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Epidemics, Air Travel, and Elimination in a Globalized World: The Case of Measles

Truelove, S. A.; Mier-y-Teran-Romero, L.; Gastanaduy, P.; Taylor Walker, A.; Berro, A.; Lessler, J.; Johansson, M.

2020-05-12 epidemiology
10.1101/2020.05.08.20095414 medRxiv
Show abstract

Although the United States (U.S.) declared measles eliminated in 2000, so long as measles circulates globally, continued importations will trigger sporadic U.S. outbreaks. The United States is highly connected to the rest of the world, and importations occur largely as a result of air travel by U.S. residents and international visitors. We developed a model to assess the risk of measles virus importation from Europe, Asia, and Africa through air travel. We projected 308 (95% prediction interval, 151-518) total measles cases imported from 66 countries during 2006-2015; 290 importations were reported. The model projected a spike in importation risk from Israel during October 2018, coincident with large, importation-triggered outbreaks in New York and New Jersey. Our model shows U.S. importation risk primarily originates from European and Asian countries. Models such as this inform preemptive actions to mitigate infectious disease threats, but only if we prioritize global surveillance and data sharing.

Matching journals

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

1
Epidemics
○ 116 papers in training set
Top 0.1%
11.6%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 17%
10.4%
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3
Journal of Travel Medicine
◐ 18 papers in training set
Top 0.1%
6.1%
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4
BMC Medicine
○ 176 papers in training set
Top 0.5%
5.3%
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5
Nature Medicine
○ 125 papers in training set
Top 0.4%
4.7%
Similar papers in this journal
6
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.4%
7
Vaccine
○ 203 papers in training set
Top 0.9%
3.2%
8
Science
● 477 papers in training set
Top 3%
3.1%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 19%
2.7%
50% of probability mass above
10
eLife
● 5828 papers in training set
Top 39%
2.7%
11
PLOS Neglected Tropical Diseases
● 466 papers in training set
Top 3%
2.7%
12
Viruses
○ 332 papers in training set
Top 2%
2.6%
13
PLOS ONE
● 5266 papers in training set
Top 42%
2.3%
14
Communications Medicine
○ 113 papers in training set
Top 1%
2.1%
15
Emerging Infectious Diseases
● 105 papers in training set
Top 0.7%
2.1%
16
Science Advances
● 1243 papers in training set
Top 17%
2.1%
17
Nature Human Behaviour
○ 95 papers in training set
Top 1%
1.9%
18
Clinical Infectious Diseases
◐ 235 papers in training set
Top 2%
1.9%
19
Scientific Reports
○ 3612 papers in training set
Top 54%
1.7%
20
International Journal of Infectious Diseases
○ 129 papers in training set
Top 1%
1.7%
21
The Lancet Infectious Diseases
○ 73 papers in training set
Top 0.6%
1.6%
22
The Journal of Infectious Diseases
◐ 202 papers in training set
Top 2%
1.4%
23
PLOS Global Public Health
● 344 papers in training set
Top 6%
1.3%
24
BMC Infectious Diseases
○ 133 papers in training set
Top 3%
1.3%
25
Eurosurveillance
● 83 papers in training set
Top 0.7%
1.1%
26
PLOS Biology
● 486 papers in training set
Top 8%
1.1%
27
BMC Public Health
○ 158 papers in training set
Top 4%
1.0%
28
Science Translational Medicine
● 127 papers in training set
Top 3%
1.0%
29
Cell
○ 431 papers in training set
Top 10%
0.8%
30
JAMA Network Open
● 130 papers in training set
Top 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.