Back

Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply

Liu, P.; Beeler, P.; Chakrabarty, R. K.

2020-04-14 infectious diseases
10.1101/2020.04.09.20059550 medRxiv
Show abstract

Social distancing has been adopted as a non-pharmaceutical intervention to prevent the COVID-19 pandemic from overwhelming the medical resources across the United States (US). The catastrophic socio-economic impacts of this intervention could outweigh its benefits if the timing and duration of implementation are left uncontrolled and ill-strategized. Here we investigate the dynamics of social distancing on age-stratified US population and benchmark its effectiveness in reducing the burden on hospital and ICU beds. Our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. We determine an optimal intermittent social-to-no-distancing ratio of 5:1 corresponding to [~]80% reduction in healthcare demands - beyond this ratio, benefit of social distancing diminishes to a negligible level. COVID-19 Medical Demand Forecasthttps://eece.wustl.edu/chakrabarty-group/covid/

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 2%
15.0%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 5%
9.6%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 21%
7.9%
Similar papers in this journal
4
eLife
● 5828 papers in training set
Top 22%
5.5%
Similar papers in this journal
5
Nature Medicine
○ 125 papers in training set
Top 0.5%
4.4%
Similar papers in this journal
6
iScience
○ 1154 papers in training set
Top 4%
4.0%
7
Communications Physics
○ 14 papers in training set
Top 0.1%
3.2%
8
npj Digital Medicine
○ 118 papers in training set
Top 2%
2.7%
50% of probability mass above
9
PLOS ONE
● 5266 papers in training set
Top 38%
2.7%
10
PLOS Computational Biology
● 1863 papers in training set
Top 12%
2.4%
11
Epidemics
○ 116 papers in training set
Top 0.8%
2.4%
12
Communications Medicine
○ 113 papers in training set
Top 1%
2.4%
13
PNAS Nexus
◐ 159 papers in training set
Top 0.7%
2.1%
14
Journal of The Royal Society Interface
● 235 papers in training set
Top 2%
1.7%
15
Medical Decision Making
○ 12 papers in training set
Top 0.2%
1.7%
16
Frontiers in Public Health
○ 148 papers in training set
Top 4%
1.7%
17
Royal Society Open Science
● 214 papers in training set
Top 4%
1.5%
18
JAMA Network Open
● 130 papers in training set
Top 3%
1.3%
19
Science Advances
● 1243 papers in training set
Top 23%
1.3%
20
BMC Medicine
○ 176 papers in training set
Top 3%
1.1%
21
Clinical Infectious Diseases
◐ 235 papers in training set
Top 2%
1.1%
22
Emerging Infectious Diseases
● 105 papers in training set
Top 1%
1.1%
23
Science
● 477 papers in training set
Top 7%
1.1%
24
Annals of Internal Medicine
● 28 papers in training set
Top 0.5%
1.0%
25
Biophysical Journal
○ 631 papers in training set
Top 4%
1.0%
26
Proceedings of the Royal Society B: Biological Sciences
● 393 papers in training set
Top 6%
0.8%
27
Journal of Medical Internet Research
◐ 87 papers in training set
Top 3%
0.8%
28
Journal of Biomedical Informatics
○ 47 papers in training set
Top 1%
0.8%
29
BMC Public Health
○ 158 papers in training set
Top 5%
0.8%
30
Demographic Research
● 11 papers in training set
Top 0.1%
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.