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Modelling and COVID-19

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Policy influence, lessons learned and pandemic preparedness

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Sebastian Funk

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Centre for Mathematical Modelling of Infectious Diseases, LSHTM

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COVID-19 Modelling in the UK

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Timeline of COVID-19 in the UK

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Hospital admissions

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  • LD1: Initial lockdown
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  • LD2: Lockdown in response to autumn wave
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  • LD3: Lockdown in response to emergence of alpha variant
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Modelling COVID-19: early phase

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Epidemiology and clinical picture

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  • \(R_0\), serial interval, incubation period, length of stay, delay to death
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  • asymptomatic proportion, case-fatality ratio, hospitalisation rate
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Situational awareness

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  • Growth rate
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  • Short-term forecasts
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Assessing the impact of early interventions

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  • Travel restrictions
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  • Contact tracing
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Planning scenarios

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  • Total number of hospitalisations and deaths
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  • Impact of interventions
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Modelling COVID-19: after ininitial phase

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Situational awareness

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  • Nowcasts (what is the latest picture)
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  • Short-term forecasts
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Planning scenarios

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  • Relaxation of restrictions
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  • Impact of new interventions (e.g. home testing)
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  • Impact of vaccines
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Epidemiological changes

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  • Impact of new variants
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Structure of policy advice in the UK

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Modelling in the UK was underpinned by data

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  • Large amounts of data in the UK, from the national to very local scale. Data publicly available, machine-readable, downloadable and ready for analysis.
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  • Data (combined with models) was used to generate insights for policy.
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  • Modelling can help inform data collection
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Lessons learned and pandemic preparedness

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Lessons learned: Data

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Need infrastructure for rapid collection, cleaning, harmonisation, storage, sharing and publication of data.

Also a clear protocol for maximising the scope and amount of data that can be shared privately and publicly.

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Lessons learned: Modelling

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Policy makers should not have to rely on outputs from a single model.

Need to have sustainable structure in place that ensures constructive dialogue between policy makers and modellers.

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Pandemic preparedness: Tools

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We are working with WHO, CDC etc. to develop software tools for analysis and modelling of outbreak data.

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Pandemic preparedness: Training

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We are developing training material and courses for modelling in outbreaks.

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https://epiverse-trace.github.io/learn.html
https://nfidd.github.io/nfidd/

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Thank you

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