model

• Experts are bottlenecks.
• Action team built from trusted collaborators of experts.
• Thrice weekly meeting of Action Team.

Explore <-> Exploit

Explore <-> Exploit

Three Phase Approach

• Light touch approach to projects.
• Phase -1: Pre-conception
• Phase 0: Conception
• Phase 1: Scoping
• Phase 2: Proof of Concept

Delve Reports

1. Facemasks 4th May 2020 (The DELVE Initiative, 2020a)
2. Test, Trace, Isolate 27th May 2020 (The DELVE Initiative, 2020b)
3. Nosocomial Infections 6th July 2020 (The DELVE Initiative, 2020c)
4. Schools 24th July 2020 (The DELVE Initiative, 2020d)
5. Economics 14th August 2020 (The DELVE Initiative, 2020e)
6. Vaccines 1st October 2020 (The DELVE Initiative, 2020f)
7. Data 24th November 2020 (The DELVE Initiative, 2020g)

Delve Data Report

• Surveillance data situation.
  • REACT Study (Imperial)
  • ONS Coronavirus (COVID-19) Infection Survey
  • RECOVERY Trial (Dexamethasone)
• Happenstance data.
  • Our report’s focus (The DELVE Initiative, 2020g)

Delve Data Report: Recommendations

• Update statutory objective of ONS to accommodate happenstance data.
• ONS and ICO to collaborate on data driving license to standardise access processes.
• Interdisciplinary pathfinder projects across government, business and academia
  • Nowcasting of economic metrics
  • Movement of populations (mobile phone data).

The Delve Action Team

The Delve Working Group

The Delve Steering Committee

The Delve Secretariat

Conclusions

• Bandwidth constraints of humans
• Data as a Convener
• Supply Chain of Ideas
• Recommendations from Delve Data Report