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Real-time epidemiological modelling during the COVID-19 emergency in Wales

Gravenor, M.; Dawson, M.; Bennett, E.; Thorpe, B.; White, C.; Rahat, A.; Archambault, D.; Picco, N.; Powathil, G.; Lucini, B.

2023-08-04 epidemiology
10.1101/2023.08.02.23293519 medRxiv
Show abstract

The sudden outbreak of the COVID-19 pandemic presented governments, policy makers and health services with an unprecedented challenge of taking real-time decisions that could keep the disease under control with non-pharmaceutical interventions, while at the same time limit as much as possible severe consequences of a very strict lockdown. Mathematical modelling has proved to be a crucial element for informing those decisions. Here we report on the rapid development and application of the Swansea Model, a mathematical model of disease spread in real time, to inform policy decisions during the COVID-19 pandemic in Wales.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
18.6%
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Top 3%
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6
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Top 0.5%
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50% of probability mass above
7
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● 235 papers in training set
Top 1%
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8
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○ 148 papers in training set
Top 2%
3.3%
9
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Top 0.5%
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10
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● 72 papers in training set
Top 0.3%
2.4%
11
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13
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Top 3%
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14
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Top 1%
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15
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17
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Top 0.9%
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18
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Top 0.8%
1.1%
19
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Top 3%
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20
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Top 12%
1.0%
21
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Top 1%
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22
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Top 0.4%
0.8%
23
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Top 59%
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24
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Top 5%
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25
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0.6%
26
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○ 78 papers in training set
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27
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.