PMC:7510993 / 26492-28362 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T151","span":{"begin":24,"end":32},"obj":"Disease"},{"id":"T152","span":{"begin":120,"end":128},"obj":"Disease"},{"id":"T153","span":{"begin":233,"end":241},"obj":"Disease"},{"id":"T154","span":{"begin":308,"end":316},"obj":"Disease"},{"id":"T155","span":{"begin":411,"end":419},"obj":"Disease"},{"id":"T156","span":{"begin":484,"end":492},"obj":"Disease"},{"id":"T157","span":{"begin":576,"end":584},"obj":"Disease"},{"id":"T158","span":{"begin":651,"end":659},"obj":"Disease"},{"id":"T159","span":{"begin":758,"end":766},"obj":"Disease"},{"id":"T160","span":{"begin":831,"end":839},"obj":"Disease"},{"id":"T161","span":{"begin":1687,"end":1694},"obj":"Disease"}],"attributes":[{"id":"A151","pred":"mondo_id","subj":"T151","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A152","pred":"mondo_id","subj":"T152","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A153","pred":"mondo_id","subj":"T153","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A154","pred":"mondo_id","subj":"T154","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A155","pred":"mondo_id","subj":"T155","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A156","pred":"mondo_id","subj":"T156","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A157","pred":"mondo_id","subj":"T157","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A158","pred":"mondo_id","subj":"T158","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A159","pred":"mondo_id","subj":"T159","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A160","pred":"mondo_id","subj":"T160","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A161","pred":"mondo_id","subj":"T161","obj":"http://purl.obolibrary.org/obo/MONDO_0005136"}],"text":"Table 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T123","span":{"begin":187,"end":188},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T124","span":{"begin":209,"end":210},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T125","span":{"begin":354,"end":355},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T126","span":{"begin":552,"end":553},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T127","span":{"begin":946,"end":952},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T128","span":{"begin":956,"end":957},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T129","span":{"begin":991,"end":995},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T130","span":{"begin":1109,"end":1110},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T131","span":{"begin":1117,"end":1118},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T132","span":{"begin":1173,"end":1174},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T133","span":{"begin":1189,"end":1190},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T134","span":{"begin":1261,"end":1262},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T135","span":{"begin":1275,"end":1276},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T136","span":{"begin":1290,"end":1291},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T137","span":{"begin":1305,"end":1306},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T138","span":{"begin":1372,"end":1375},"obj":"http://purl.obolibrary.org/obo/CLO_0001178"},{"id":"T139","span":{"begin":1372,"end":1375},"obj":"http://purl.obolibrary.org/obo/CLO_0052433"},{"id":"T140","span":{"begin":1420,"end":1421},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T141","span":{"begin":1436,"end":1437},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T142","span":{"begin":1452,"end":1453},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T143","span":{"begin":1468,"end":1469},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T144","span":{"begin":1501,"end":1502},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T145","span":{"begin":1519,"end":1520},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T146","span":{"begin":1537,"end":1538},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T147","span":{"begin":1593,"end":1594},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T148","span":{"begin":1610,"end":1611},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T149","span":{"begin":1627,"end":1628},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T150","span":{"begin":1644,"end":1645},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T151","span":{"begin":1710,"end":1711},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T152","span":{"begin":1727,"end":1728},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T153","span":{"begin":1744,"end":1745},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T154","span":{"begin":1762,"end":1763},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T155","span":{"begin":1822,"end":1823},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T156","span":{"begin":1838,"end":1839},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"}],"text":"Table 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d"}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T69","span":{"begin":1470,"end":1473},"obj":"Chemical"},{"id":"T71","span":{"begin":1555,"end":1558},"obj":"Chemical"}],"attributes":[{"id":"A69","pred":"chebi_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/CHEBI_17552"},{"id":"A70","pred":"chebi_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/CHEBI_58189"},{"id":"A71","pred":"chebi_id","subj":"T71","obj":"http://purl.obolibrary.org/obo/CHEBI_41001"}],"text":"Table 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d"}

{"project":"LitCovid-PubTator","denotations":[{"id":"393","span":{"begin":1668,"end":1674},"obj":"Species"},{"id":"394","span":{"begin":1786,"end":1792},"obj":"Species"},{"id":"395","span":{"begin":1555,"end":1558},"obj":"Species"},{"id":"396","span":{"begin":1675,"end":1683},"obj":"Disease"},{"id":"397","span":{"begin":1687,"end":1694},"obj":"Disease"},{"id":"399","span":{"begin":24,"end":32},"obj":"Disease"},{"id":"409","span":{"begin":120,"end":128},"obj":"Disease"},{"id":"410","span":{"begin":233,"end":241},"obj":"Disease"},{"id":"411","span":{"begin":308,"end":316},"obj":"Disease"},{"id":"412","span":{"begin":411,"end":419},"obj":"Disease"},{"id":"413","span":{"begin":484,"end":492},"obj":"Disease"},{"id":"414","span":{"begin":576,"end":584},"obj":"Disease"},{"id":"415","span":{"begin":651,"end":659},"obj":"Disease"},{"id":"416","span":{"begin":758,"end":766},"obj":"Disease"},{"id":"417","span":{"begin":831,"end":839},"obj":"Disease"}],"attributes":[{"id":"A393","pred":"tao:has_database_id","subj":"393","obj":"Tax:9606"},{"id":"A394","pred":"tao:has_database_id","subj":"394","obj":"Tax:9606"},{"id":"A395","pred":"tao:has_database_id","subj":"395","obj":"Tax:33892"},{"id":"A396","pred":"tao:has_database_id","subj":"396","obj":"MESH:D007239"},{"id":"A397","pred":"tao:has_database_id","subj":"397","obj":"MESH:D008288"},{"id":"A399","pred":"tao:has_database_id","subj":"399","obj":"MESH:C000657245"},{"id":"A409","pred":"tao:has_database_id","subj":"409","obj":"MESH:C000657245"},{"id":"A410","pred":"tao:has_database_id","subj":"410","obj":"MESH:C000657245"},{"id":"A411","pred":"tao:has_database_id","subj":"411","obj":"MESH:C000657245"},{"id":"A412","pred":"tao:has_database_id","subj":"412","obj":"MESH:C000657245"},{"id":"A413","pred":"tao:has_database_id","subj":"413","obj":"MESH:C000657245"},{"id":"A414","pred":"tao:has_database_id","subj":"414","obj":"MESH:C000657245"},{"id":"A415","pred":"tao:has_database_id","subj":"415","obj":"MESH:C000657245"},{"id":"A416","pred":"tao:has_database_id","subj":"416","obj":"MESH:C000657245"},{"id":"A417","pred":"tao:has_database_id","subj":"417","obj":"MESH:C000657245"}],"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":"Table 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d"}

{"project":"LitCovid-sentences","denotations":[{"id":"T133","span":{"begin":0,"end":73},"obj":"Sentence"},{"id":"T134","span":{"begin":74,"end":181},"obj":"Sentence"},{"id":"T135","span":{"begin":182,"end":871},"obj":"Sentence"},{"id":"T136","span":{"begin":872,"end":996},"obj":"Sentence"},{"id":"T137","span":{"begin":997,"end":1095},"obj":"Sentence"},{"id":"T138","span":{"begin":1096,"end":1134},"obj":"Sentence"},{"id":"T139","span":{"begin":1135,"end":1222},"obj":"Sentence"},{"id":"T140","span":{"begin":1223,"end":1306},"obj":"Sentence"},{"id":"T141","span":{"begin":1307,"end":1376},"obj":"Sentence"},{"id":"T142","span":{"begin":1377,"end":1469},"obj":"Sentence"},{"id":"T143","span":{"begin":1470,"end":1554},"obj":"Sentence"},{"id":"T144","span":{"begin":1555,"end":1645},"obj":"Sentence"},{"id":"T145","span":{"begin":1646,"end":1763},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Table 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d"}