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Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak

Feng, Z.; Damon-Feng, H.; Zhao, H.

2020-04-22 health policy
10.1101/2020.04.17.20069351 medRxiv
Show abstract

Governments around the world have enacted strict social distancing policies in order to slow the spread of COVID-19. The next step is figuring out when to relax these restrictions and to what degree. Our results predict potentially disastrous implications of ending these policies too soon, based on projections made from a Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. Even when infection rates appear to be slowing down or decreasing, prematurely returning to "business as usual" produces a severe second peak far worse than the first. Furthermore, such a second peak is made more likely when very severe restrictions are initially enacted. Only an appropriately measured and committed set of restrictions can appropriately control COVID-19 outbreak levels.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 9%
18.8%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 2%
13.1%
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3
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 2%
12.1%
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4
Nature Human Behaviour
○ 95 papers in training set
Top 0.3%
5.3%
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5
Nature Communications
○ 5641 papers in training set
Top 33%
3.6%
Similar papers in this journal
50% of probability mass above
6
BMC Public Health
○ 158 papers in training set
Top 2%
3.3%
7
eLife
● 5828 papers in training set
Top 34%
3.3%
8
Nature Medicine
○ 125 papers in training set
Top 0.9%
2.8%
9
PNAS Nexus
◐ 159 papers in training set
Top 0.4%
2.5%
10
Frontiers in Public Health
○ 148 papers in training set
Top 3%
2.2%
11
Royal Society Open Science
● 214 papers in training set
Top 3%
1.9%
12
Medical Decision Making
○ 12 papers in training set
Top 0.1%
1.9%
13
Epidemics
○ 116 papers in training set
Top 1%
1.5%
14
Mathematical Biosciences
○ 49 papers in training set
Top 0.7%
1.5%
15
Journal of The Royal Society Interface
● 235 papers in training set
Top 3%
1.1%
16
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
1.1%
17
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
1.1%
18
F1000Research
○ 88 papers in training set
Top 3%
1.1%
19
Proceedings of the Royal Society B: Biological Sciences
● 393 papers in training set
Top 5%
1.1%
20
Science Advances
● 1243 papers in training set
Top 28%
1.0%
21
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.7%
1.0%
22
PeerJ
◐ 308 papers in training set
Top 10%
0.9%
23
Communications Physics
○ 14 papers in training set
Top 0.1%
0.9%
24
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.8%
0.9%
25
Physical Biology
○ 46 papers in training set
Top 0.9%
0.9%
26
International Journal of Infectious Diseases
○ 129 papers in training set
Top 3%
0.6%
27
Mathematical Medicine and Biology: A Journal of the IMA
◐ 10 papers in training set
Top 0.3%
0.6%
28
Annals of Epidemiology
○ 21 papers in training set
Top 0.7%
0.6%
29
Frontiers in Physics
○ 21 papers in training set
Top 0.4%
0.6%
30
Travel Medicine and Infectious Disease
○ 14 papers in training set
Top 0.5%
0.6%

"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.