China

January 25th, 2020 (Lunar New Year's Day)

Preliminary risk analysis of 2019 novel coronavirus spread within and beyond China

Shengjie Lai1*, Isaac I. Bogoch2, Alexander Watts3,4, Kamran Khan2,3,4, Zhongjie Li5, Andrew Tatem1*

  • 1WorldPop, School of Geography and Environmental Science, University of Southampton, UK
  • 2Department of Medicine, University of Toronto, Toronto, Canada
  • 3Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
  • 4Bluedot, Toronto, Canada
  • 5Division of Infectious Diseases, Chinese Centre for Disease Control and Prevention
  • *Email: Shengjie.Lai@soton.ac.uk; A.J.Tatem@soton.ac.uk

As of January 25th, 2020 (Beijing time), China has reported 1409 confirmed cases, 2032 suspected cases, and 42 deaths of 2019 Novel Coronavirus (2019-nCoV) infections, with most reported from Wuhan city, Hubei Province [1-3]. Nearly all provinces have confirmed imported cases from Wuhan and secondary transmission has been reported in some provinces. The spread of the virus could have been exacerbated by the surge in domestic travel during the 40-day Lunar New Year celebrations (from 10 January to 18 February 2020) – the largest annual human migration in the world, comprised of hundreds of millions of people travelling across the country.

We used de-identified and aggregated domestic population movement data from 2013 to 2015, derived from Baidu Location-Based Services (LBS) [4], and international air travel data in 2018, obtained from the International Air Transport Association (IATA) [5], to explore patterns of mobility of travellers from Wuhan to other cities in China, and inform the risk of 2019-nCoV spreading across and beyond the country during the Lunar New Year migration.

Using the 2013-2015 LBS data, we found that a large number of travellers were likely departing Wuhan into neighbouring cities and other megacities in China before Lunar New Year (Figures 1-3 and Tables 1-3). There was a high correlation between the number of imported cases and the risk of importation via travellers from Wuhan within the two weeks before Lunar New Year’s Day (Figure 4). Further, a high proportion of cases travelled with symptoms at the early stage of the outbreak. Although a cordon sanitaire of Wuhan and some cities in Hubei Province has been in place since January 23rd, 2020, the timing of this may have occurred during the latter stages of peak population numbers leaving Wuhan (Figure 1). Should secondary outbreaks occur in the cities and provinces that receive high volumes of travellers from Wuhan, e.g. Beijing, Shanghai, and Guangzhou, these could contribute to further spread of infection to other highly connected cities within China via movement after the 7-day public holiday (Figures 5-7). Additionally, based on historical air travel data, the connectivity between high-risk cities in China and other countries was defined for the three months around Lunar New Year holiday (Tables 4 and 5). We have initially focussed on specific destination cities in Africa due to the weak surveillance and health systems of this vulnerability region (Tables 6-7 and Figures 8-9), but will expand similar assessments to the rest of the World.

Given the current epidemic and limited understanding of the epidemiology of this disease, our findings of travel patterns from historical data could help contribute to tailoring public health interventions. However, it is important to highlight that our analysis assumes “business as usual” travel based on previous non-outbreak years and we are currently in unprecedented territory, with likely significant changes to human travel behaviours across China. We are closely monitoring the epidemic, and further analyses will be conducted to estimate the risk of onward domestic and international spread of the virus during the Lunar New Year and the next few months. Moreover, we will also attempt to evaluate the effectiveness of the transport lockdown in Chinese cities, and the impact of movements of people returning from holiday on the transmission of the 2019-nCoV virus.

Reference

  1. Zhu N, et al. (2020) A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. DOI: 10.1056/NEJMoa2001017
  2. Chan J, et al. (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. DOI: 10.1016/S0140-6736(20)30154-9
  3. Huang C, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. DOI: 10.1016/S0140-6736(20)30183-5
  4. Baidu Migration. http://qianxi.baidu.com/
  5. Bogoch I, et al. (2020) Pneumonia of Unknown Etiology in Wuhan, China: Potential for International Spread Via Commercial Air Travel. Journal of Travel Medicine, https://doi.org/10.1093/jtm/taaa008

Section I. Domestic Travel

WorldPop

Figure 1. Patterns of daily human movement by county in Wuhan City and Hubei Province across six months.

Shadow colours:

  • Green: 2 weeks before Lunar New Year’s Day;
  • Red: 2 weeks since Lunar New Year’s Day;
  • Purple: Lantern Festival;
  • Pink: Tomb Sweeping Day;
  • Red line: Lockdown day of cities in Hubei.
Relative netflow = (Inflow – Outflow)/population, based on the population movement data in 2013-2014 from Baidu, Inc.

Table 1. Top 30 ranked cities in mainland China receiving travellers from Wuhan during the two weeks before Lunar New Year's Day.

Rank

City

Population (million)*

Province

Volume (%)**

1

Jingzhou

5.7

Hubei

7.26

2

Xiangfan

5.6

Hubei

7.11

3

Xianning

2.5

Hubei

6.17

4

Beijing

21.7

Beijing

6.10

5

Huanggang

6.3

Hubei

5.94

6

Yichang

4.1

Hubei

5.86

7

Huangshi

2.5

Hubei

5.18

8

Xiaogan

4.9

Hubei

4.73

9

Sheng Zhixia

3.5

Hubei

4.62

10

Shiyan

3.4

Hubei

4.14

11

Shanghai

24.2

Shanghai

3.52

12

Enshi

3.3

Hubei

3.51

13

Jingmen

2.9

Hubei

3.06

14

Suizhou

2.2

Hubei

2.49

15

Guangzhou

14.0

Guangdong

2.45

16

Zhengzhou

9.6

Henan

2.22

17

Ezhou

1.1

Hubei

1.93

18

Tianjin

15.6

Tianjin

1.56

19

Jiaxing

4.6

Zhejiang

1.25

20

Hangzhou

9.0

Zhejiang

1.23

21

Changsha

7.6

Hunan

1.13

22

Xi'an

8.3

Shaanxi

1.02

23

Nanjing

8.3

Jiangsu

0.97

24

Shenzhen

10.2

Guangdong

0.96

25

Chongqing

30.9

Chongqing

0.82

26

Fuzhou

7.6

Fujian

0.58

27

Nanchang

5.4

Jiangxi

0.57

28

Chengdu

14.3

Sichuan

0.56

29

Hefei

7.9

Anhui

0.52

30

Dongguan

8.3

Guangdong

0.45

 

Other

1115.8

 

12.09

 

Total

1371.5

 

100.00

* 2016 population, National Bureau of Statistics, P.R. China.
** Percentage of travellers leaving Wuhan city within 2 weeks before the Lunar New Year in 2014 and 2015. Data were obtained from Baidu, Inc., a Chinese technology company specializing in Internet-related and location-based services with nearly 9 billion location requests each day.

Table 2. Top 30 ranked cities in mainland China receiving travellers from Wuhan during the two weeks since Lunar New Year’s Day.

Rank

City

Population (million)*

Province

Volume (%)**

1

Yichang

4.1

Hubei

7.48

2

Jingzhou

5.7

Hubei

6.65

3

Xiangfan

5.6

Hubei

6.48

4

Huanggang

6.3

Hubei

5.91

5

Beijing

21.7

Beijing

5.56

6

Xiaogan

4.9

Hubei

5.16

7

Xianning

2.5

Hubei

4.25

8

Sheng Zhixia

3.5

Hubei

4.22

9

Shanghai

24.2

Shanghai

3.97

10

Shiyan

3.4

Hubei

3.89

11

Jingmen

2.9

Hubei

3.51

12

Huangshi

2.5

Hubei

3.46

13

Guangzhou

14.0

Guangdong

3.07

14

Enshi

3.3

Hubei

3.01

15

Suizhou

2.2

Hubei

2.50

16

Ezhou

1.1

Hubei

2.26

17

Zhengzhou

9.6

Henan

2.13

18

Changsha

7.6

Hunan

1.78

19

Tianjin

15.6

Tianjin

1.65

20

Shenzhen

10.2

Guangdong

1.24

21

Xi'an

8.3

Shaanxi

1.24

22

Nanjing

8.3

Jiangsu

1.13

23

Hangzhou

9.0

Zhejiang

1.12

24

Jiaxing

4.6

Zhejiang

1.04

25

Nanchang

5.4

Jiangxi

0.83

26

Chongqing

30.9

Chongqing

0.82

27

Fuzhou

7.6

Fujian

0.82

28

Hefei

7.9

Anhui

0.78

29

Suzhou

10.6

Jiangsu

0.51

30

Dongguan

8.3

Guangdong

0.47

 

Other

1119.5

 

13.04

 

Total

1371.5

 

100.00

* 2016 population, National Bureau of Statistics, P.R. China.
** Percentage of travellers leaving Wuhan city within 2 weeks since the first day of the Lunar New Year in 2014 and 2015. Data were obtained from Baidu, Inc., a Chinese technology company specializing in Internet-related and location-based services with nearly 9 billion location requests each day.

Table 3. The rank of provinces in mainland China receiving travellers from Wuhan city around Lunar New Year's Day.

Rank

Within 2 weeks before Lunar New Year

Within 2 weeks since Lunar New Year

Province*

Population (million) a

Volume (%) b

Province*

Population (million) a

Volume (%) b

1

Beijing

21.5

16.07

Beijing

21.5

13.50

2

Guangdong

113.5

12.19

Guangdong

113.5

13.32

3

Henan

96.1

9.48

Shanghai

24.2

9.64

4

Shanghai

24.2

9.25

Henan

96.1

7.95

5

Zhejiang

57.4

8.19

Zhejiang

57.4

7.22

6

Jiangsu

80.5

5.51

Jiangsu

80.5

6.81

7

Hunan

69.0

4.80

Hunan

69.0

6.29

8

Shaanxi

38.6

4.54

Shaanxi

38.6

4.98

9

Tianjin

15.6

4.11

Tianjin

15.6

4.00

10

Shandong

100.5

3.66

Shandong

100.5

3.89

11

Sichuan

83.4

3.13

Fujian

39.4

3.70

12

Jiangxi

46.5

2.75

Anhui

63.2

3.27

13

Fujian

39.4

2.72

Jiangxi

46.5

2.90

14

Anhui

63.2

2.62

Sichuan

83.4

2.13

15

Chongqing

31.0

2.15

Chongqing

31.0

2.00

16

Hebei

75.6

1.94

Hebei

75.6

1.74

17

Yunnan

48.3

1.22

Liaoning

43.6

1.21

18

Guangxi

49.3

1.10

Yunnan

48.3

1.08

19

Liaoning

43.6

1.06

Guangxi

49.3

1.00

20

Hainan

9.3

0.58

Shanxi

37.2

0.62

21

Shanxi

37.2

0.54

Hainan

9.3

0.48

22

Guizhou

36.0

0.47

Guizhou

36.0

0.46

23

Heilongjiang

37.7

0.40

Heilongjiang

37.7

0.41

24

Xinjiang

24.9

0.40

Xinjiang

24.9

0.33

25

Gansu

26.4

0.32

Jilin

27.0

0.31

26

Jilin

27.0

0.31

Gansu

26.4

0.26

27

Inner Mongolia

25.3

0.29

Inner Mongolia

25.3

0.25

28

Ningxia

6.9

0.11

Ningxia

6.9

0.11

29

Qinghai

6.0

0.07

Qinghai

6.0

0.10

30

Tibet

3.4

0.03

Tibet

3.4

0.03

* All provinces except Tibet and Qinghai have reported imported or local confirmed cases, as of 08:20 on January 25th, 2020 (Beijing time).
a 2016 population, National Bureau of Statistics, P.R. China..
b Percentage of travellers leaving Wuhan city within 2 weeks since the first day of the Lunar New Year in 2014 and 2015. Data were obtained from Baidu, Inc., a Chinese technology company specializing in Internet-related and location-based services with nearly 9 billion location requests each day.

WorldPop

Figure 2. Risk of cities in mainland China receiving travellers with 2019-nCoV infections from Wuhan during the Lunar New Year migration.

The risk of importation at city level was preliminarily defined as the percentage of travellers received by each city out of the total volume of travellers leaving Wuhan within 2 weeks before and since the first day of Lunar New Year, based on the population movement data from Baidu, Inc.

WorldPop

Figure 3. Risk of provinces in mainland China receiving travellers with 2019-nCoV infections from Wuhan during the Lunar New Year migration.

The risk of importation at provincial level was preliminarily defined as the percentage of travellers received by each province out of the total volume of travellers leaving Wuhan within 2 weeks before and since the first day of Lunar New Year, based on the population movement data from Baidu, Inc.

WorldPop

Figure 4. Time distributions of imported cases and correlation between the number of imported cases and the risk of importation via travellers from Wuhan within the two weeks before Lunar New Year's Day.

(A)-(C) The time distribution of imported cases travelling from Wuhan, illness onset, admission to hospital, and diagnosis by province. (D) correlation between the number of imported cases reported in each province and the risk of importation via travellers. The risk of importation at provincial level was preliminarily defined as the percentage of travellers received by each province out of the total volume of travellers leaving Wuhan within 2 weeks before and since the first day of Lunar New Year, based on the population movement data from Baidu, Inc. The data on cases as of January 24thip>, 2020, were obtained from the websites of Chinese National and Local Health Commissions.

WorldPop

Figure 5. Patterns of daily human movement by county in Beijing, Shanghai, and Guangdong Province across six months.

Shadow colours:

  • Green: 2 weeks before Lunar New Year;
  • Red: 2 weeks since Lunar New Year;
  • Purple: Lantern Festival;
  • Pink: Tomb Sweeping Day;
Relative netflow = (Inflow – Outflow)/population, based on the population movement data in 2013-2014 from Baidu, Inc.

Risk of cities in mainland China receiving travellers from 18 high-risk cities (blue circles) with 2019-nCoV infections or importations during the next four weeks since Lunar New Year's Day

Figure 6. Risk of cities in mainland China receiving travellers from 18 high-risk cities (blue circles) with 2019-nCoV infections or importations during the next four weeks since Lunar New Year's Day.

The risk of importation at city level was preliminarily defined as the averaged percentage of travellers received by each city out of the total volume of travellers leaving each high-risk city, based on the population movement data from Baidu, Inc.
18 high-risk cities include Wuhan and other 17 cities in other provinces receiving high volume of travellers from Wuhan before the Lunar New Year: Beijing, Shanghai, Guangzhou, Zhengzhou, Tianjin, Hangzhou, Jiaxing, Changsha, Xi’an, Nanjing, Shenzhen, Chongqing, Nanchang, Chengdu, Hefei, Fuzhou, Dongguan.

Estimated connectivity of cities in mainland China receiving travellers from 18 high-risk cities (blue circles) with 2019-nCoV infections or importations during the four weeks following Lunar New Year's Day

Figure 7. Estimated connectivity of cities in mainland China receiving travellers from 18 high-risk cities (blue circles) with 2019-nCoV infections or importations during the four weeks following Lunar New Year's Day.

The arrows show the link and direction of the risk of importation at city level, preliminarily defined as the percentage of travellers received by each city (top 10 ranked cities) out of the total volume of travellers leaving each high-risk city (18 cities), based on the population movement data from Baidu, Inc.
18 high-risk cities: Wuhan in Wuhan Province and 17 cities (Beijing, Shanghai, Guangzhou, Zhengzhou, Tianjin, Hangzhou, Jiaxing, Changsha, Xi’an, Nanjing, Shenzhen, Chongqing, Nanchang, Chengdu, Hefei, Fuzhou, Dongguan) in other provinces receiving high volume of travellers from Wuhan before the Lunar New Year.

Section 2. International Travel

Table 4. Top 30 ranked cities across the globe receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year’s Day and 2 and half months following Lunar New Year's Day.

Rank

City

Country/region

Volume (in thousands)

Risk (%)*

1

Bangkok

Thailand

1062.9

7.86

2

Hong Kong

Hong Kong SAR, China

1001.7

7.41

3

Taipei

Taiwan, China

857.5

6.34

4

Seoul

South Korea

757.9

5.61

5

Tokyo

Japan

714.3

5.28

6

Singapore

Singapore

568.1

4.20

7

Phuket

Thailand

492.8

3.65

8

Osaka

Japan

434.1

3.21

9

Kuala Lumpur

Malaysia

382.7

2.83

10

Macau

Macau SAR, China

260.4

1.93

11

Denpasar Bali

Indonesia

222.2

1.64

12

Sydney

Australia

207.4

1.53

13

Chiang Mai

Thailand

156.9

1.16

14

Melbourne

Australia

154.5

1.14

15

Los Angeles

United States

154.5

1.14

16

New York

United States

145.9

1.08

17

Dubai

United Arab Emirates

144.9

1.07

18

Nha Trang

Viet Nam

143

1.06

19

London

United Kingdom

142.1

1.05

20

Ho Chi Minh City

Viet Nam

142

1.05

21

Nagoya

Japan

140.1

1.04

22

Kota Kinabalu

Malaysia

130.4

0.96

23

Phnom Penh

Cambodia

127.5

0.94

24

Krabi

Thailand

125.2

0.93

25

Manila

Philippines

121.9

0.90

26

Siem Reap

Cambodia

121.4

0.90

27

Paris

France

119.5

0.88

28

Jakarta

Indonesia

113.9

0.84

29

Kaohsiung

Taiwan

107.6

0.80

30

Frankfurt

Germany

103.3

0.76

Other

4158.2

30.77

 

Total

 

13514.9

100

* Relative risk was preliminary defined as the percentage of airline travellers received by each city out of the total volume of travellers leaving high-risk cities (18 cities), based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA).The Lunar New Year in 2018 started from February 16th, 2018.

Table 5. Top 30 ranked countries or regions receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year’s Day and 2 and half months following Lunar New Year's Day.

Rank

Country/region

Volume (in thousands)

Risk *

1

Thailand

2031.9

15.03

2

Japan

1563.3

11.57

3

Hong Kong SAR, China

1001.7

7.41

4

Taiwan, China

979.7

7.25

5

South Korea

936.6

6.93

6

United States

773.3

5.72

7

Malaysia

634.3

4.69

8

Singapore

568.1

4.20

9

Viet Nam

468.4

3.47

10

Australia

455.6

3.37

11

Indonesia

412.5

3.05

12

Cambodia

262.9

1.95

13

Macao SAR, China

260.4

1.93

14

Philippines

250.3

1.85

15

Germany

234.9

1.74

16

Canada

208.5

1.54

17

United Kingdom

190.7

1.41

18

United Arab Emirates

162.3

1.20

19

Italy

152.9

1.13

20

Russia

151.3

1.12

21

France

137.9

1.02

22

New Zealand

120.7

0.89

23

India

106.7

0.79

24

Spain

105.8

0.78

25

Turkey

66.5

0.49

26

Egypt

57.5

0.43

27

Sri Lanka

55.7

0.41

28

Maldives

50.7

0.37

29

Netherlands

44.9

0.33

30

Myanmar

43.3

0.32

Other

1025.6

7.59

 

Total

13514.9

100

* Relative risk was preliminary defined as the percentage of airline travellers received by each city out of the total volume of travellers leaving high-risk cities (18 cities), based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA). The Lunar New Year in 2018 started from February 16th, 2018.

Estimated connectivity of cities in mainland China receiving travellers from 18 high-risk cities (blue circles) with 2019-nCoV infections or importations during the four weeks following Lunar New Year's Day

Figure 8. Geographic distribution of cities across the globe receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year’s Day and 2 and half months following Lunar New Year’s Day. The volume of airline travellers of the top 30 ranked cities is presented
Based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA). The Lunar New Year in 2018 started from February 16th, 2018.

Table 6. . Top 30 ranked cities in Africa receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year’s Day and 2 and half months following Lunar New Year's Day.

Rank

City

Country/region

Volume

%*

1

Cairo

Egypt

56735

20.49

2

Johannesburg

South Africa

20530

7.42

3

Mauritius

Mauritius

18297

6.61

4

Addis Ababa

Ethiopia

17882

6.46

5

Casablanca

Morocco

15787

5.70

6

Nairobi

Kenya

12859

4.64

7

Entebbe

Uganda

8246

2.98

8

Accra

Ghana

8211

2.97

9

Lagos

Nigeria

8087

2.92

10

Lusaka

Zambia

7672

2.77

11

Dar Es Salaam

Tanzania

6769

2.44

12

Algiers

Algeria

6074

2.19

13

Luanda

Angola

5994

2.16

14

Khartoum

Sudan

5412

1.95

15

Abuja

Nigeria

4193

1.51

16

Lubumbashi

Congo (Kinshasa)

3546

1.28

17

Abidjan

Cote D'Ivoire

3511

1.27

18

Cape Town

South Africa

3461

1.25

19

Conakry

Guinea

3455

1.25

20

Tunis

Tunisia

2912

1.05

21

Libreville

Gabon

2786

1.01

22

Harare

Zimbabwe

2665

0.96

23

Dakar

Senegal

2659

0.96

24

Maputo

Mozambique

2560

0.92

25

Antananarivo

Madagascar

2515

0.91

26

Nouakchott

Mauritania

1955

0.71

27

Malabo

Equatorial Guinea

1864

0.67

28

Mahe Island

Seychelles

1850

0.67

29

Durban

South Africa

1815

0.66

30

Ndola

Zambia

1796

0.65

* The percentage of airline travellers received by each city in Africa out of the total volume of travellers leaving high-risk cities (18 cities) into Africa, based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA). The Lunar New Year in 2018 started from February 16th, 2018.

Table 7. African countries or territories receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year's Day and 2 and half months following Lunar New Year's Day.

Rank

Country/region

Volume

%*

Rank

Country/region

Volume

%*

1

Egypt

57516

20.77

27

Seychelles

1863

0.67

2

South Africa

26405

9.54

28

Botswana

1627

0.59

3

Ethiopia

18393

6.64

29

Djibouti

1602

0.58

4

Mauritius

18297

6.61

30

Mali

1587

0.57

5

Morocco

16974

6.13

31

Congo (Brazzaville)

1500

0.54

6

Nigeria

13734

4.96

32

Chad

1425

0.51

7

Kenya

13185

4.76

33

Rwanda

1386

0.50

8

Zambia

9471

3.42

34

Sierra Leone

1330

0.48

9

Tanzania

8388

3.03

35

Namibia

1207

0.44

10

Uganda

8246

2.98

36

Malawi

1139

0.41

11

Ghana

8211

2.97

37

Benin

890

0.32

12

Algeria

7887

2.85

38

Togo

858

0.31

13

Angola

5994

2.16

39

Lesotho

853

0.31

14

Sudan

5433

1.96

40

Reunion

809

0.29

15

Congo (Kinshasa)

5248

1.90

41

Niger

790

0.29

16

Mozambique

3928

1.42

42

Liberia

711

0.26

17

Cote D'Ivoire

3511

1.27

43

South Sudan

711

0.26

18

Guinea

3455

1.25

44

Burkina Faso

406

0.15

19

Tunisia

2912

1.05

45

Gambia

365

0.13

20

Gabon

2786

1.01

46

Central African Rep

339

0.12

21

Cameroon

2734

0.99

47

Cape Verde

276

0.10

22

Zimbabwe

2716

0.98

48

Eritrea

246

0.09

23

Senegal

2659

0.96

49

Burundi

232

0.08

24

Madagascar

2515

0.91

50

Comoros

178

0.06

25

Mauritania

1955

0.71

51

Somalia

68

0.02

26

Equatorial Guinea

1864

0.67

52

Guinea-Bissau

52

0.02

* The percentage of airline travellers received by each city in Africa out of the total volume of travellers leaving high-risk cities (18 cities) into Africa, based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA).The Lunar New Year in 2018 started from February 16th, 2018.

WorldPop

Figure 3. Geographic distribution of African cities receiving airline travellers from 18 high-risk cities (Figure 6) in mainland China over a period of three months, representing 15 days before Lunar New Year's Day and 2 and half months following Lunar New Year's Day.

Based on air travel data from February to April 2018, obtained from the International Air Travel Association (IATA). The Lunar New Year in 2018 started from February 16th, 2018.

WorldPop China datasets

WorldPop China datasets are available to download here

WorldPop