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Tracking the spread of novel coronavirus (2019-nCoV) based on big data

Zhao, X.; Liu, X.; Li, X.

2020-02-11 epidemiology
10.1101/2020.02.07.20021196 medRxiv
Show abstract

The novel coronavirus (2019-nCoV) appeared in Wuhan in late 2019 have infected 34,598 people, and killed 723 among them until 8th February 2020. The new virus has spread to at least 316 cities (until 1st February 2020) in China. We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China. We found the contribution of air passengers from Wuhan was decreasing gradually, but the effect of local population was increasing, indicating the trend of local transmission. However, the increase of local transmission is slow during the early stage of novel coronavirus, due to the super strict control measures carried out by government agents and communities.

Matching journals

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

1
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Top 0.1%
4.0%
50% of probability mass above
8
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188 papers in training set
Top 0.6%
3.2%
9
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○ 12 papers in training set
Top 0.1%
2.7%
10
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Top 42%
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11
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12
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Top 0.3%
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13
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Top 0.1%
1.7%
14
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1.7%
15
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Top 2%
1.5%
16
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Top 6%
1.4%
17
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Top 0.9%
1.3%
18
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● 23 papers in training set
Top 0.6%
1.3%
19
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Top 4%
1.1%
20
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Top 17%
1.1%
21
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Top 1.0%
1.0%
22
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Top 1%
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23
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○ 116 papers in training set
Top 2%
0.8%
24
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Top 1%
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25
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26
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27
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● 477 papers in training set
Top 10%
0.6%
28
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○ 15 papers in training set
Top 0.5%
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29
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Top 10%
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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.