Back

Predicting the impact of asymptomatic transmission,non-pharmaceutical intervention and testing on the spread of COVID19COVID19

Schwartz, I. B.; Kaufman, J. H.; Hu, K.; Bianco, S.

2020-04-22 epidemiology
10.1101/2020.04.16.20068387 medRxiv
Show abstract

We introduce a novel mathematical model to analyze the effect of removing non-pharmaceutical interventions on the spread of COVID19 as a function of disease testing rate. We find that relaxing interventions has a strong impact on the size of the epidemic peak as a function of intervention removal time. We show that it is essential for predictive models to explicitly capture transmission from asymptomatic carriers and important to obtain precise information on asymptomatic transmission by testing. The asymptomatic reservoir, reported to account for as much as 85% of transmission, will contribute to resurgence of the epidemic if public health interventions are removed too soon. Use of more basic models that fail to capture asymptomatic transmission can result in large errors in predicted clinical caseload or in fitted epidemiological parameters and, therefore, may be unreliable in estimating the risk of a second wave based on the timing of terminated interventions.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
● 1863 papers in training set
Top 3%
11.9%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 3%
11.9%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 23%
6.7%
Similar papers in this journal
4
Journal of The Royal Society Interface
● 235 papers in training set
Top 0.5%
6.7%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 27%
5.5%
Similar papers in this journal
6
Communications Medicine
○ 113 papers in training set
Top 0.3%
5.5%
7
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 15%
3.4%
50% of probability mass above
8
Journal of Theoretical Biology
○ 162 papers in training set
Top 0.9%
3.2%
9
Epidemics
○ 116 papers in training set
Top 0.7%
3.1%
10
Epidemiology
○ 32 papers in training set
Top 0.2%
2.4%
11
International Journal of Epidemiology
◐ 88 papers in training set
Top 0.6%
2.4%
12
Science Advances
● 1243 papers in training set
Top 19%
1.7%
13
BMC Medicine
○ 176 papers in training set
Top 2%
1.7%
14
PNAS Nexus
◐ 159 papers in training set
Top 1%
1.5%
15
Nature Medicine
○ 125 papers in training set
Top 2%
1.5%
16
Mathematical Biosciences
○ 49 papers in training set
Top 0.8%
1.4%
17
Mathematical Medicine and Biology: A Journal of the IMA
◐ 10 papers in training set
Top 0.1%
1.4%
18
Infectious Disease Modelling
○ 54 papers in training set
Top 0.9%
1.3%
19
eLife
● 5828 papers in training set
Top 59%
1.1%
20
BMC Public Health
○ 158 papers in training set
Top 4%
1.1%
21
Nature Human Behaviour
○ 95 papers in training set
Top 2%
1.1%
22
Statistics in Medicine
○ 40 papers in training set
Top 0.4%
1.1%
23
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
0.9%
24
Royal Society Open Science
● 214 papers in training set
Top 6%
0.8%
25
Medical Decision Making
○ 12 papers in training set
Top 0.4%
0.8%
26
PLOS Medicine
● 110 papers in training set
Top 3%
0.8%
27
Communications Physics
○ 14 papers in training set
Top 0.1%
0.8%
28
Evolution
◐ 225 papers in training set
Top 2%
0.8%
29
Frontiers in Physics
○ 21 papers in training set
Top 0.3%
0.8%
30
Epidemiology and Infection
◐ 89 papers in training set
Top 3%
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.