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A Statistical Model for Quantifying the Needed Duration of Social Distancing for the COVID-19 Pandemic

Rakocz, N.; Fu, B.; Halperin, E.; Sankararaman, S.

2020-07-03 health informatics
10.1101/2020.05.30.20117796 medRxiv
Show abstract

Understanding the effectiveness of strategies such as social distancing is a central question in attempts to control the COVID-19 pandemic. A key unknown in social distancing strategies is the duration of time for which such strategies are needed. Answering this question requires an accurate model of the transmission trajectory. A challenge in fitting such a model is the limited COVID-19 case data available from a given location. To overcome this challenge, we propose fitting a model of SARS-CoV-2 transmission jointly across multiple locations. We apply the model to COVID-19 case data from Spain, UK, Germany, France, Denmark, and New York to estimate the distribution for the time needed for social distancing to end to range from May 2020 to July 2021 (95% credible interval), where the median date is October, 2020. Our method is not specific to COVID-19, and it can also be applied to future pandemics.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Scientific Reports
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Top 5%
8.9%
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2
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6.3%
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8
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3.3%
9
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.8%
3.2%
50% of probability mass above
10
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● 214 papers in training set
Top 1%
3.2%
11
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○ 78 papers in training set
Top 0.7%
2.7%
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13
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Top 2%
2.5%
14
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2.4%
15
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Top 0.1%
2.1%
16
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Top 0.4%
1.9%
17
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Top 0.7%
1.9%
18
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Top 0.6%
1.7%
19
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○ 32 papers in training set
Top 0.4%
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20
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Top 0.1%
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21
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Top 0.3%
1.1%
22
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Top 3%
1.1%
23
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Top 6%
0.8%
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Top 3%
0.8%
25
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Top 66%
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26
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Top 35%
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27
Proceedings of the National Academy of Sciences
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Top 41%
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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.