PMC:7210464 / 47081-48508 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T12","span":{"begin":612,"end":616},"obj":"Body_part"}],"attributes":[{"id":"A12","pred":"fma_id","subj":"T12","obj":"http://purl.org/sig/ont/fma/fma25056"}],"text":"Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week. Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk. A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T105","span":{"begin":505,"end":513},"obj":"Disease"},{"id":"T106","span":{"begin":1161,"end":1170},"obj":"Disease"},{"id":"T107","span":{"begin":1267,"end":1276},"obj":"Disease"},{"id":"T108","span":{"begin":1357,"end":1366},"obj":"Disease"}],"attributes":[{"id":"A105","pred":"mondo_id","subj":"T105","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A106","pred":"mondo_id","subj":"T106","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A107","pred":"mondo_id","subj":"T107","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A108","pred":"mondo_id","subj":"T108","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"}],"text":"Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week. Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk. A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T203","span":{"begin":602,"end":603},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T204","span":{"begin":610,"end":611},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week. Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk. A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17."}

{"project":"LitCovid-sentences","denotations":[{"id":"T407","span":{"begin":0,"end":161},"obj":"Sentence"},{"id":"T408","span":{"begin":162,"end":302},"obj":"Sentence"},{"id":"T409","span":{"begin":303,"end":609},"obj":"Sentence"},{"id":"T410","span":{"begin":610,"end":855},"obj":"Sentence"},{"id":"T411","span":{"begin":856,"end":949},"obj":"Sentence"},{"id":"T412","span":{"begin":950,"end":1052},"obj":"Sentence"},{"id":"T413","span":{"begin":1053,"end":1148},"obj":"Sentence"},{"id":"T414","span":{"begin":1149,"end":1427},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week. Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk. A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17."}

{"project":"LitCovid-PubTator","denotations":[{"id":"260","span":{"begin":448,"end":454},"obj":"Species"},{"id":"261","span":{"begin":983,"end":989},"obj":"Species"},{"id":"262","span":{"begin":505,"end":513},"obj":"Disease"},{"id":"263","span":{"begin":1161,"end":1170},"obj":"Disease"},{"id":"264","span":{"begin":1267,"end":1276},"obj":"Disease"},{"id":"265","span":{"begin":1357,"end":1366},"obj":"Disease"}],"attributes":[{"id":"A260","pred":"tao:has_database_id","subj":"260","obj":"Tax:9606"},{"id":"A261","pred":"tao:has_database_id","subj":"261","obj":"Tax:9606"},{"id":"A262","pred":"tao:has_database_id","subj":"262","obj":"MESH:C000657245"},{"id":"A263","pred":"tao:has_database_id","subj":"263","obj":"MESH:D007239"},{"id":"A264","pred":"tao:has_database_id","subj":"264","obj":"MESH:D007239"},{"id":"A265","pred":"tao:has_database_id","subj":"265","obj":"MESH:D007239"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week. Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk. A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17."}