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LitCovid-PubTator

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T9","span":{"begin":816,"end":825},"obj":"Disease"},{"id":"T10","span":{"begin":2043,"end":2047},"obj":"Disease"}],"attributes":[{"id":"A9","pred":"mondo_id","subj":"T9","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A10","pred":"mondo_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"}],"text":"Estimation of risk of transmission\nTo estimate the relative risk of 2019-nCoV transmission, we considered all infected passengers who travelled between 1 January and 31 January to possess a maximum risk of transmission 1 (and no infected passengers means no risk) and estimated the relative risk of each country based on the number of passengers who travelled from each of the four cities. Thus countries with a higher number of passengers travelling from any of these cities had a higher risk of transmission. We then weighted the risk estimated for each city with the number of reported infected people in each city by 31 January 2020 [14] and estimated the mean average risk of transmission termed as ‘Risk index’ which follows the equation below:where x is the destination country, Risk index (x) is the risk of infection importation in country x, P(x)n is the number of passengers to country x from city n, Pn is the total number of passengers who left city n, In is the number of infected people in city n and Cn is the population size of city n. The risk index denotes the risk of at least one case being imported into a country or territory where 1 means an absolute certainty and 0 means no risk at all. Our model assumed that there is no case outside China and thus ignored if any country already had imported case(s). In countries where 2019-nCoV is already detected, the risk index would explain the risk of importing additional infected individuals from China.\nWe performed a Pearson correlation coefficient test between the risk index of the country and the WHO's reported case number from the country. We grouped the countries in four quantiles based on the risk index where high-risk countries were grouped as the 4th (\u003e75th percentiles) and the 3rd (\u003e50th to ⩽75th percentiles) quantiles and low-risk countries were grouped as the 2nd (\u003e25th to ⩽50th percentiles) and the 1st (⩽25th percentile) quantiles (Table 1).\nTable 1. The list of countries or territories based on their risk index in different quantiles for 2019-nCoV (SARS-COV-2) transmission\n4th Quantile risk index (highest risk) 3rd Quantile risk index 2nd Quantile risk index 1st Quantile risk index (lowest risk)\nSl/Rank Country Risk index Country Risk index Country Risk index Country Risk index\n1 China 0.609603126 Sweden 0.000320248 Kenya 3.53 × 10−05 Jamaica 3.84 × 10−06\n2 Thailand 0.099432816 Laos 0.000296262 Peru 3.18 × 10−05 Serbia 3.22 × 10−06\n3 Cambodia 0.05294058 Brazil 0.00027186 Algeria 3.11 × 10−05 Togo 2.73 × 10−06\n4 Malaysia 0.041899039 Denmark 0.000254904 French Polynesia 3.07 × 10−05 Uganda 2.71 × 10−06\n5 Canada 0.02730388 Oman 0.000248884 Iceland 3.00 × 10−05 Tonga 2.69 × 10−06\n6 USA 0.021169936 Israel 0.000221865 Samoa 2.74 × 10−05 The Bahamas 2.39 × 10−06\n7 Japan 0.01479856 Ukraine 0.000209515 Tanzania 2.73 × 10−05 Cote d'Ivoire 2.02 × 10−06\n8 India 0.010256629 Poland 0.0002 Palau 2.63 × 10−05 Suriname 2.01 × 10−06\n9 UK 0.008786839 Brunei 0.000181028 Djibouti 2.45 × 10−05 Vanuatu 1.99 × 10−06\n10 South Korea 0.008072566 Czech Republic 0.000179806 Belarus 2.40 × 10−05 Albania 1.90 × 10−06\n11 Vietnam 0.007928803 Northern Mariana Islands 0.000177972 Bosnia and Herzegovina 2.40 × 10−05 Malta 1.73 × 10−06\n12 Singapore 0.007784474 Belgium 0.000175566 Cook Islands 2.26 × 10−05 Guinea 1.60 × 10−06\n13 Hong Kong 0.007636714 Maldives 0.000172453 Colombia 1.94 × 10−05 Namibia 1.55 × 10−06\n14 Indonesia 0.007197131 Norway 0.000171441 Papua New Guinea 1.88 × 10−05 Democratic Republic of Congo 1.41 × 10−06\n15 United Arab Emirates 0.007089607 Kuwait 0.000166929 Nigeria 1.85 × 10−05 Rwanda 1.22 × 10−06\n16 France 0.006814962 Egypt 0.000139373 Jordan 1.83 × 10−05 Honduras 1.02 × 10−06\n17 Turkey 0.00568808 Iran 0.000137181 Cuba 1.79 × 10−05 Gabon 8.98 × 10−07\n18 Australia 0.005671745 Mongolia 0.000132319 Argentina 1.70 × 10−05 Republic of Congo 7.22 × 10−07\n19 Russia 0.005592859 Chile 0.000129975 Tunisia 1.65 × 10−05 Bermuda 5.99 × 10−07\n20 Pakistan 0.004811284 North Korea 0.000128562 Ghana 1.61 × 10−05 Antigua and Barbuda 3.52 × 10−07\n21 Qatar 0.004113225 Mauritius 0.000126679 Armenia 1.53 × 10−05 Barbados 3.52 × 10−07\n22 Macau 0.003373727 Portugal 0.000113251 Dominican Republic 1.46 × 10−05 Cape Verde 3.52 × 10−07\n23 Germany 0.003176858 Uzbekistan 9.67 × 10−05 New Caledonia 1.34 × 10−05 Guyana 3.52 × 10−07\n24 Italy 0.002753413 Hungary 8.97 × 10−05 Cyprus 1.29 × 10−05 Madagascar 2.99 × 10−07\n25 Philippines 0.002740638 Azerbaijan 8.64 × 10−05 Bhutan 1.25 × 10−05 Grenada 2.47 × 10−07\n26 Taiwan 0.002590034 Croatia 8.57 × 10−05 Nepal 1.25 × 10−05 Bolivia 1.76 × 10−07\n27 Belize 0.002009996 Tajikistan 8.50 × 10−05 Slovenia 1.24 × 10−05 Burkina Faso 1.76 × 10−07\n28 Ethiopia 0.001469205 Bahrain 6.31 × 10−05 Moldova 1.22 × 10−05 Cameroon 1.76 × 10−07\n29 Finland 0.001307074 Fiji 6.29 × 10−05 Kosovo 1.21 × 10−05 Chad 1.76 × 10−07\n30 Sri Lanka 0.001179859 Kyrgyzstan 6.22 × 10−05 Zambia 1.20 × 10−05 Mozambique 1.76 × 10−07\n31 The Netherlands 0.000980799 Afghanistan 6.16 × 10−05 El Salvador 1.05 × 10−05 Paraguay 1.76 × 10−07\n32 New Zealand 0.000971254 Panama 5.91 × 10−05 Romania 7.56 × 10−06 Solomon Islands 1.76 × 10−07\n33 Greece 0.000958209 Morocco 5.60 × 10−05 Guatemala 6.92 × 10−06 Syria 1.76 × 10−07\n34 Bangladesh 0.000831196 Iraq 5.54 × 10−05 Angola 6.77 × 10−06 Uruguay 1.76 × 10−07\n35 Myanmar 0.000803755 Bulgaria 5.07 × 10−05 Costa Rica 6.41 × 10−06 Venezuela 1.76 × 10−07\n36 Saudi Arabia 0.000750981 Lithuania 4.99 × 10−05 Trinidad and Tobago 6.13 × 10−06 Zimbabwe 1.76 × 10−07\n37 Spain 0.000564876 Seychelles 4.82 × 10−05 Sudan 5.66 × 10−06 Libya 1.23 × 10−07\n38 Switzerland 0.00056232 Lebanon 4.47 × 10−05 Ecuador 5.56 × 10−06 Macedonia 1.23 × 10−07\n39 Austria 0.000498664 Georgia 4.37 × 10−05 Puerto Rico 4.68 × 10−06 Saint Lucia 1.23 × 10−07\n40 South Africa 0.000468876 Latvia 4.06 × 10−05 Turkmenistan 4.42 × 10−06 Sierra Leone 1.23 × 10−07\n41 Kazakhstan 0.000443175 Luxembourg 3.80 × 10−05 Mauritania 4.09 × 10−06 Somalia 1.23 × 10−07\n42 Ireland 0.000371199 Estonia 3.69 × 10−05 Timor-Leste 1.23 × 10−07\n43 Mexico 0.000322198\n44\nNumber of countries/territories: 168 Africa: 2 Africa: 3 Africa: 11 Africa: 19\nAsian: 22 Asian: 16 Asian: 4 Asian: 2\nPan-Europe:13 Pan-Europe: 18 Pan-Europe: 9 Pan-Europe: 4\nNorth America: 4 North America: 0 North America: 3 North America: 7\nOceania: 2 Oceania: 2 Oceania: 6 Oceania: 3\nSouth America: 0 South America: 3 South America: 8 South America: 7\nTotal: 43 Total: 42 Total: 41 Total: 42"}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T1","span":{"begin":1016,"end":1018},"obj":"Chemical"}],"attributes":[{"id":"A1","pred":"chebi_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/CHEBI_33417"},{"id":"A2","pred":"chebi_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/CHEBI_33418"},{"id":"A3","pred":"chebi_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/CHEBI_33517"}],"text":"Estimation of risk of transmission\nTo estimate the relative risk of 2019-nCoV transmission, we considered all infected passengers who travelled between 1 January and 31 January to possess a maximum risk of transmission 1 (and no infected passengers means no risk) and estimated the relative risk of each country based on the number of passengers who travelled from each of the four cities. Thus countries with a higher number of passengers travelling from any of these cities had a higher risk of transmission. We then weighted the risk estimated for each city with the number of reported infected people in each city by 31 January 2020 [14] and estimated the mean average risk of transmission termed as ‘Risk index’ which follows the equation below:where x is the destination country, Risk index (x) is the risk of infection importation in country x, P(x)n is the number of passengers to country x from city n, Pn is the total number of passengers who left city n, In is the number of infected people in city n and Cn is the population size of city n. The risk index denotes the risk of at least one case being imported into a country or territory where 1 means an absolute certainty and 0 means no risk at all. Our model assumed that there is no case outside China and thus ignored if any country already had imported case(s). In countries where 2019-nCoV is already detected, the risk index would explain the risk of importing additional infected individuals from China.\nWe performed a Pearson correlation coefficient test between the risk index of the country and the WHO's reported case number from the country. We grouped the countries in four quantiles based on the risk index where high-risk countries were grouped as the 4th (\u003e75th percentiles) and the 3rd (\u003e50th to ⩽75th percentiles) quantiles and low-risk countries were grouped as the 2nd (\u003e25th to ⩽50th percentiles) and the 1st (⩽25th percentile) quantiles (Table 1).\nTable 1. The list of countries or territories based on their risk index in different quantiles for 2019-nCoV (SARS-COV-2) transmission\n4th Quantile risk index (highest risk) 3rd Quantile risk index 2nd Quantile risk index 1st Quantile risk index (lowest risk)\nSl/Rank Country Risk index Country Risk index Country Risk index Country Risk index\n1 China 0.609603126 Sweden 0.000320248 Kenya 3.53 × 10−05 Jamaica 3.84 × 10−06\n2 Thailand 0.099432816 Laos 0.000296262 Peru 3.18 × 10−05 Serbia 3.22 × 10−06\n3 Cambodia 0.05294058 Brazil 0.00027186 Algeria 3.11 × 10−05 Togo 2.73 × 10−06\n4 Malaysia 0.041899039 Denmark 0.000254904 French Polynesia 3.07 × 10−05 Uganda 2.71 × 10−06\n5 Canada 0.02730388 Oman 0.000248884 Iceland 3.00 × 10−05 Tonga 2.69 × 10−06\n6 USA 0.021169936 Israel 0.000221865 Samoa 2.74 × 10−05 The Bahamas 2.39 × 10−06\n7 Japan 0.01479856 Ukraine 0.000209515 Tanzania 2.73 × 10−05 Cote d'Ivoire 2.02 × 10−06\n8 India 0.010256629 Poland 0.0002 Palau 2.63 × 10−05 Suriname 2.01 × 10−06\n9 UK 0.008786839 Brunei 0.000181028 Djibouti 2.45 × 10−05 Vanuatu 1.99 × 10−06\n10 South Korea 0.008072566 Czech Republic 0.000179806 Belarus 2.40 × 10−05 Albania 1.90 × 10−06\n11 Vietnam 0.007928803 Northern Mariana Islands 0.000177972 Bosnia and Herzegovina 2.40 × 10−05 Malta 1.73 × 10−06\n12 Singapore 0.007784474 Belgium 0.000175566 Cook Islands 2.26 × 10−05 Guinea 1.60 × 10−06\n13 Hong Kong 0.007636714 Maldives 0.000172453 Colombia 1.94 × 10−05 Namibia 1.55 × 10−06\n14 Indonesia 0.007197131 Norway 0.000171441 Papua New Guinea 1.88 × 10−05 Democratic Republic of Congo 1.41 × 10−06\n15 United Arab Emirates 0.007089607 Kuwait 0.000166929 Nigeria 1.85 × 10−05 Rwanda 1.22 × 10−06\n16 France 0.006814962 Egypt 0.000139373 Jordan 1.83 × 10−05 Honduras 1.02 × 10−06\n17 Turkey 0.00568808 Iran 0.000137181 Cuba 1.79 × 10−05 Gabon 8.98 × 10−07\n18 Australia 0.005671745 Mongolia 0.000132319 Argentina 1.70 × 10−05 Republic of Congo 7.22 × 10−07\n19 Russia 0.005592859 Chile 0.000129975 Tunisia 1.65 × 10−05 Bermuda 5.99 × 10−07\n20 Pakistan 0.004811284 North Korea 0.000128562 Ghana 1.61 × 10−05 Antigua and Barbuda 3.52 × 10−07\n21 Qatar 0.004113225 Mauritius 0.000126679 Armenia 1.53 × 10−05 Barbados 3.52 × 10−07\n22 Macau 0.003373727 Portugal 0.000113251 Dominican Republic 1.46 × 10−05 Cape Verde 3.52 × 10−07\n23 Germany 0.003176858 Uzbekistan 9.67 × 10−05 New Caledonia 1.34 × 10−05 Guyana 3.52 × 10−07\n24 Italy 0.002753413 Hungary 8.97 × 10−05 Cyprus 1.29 × 10−05 Madagascar 2.99 × 10−07\n25 Philippines 0.002740638 Azerbaijan 8.64 × 10−05 Bhutan 1.25 × 10−05 Grenada 2.47 × 10−07\n26 Taiwan 0.002590034 Croatia 8.57 × 10−05 Nepal 1.25 × 10−05 Bolivia 1.76 × 10−07\n27 Belize 0.002009996 Tajikistan 8.50 × 10−05 Slovenia 1.24 × 10−05 Burkina Faso 1.76 × 10−07\n28 Ethiopia 0.001469205 Bahrain 6.31 × 10−05 Moldova 1.22 × 10−05 Cameroon 1.76 × 10−07\n29 Finland 0.001307074 Fiji 6.29 × 10−05 Kosovo 1.21 × 10−05 Chad 1.76 × 10−07\n30 Sri Lanka 0.001179859 Kyrgyzstan 6.22 × 10−05 Zambia 1.20 × 10−05 Mozambique 1.76 × 10−07\n31 The Netherlands 0.000980799 Afghanistan 6.16 × 10−05 El Salvador 1.05 × 10−05 Paraguay 1.76 × 10−07\n32 New Zealand 0.000971254 Panama 5.91 × 10−05 Romania 7.56 × 10−06 Solomon Islands 1.76 × 10−07\n33 Greece 0.000958209 Morocco 5.60 × 10−05 Guatemala 6.92 × 10−06 Syria 1.76 × 10−07\n34 Bangladesh 0.000831196 Iraq 5.54 × 10−05 Angola 6.77 × 10−06 Uruguay 1.76 × 10−07\n35 Myanmar 0.000803755 Bulgaria 5.07 × 10−05 Costa Rica 6.41 × 10−06 Venezuela 1.76 × 10−07\n36 Saudi Arabia 0.000750981 Lithuania 4.99 × 10−05 Trinidad and Tobago 6.13 × 10−06 Zimbabwe 1.76 × 10−07\n37 Spain 0.000564876 Seychelles 4.82 × 10−05 Sudan 5.66 × 10−06 Libya 1.23 × 10−07\n38 Switzerland 0.00056232 Lebanon 4.47 × 10−05 Ecuador 5.56 × 10−06 Macedonia 1.23 × 10−07\n39 Austria 0.000498664 Georgia 4.37 × 10−05 Puerto Rico 4.68 × 10−06 Saint Lucia 1.23 × 10−07\n40 South Africa 0.000468876 Latvia 4.06 × 10−05 Turkmenistan 4.42 × 10−06 Sierra Leone 1.23 × 10−07\n41 Kazakhstan 0.000443175 Luxembourg 3.80 × 10−05 Mauritania 4.09 × 10−06 Somalia 1.23 × 10−07\n42 Ireland 0.000371199 Estonia 3.69 × 10−05 Timor-Leste 1.23 × 10−07\n43 Mexico 0.000322198\n44\nNumber of countries/territories: 168 Africa: 2 Africa: 3 Africa: 11 Africa: 19\nAsian: 22 Asian: 16 Asian: 4 Asian: 2\nPan-Europe:13 Pan-Europe: 18 Pan-Europe: 9 Pan-Europe: 4\nNorth America: 4 North America: 0 North America: 3 North America: 7\nOceania: 2 Oceania: 2 Oceania: 6 Oceania: 3\nSouth America: 0 South America: 3 South America: 8 South America: 7\nTotal: 43 Total: 42 Total: 41 Total: 42"}

LitCovid-sentences

PMC:7058650 / 6940-13604 JSONTXT

Annnotations TAB JSON ListView MergeView

LitCovid-PubTator

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-sentences

PMC:7058650 / 6940-13604 JSON TXT