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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 5311-5319 Body_part denotes Appendix http://purl.org/sig/ont/fma/fma14542
T2 6341-6346 Body_part denotes cells http://purl.org/sig/ont/fma/fma68646
T3 8404-8408 Body_part denotes axis http://purl.org/sig/ont/fma/fma12520
T4 8453-8457 Body_part denotes axis http://purl.org/sig/ont/fma/fma12520
T5 14127-14130 Body_part denotes map http://purl.org/sig/ont/fma/fma67847
T6 16101-16104 Body_part denotes map http://purl.org/sig/ont/fma/fma67847
T7 17420-17424 Body_part denotes dens http://purl.org/sig/ont/fma/fma24043
T8 17603-17607 Body_part denotes axis http://purl.org/sig/ont/fma/fma12520
T9 26086-26089 Body_part denotes map http://purl.org/sig/ont/fma/fma67847
T10 32108-32112 Body_part denotes dens http://purl.org/sig/ont/fma/fma24043
T11 32291-32295 Body_part denotes axis http://purl.org/sig/ont/fma/fma12520
T12 32766-32770 Body_part denotes dens http://purl.org/sig/ont/fma/fma24043
T13 32949-32953 Body_part denotes axis http://purl.org/sig/ont/fma/fma12520
T14 33257-33265 Body_part denotes Appendix http://purl.org/sig/ont/fma/fma14542

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 2855-2860 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T2 25020-25025 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T3 25322-25327 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T4 25439-25444 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T5 25587-25592 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 24-32 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 129-137 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T3 206-230 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T4 232-240 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 363-371 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 413-420 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T7 590-598 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 776-784 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 848-856 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 905-913 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T11 956-958 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T12 1065-1073 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T13 1111-1119 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T14 1227-1235 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T15 1390-1398 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T16 1588-1596 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T17 1707-1715 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T18 1784-1792 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T19 1845-1853 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T20 1904-1912 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T21 2032-2042 Disease denotes infectious http://purl.obolibrary.org/obo/MONDO_0005550
T22 2329-2353 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T23 2355-2363 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 2425-2433 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T25 2528-2536 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T26 2795-2803 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T27 2826-2835 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T28 2885-2893 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T29 3114-3124 Disease denotes Infectious http://purl.obolibrary.org/obo/MONDO_0005550
T30 3491-3499 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T31 3577-3585 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T32 3825-3833 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T33 3966-3974 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T34 4046-4054 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T35 4136-4144 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T36 4300-4308 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T37 4377-4385 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T38 4681-4689 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T39 4710-4718 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T40 4760-4767 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T41 4925-4933 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T42 5026-5034 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 5151-5159 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T44 5239-5247 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T45 5355-5363 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 5439-5447 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T47 5564-5572 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T48 5780-5788 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T49 5884-5892 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T50 6827-6835 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T51 6961-6968 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T52 7269-7277 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T53 7343-7350 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T54 8168-8180 Disease denotes tuberculosis http://purl.obolibrary.org/obo/MONDO_0018076
T55 8209-8211 Disease denotes TB http://purl.obolibrary.org/obo/MONDO_0018076
T56 8376-8378 Disease denotes TB http://purl.obolibrary.org/obo/MONDO_0018076
T57 9082-9090 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T58 9290-9298 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T59 9433-9441 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T60 9636-9644 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T61 9721-9729 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T62 9945-9953 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T63 10126-10134 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T64 10474-10482 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T65 10578-10585 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T66 10780-10788 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T67 10835-10843 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T68 11290-11292 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T69 13229-13237 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T70 13587-13595 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T71 13674-13682 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T72 13752-13760 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T73 13885-13893 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T74 14073-14081 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T75 14252-14260 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T76 14454-14456 Disease denotes TS http://purl.obolibrary.org/obo/MONDO_0010979|http://purl.obolibrary.org/obo/MONDO_0016455
T78 14579-14581 Disease denotes WS http://purl.obolibrary.org/obo/MONDO_0010196
T79 14699-14707 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T80 14874-14882 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T81 15062-15070 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T82 15205-15213 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T83 15450-15458 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T84 15525-15533 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T85 15632-15640 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T86 15705-15713 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T87 15801-15809 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T88 15876-15884 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T89 15983-15991 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T90 16056-16064 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T91 16212-16220 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T92 16356-16364 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T93 16586-16594 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T94 16657-16665 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T95 16864-16872 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T96 17107-17115 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T97 17701-17708 Disease denotes Malaria http://purl.obolibrary.org/obo/MONDO_0005136
T98 17719-17726 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T99 18093-18095 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T100 18413-18415 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T101 18438-18446 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T102 18793-18801 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T103 18963-18971 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T104 19276-19278 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T105 19340-19348 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T106 19992-20000 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T107 20256-20264 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T108 20363-20370 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T109 20451-20459 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T110 20567-20575 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T111 20854-20862 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T112 20960-20968 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T113 21052-21060 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T114 21169-21177 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T115 21314-21322 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T116 21541-21549 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T117 21829-21837 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T118 21887-21894 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T119 22127-22135 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T120 22340-22348 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T121 22560-22568 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T122 22768-22776 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T123 22819-22827 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T124 22969-22977 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T125 23099-23107 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T126 23286-23294 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T127 23367-23374 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T128 23394-23402 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T129 23565-23573 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T130 23717-23725 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T131 23836-23844 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T132 23932-23939 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T133 24076-24084 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T134 24311-24319 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T135 24464-24472 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T136 24538-24546 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T137 24598-24606 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T138 24635-24643 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T139 24936-24944 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T140 25069-25078 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T141 25271-25279 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T142 25392-25400 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T143 25453-25463 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T144 25658-25666 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T145 25732-25740 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T146 25756-25764 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T147 25962-25970 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T148 26259-26269 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T149 26295-26303 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T150 26423-26431 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T151 26516-26524 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T152 26612-26620 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T153 26725-26733 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T154 26800-26808 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T155 26903-26911 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T156 26976-26984 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T157 27068-27076 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T158 27143-27151 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T159 27250-27258 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T160 27323-27331 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T161 28179-28186 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T162 28406-28414 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T163 28446-28455 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T164 28646-28654 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T165 28686-28695 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T166 28831-28839 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T167 28871-28880 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T168 29079-29087 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T169 29119-29128 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T170 29267-29274 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T171 29361-29369 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T172 29533-29541 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T173 29657-29665 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T174 29723-29731 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T175 29732-29741 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T176 29950-29958 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T177 30178-30186 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T178 30353-30361 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T179 30472-30480 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T180 30549-30557 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T181 30875-30883 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T182 30959-30967 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T183 31034-31042 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T184 31137-31145 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T185 31210-31218 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T186 31305-31313 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T187 31380-31388 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T188 31487-31495 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T189 31560-31568 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T190 31629-31631 Disease denotes TS http://purl.obolibrary.org/obo/MONDO_0010979|http://purl.obolibrary.org/obo/MONDO_0016455
T192 31754-31756 Disease denotes WS http://purl.obolibrary.org/obo/MONDO_0010196
T193 32030-32038 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T194 32389-32396 Disease denotes Malaria http://purl.obolibrary.org/obo/MONDO_0005136
T195 32407-32414 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T196 32642-32650 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T197 33047-33054 Disease denotes Malaria http://purl.obolibrary.org/obo/MONDO_0005136
T198 33065-33072 Disease denotes malaria http://purl.obolibrary.org/obo/MONDO_0005136
T199 33296-33304 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 490-495 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T2 1431-1436 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T3 1671-1676 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T4 1874-1875 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T5 2194-2199 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T6 2287-2292 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T7 2438-2441 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T8 3153-3160 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T9 3194-3195 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 3247-3248 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T11 3298-3303 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T12 3380-3387 http://purl.obolibrary.org/obo/UBERON_0000982 denotes jointly
T13 3380-3387 http://purl.obolibrary.org/obo/UBERON_0004905 denotes jointly
T14 3404-3409 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T15 3460-3465 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T16 3469-3474 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T17 3637-3644 http://purl.obolibrary.org/obo/CLO_0009985 denotes focused
T18 3758-3763 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T19 3773-3775 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T20 4315-4320 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T21 4940-4945 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T22 5308-5310 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T23 5324-5325 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T24 5579-5584 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T25 5639-5651 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T26 5692-5699 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T27 6304-6305 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T28 6341-6346 http://purl.obolibrary.org/obo/GO_0005623 denotes cells
T29 6362-6363 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T30 6593-6605 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T31 7356-7358 http://purl.obolibrary.org/obo/CLO_0037161 denotes en
T32 7417-7419 http://purl.obolibrary.org/obo/CLO_0037161 denotes en
T33 9660-9665 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T34 9867-9868 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T35 10148-10149 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T36 10382-10387 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T37 10844-10849 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T38 10864-10865 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T39 11006-11007 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T40 11177-11178 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T41 11370-11371 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T42 11616-11620 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T43 11678-11679 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T44 11697-11698 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T45 12296-12302 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T46 12577-12584 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T47 12710-12715 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T48 12777-12778 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T49 12891-12898 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T50 12982-12989 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T51 13124-13126 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T52 13391-13393 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T53 13391-13393 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T54 13415-13417 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T55 13529-13531 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T56 13529-13531 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T57 13707-13709 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T58 13834-13835 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T59 13921-13922 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T60 13944-13945 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T61 14175-14176 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T62 14493-14495 http://purl.obolibrary.org/obo/CLO_0009287 denotes TE
T63 14657-14659 http://purl.obolibrary.org/obo/CLO_0009445 denotes TU
T64 14823-14824 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T65 14910-14911 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T66 14933-14934 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T67 15389-15390 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T68 15391-15397 http://www.ebi.ac.uk/efo/EFO_0000265 denotes D): (A
T69 15404-15405 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T70 15426-15427 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T71 15567-15568 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T72 15575-15576 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T73 15777-15778 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T74 16149-16150 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T75 16821-16826 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T76 17151-17152 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T77 17218-17219 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T78 17241-17242 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T79 18134-18135 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T80 18346-18348 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T81 18346-18348 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T82 18750-18755 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T83 18921-18926 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T84 19028-19033 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T85 19121-19126 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T86 19421-19422 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T87 19487-19488 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T88 20710-20711 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T89 21726-21728 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T90 21726-21728 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T91 21787-21792 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T92 22136-22141 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T93 22145-22146 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T94 22297-22302 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T95 22461-22468 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T96 22700-22705 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T97 22716-22718 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T98 22782-22789 http://purl.obolibrary.org/obo/UBERON_0000982 denotes jointly
T99 22782-22789 http://purl.obolibrary.org/obo/UBERON_0004905 denotes jointly
T100 22940-22941 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T101 23009-23014 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T102 23031-23034 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T103 23147-23148 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T104 23527-23528 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T105 23663-23664 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T106 23895-23896 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T107 24043-24044 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T108 24110-24111 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T109 24158-24164 http://purl.obolibrary.org/obo/NCBITaxon_9005 denotes Turkey
T110 24279-24284 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T111 24496-24501 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T112 24680-24682 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T113 24680-24682 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T114 24778-24780 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T115 24811-24814 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T116 24822-24823 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T117 25205-25208 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T118 25767-25770 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T119 26134-26135 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T120 26199-26200 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T121 26399-26401 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T122 26454-26455 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T123 26679-26680 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T124 26701-26702 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T125 26846-26847 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T126 27044-27045 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T127 27438-27444 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T128 27448-27449 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T129 27483-27487 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T130 27601-27602 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T131 27609-27610 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T132 27665-27666 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T133 27681-27682 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T134 27753-27754 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T135 27767-27768 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T136 27782-27783 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T137 27797-27798 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T138 27864-27867 http://purl.obolibrary.org/obo/CLO_0001178 denotes 243
T139 27864-27867 http://purl.obolibrary.org/obo/CLO_0052433 denotes 243
T140 27912-27913 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T141 27928-27929 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T142 27944-27945 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T143 27960-27961 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T144 27993-27994 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T145 28011-28012 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T146 28029-28030 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T147 28085-28086 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T148 28102-28103 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T149 28119-28120 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T150 28136-28137 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T151 28202-28203 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T152 28219-28220 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T153 28236-28237 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T154 28254-28255 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T155 28314-28315 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T156 28330-28331 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T157 28372-28373 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T158 28551-28556 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T159 28612-28613 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T160 29594-29595 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T161 29645-29652 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T162 29765-29766 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T163 29802-29810 http://purl.obolibrary.org/obo/CLO_0009985 denotes focusing
T164 29877-29878 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T165 29933-29937 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T166 29989-29994 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T167 30134-30141 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T168 30436-30441 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T169 30633-30634 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T170 30804-30806 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T171 30913-30914 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T172 30935-30936 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T173 31080-31081 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T174 31278-31279 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T175 31668-31670 http://purl.obolibrary.org/obo/CLO_0009287 denotes TE
T176 31832-31834 http://purl.obolibrary.org/obo/CLO_0009445 denotes TU
T177 31887-31889 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T178 31887-31889 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T179 32651-32656 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T180 32678-32679 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T181 33188-33192 http://purl.obolibrary.org/obo/UBERON_0000473 denotes Test
T182 33204-33209 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T183 33254-33256 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T184 33364-33366 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T185 33400-33402 http://purl.obolibrary.org/obo/CLO_0001382 denotes 48
T186 33447-33449 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T187 33447-33449 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 388-391 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T2 4735-4738 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T3 7043-7046 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T4 7136-7139 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T5 7300-7303 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T6 7356-7358 Chemical denotes en http://purl.obolibrary.org/obo/CHEBI_30347
T7 7417-7419 Chemical denotes en http://purl.obolibrary.org/obo/CHEBI_30347
T8 7429-7432 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T9 7534-7537 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T10 7610-7613 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T11 7801-7804 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T12 7884-7887 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T13 7984-7987 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T14 8020-8023 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T15 8066-8069 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T16 8141-8144 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T17 8220-8223 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T18 8416-8419 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T20 8447-8450 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T22 8516-8519 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T24 8564-8567 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T25 8828-8831 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T27 8852-8855 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T29 10351-10354 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T31 10595-10598 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T32 13391-13393 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T33 13529-13531 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T34 14113-14115 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388
T35 14427-14429 Chemical denotes TR http://purl.obolibrary.org/obo/CHEBI_74825
T36 14454-14456 Chemical denotes TS http://purl.obolibrary.org/obo/CHEBI_73664
T37 14493-14495 Chemical denotes TE http://purl.obolibrary.org/obo/CHEBI_74857
T38 14521-14523 Chemical denotes SD http://purl.obolibrary.org/obo/CHEBI_74807
T39 14546-14548 Chemical denotes TD http://purl.obolibrary.org/obo/CHEBI_74854
T40 14579-14581 Chemical denotes WS http://purl.obolibrary.org/obo/CHEBI_73694
T41 14604-14606 Chemical denotes TG http://purl.obolibrary.org/obo/CHEBI_74859|http://purl.obolibrary.org/obo/CHEBI_9555
T43 14637-14639 Chemical denotes BF http://purl.obolibrary.org/obo/CHEBI_34565
T44 15150-15152 Chemical denotes S4 http://purl.obolibrary.org/obo/CHEBI_29401
T45 17507-17510 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T47 17547-17550 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T48 17552-17555 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T49 17599-17602 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T51 17646-17649 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T52 18346-18348 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T53 18669-18671 Chemical denotes 5B http://purl.obolibrary.org/obo/CHEBI_27560
T54 18852-18854 Chemical denotes 5B http://purl.obolibrary.org/obo/CHEBI_27560
T55 20091-20093 Chemical denotes 6C http://purl.obolibrary.org/obo/CHEBI_27594
T56 20170-20173 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T58 20335-20338 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T59 21726-21728 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T60 21859-21862 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T61 22246-22248 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388
T62 23342-23345 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T63 23449-23453 Chemical denotes drug http://purl.obolibrary.org/obo/CHEBI_23888
T64 23697-23700 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T65 23978-23981 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T66 24112-24115 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T67 24680-24682 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T68 24687-24689 Chemical denotes S4 http://purl.obolibrary.org/obo/CHEBI_29401
T69 27962-27965 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T71 28047-28050 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T72 28762-28765 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T73 29019-29022 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T74 29295-29298 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T75 30286-30294 Chemical denotes carriers http://purl.obolibrary.org/obo/CHEBI_78059
T76 30708-30719 Chemical denotes application http://purl.obolibrary.org/obo/CHEBI_33232
T77 31602-31604 Chemical denotes TR http://purl.obolibrary.org/obo/CHEBI_74825
T78 31629-31631 Chemical denotes TS http://purl.obolibrary.org/obo/CHEBI_73664
T79 31668-31670 Chemical denotes TE http://purl.obolibrary.org/obo/CHEBI_74857
T80 31696-31698 Chemical denotes SD http://purl.obolibrary.org/obo/CHEBI_74807
T81 31721-31723 Chemical denotes TD http://purl.obolibrary.org/obo/CHEBI_74854
T82 31754-31756 Chemical denotes WS http://purl.obolibrary.org/obo/CHEBI_73694
T83 31779-31781 Chemical denotes TG http://purl.obolibrary.org/obo/CHEBI_74859|http://purl.obolibrary.org/obo/CHEBI_9555
T85 31812-31814 Chemical denotes BF http://purl.obolibrary.org/obo/CHEBI_34565
T86 31887-31889 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T87 32195-32198 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T89 32235-32238 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T90 32240-32243 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T91 32287-32290 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T93 32334-32337 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T94 32573-32575 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388
T95 32853-32856 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T97 32893-32896 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T98 32898-32901 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T99 32945-32948 Chemical denotes PCA http://purl.obolibrary.org/obo/CHEBI_36751|http://purl.obolibrary.org/obo/CHEBI_62248
T101 32992-32995 Chemical denotes BCG http://purl.obolibrary.org/obo/CHEBI_41001
T102 33447-33449 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T103 33530-33532 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388
T104 33613-33615 Chemical denotes S4 http://purl.obolibrary.org/obo/CHEBI_29401

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 24-32 Disease denotes COVID-19 MESH:C000657245
23 490-495 Species denotes human Tax:9606
24 1431-1436 Species denotes human Tax:9606
25 1671-1676 Species denotes human Tax:9606
26 388-391 Species denotes BCG Tax:33892
27 206-230 Disease denotes coronavirus disease 2019 MESH:C000657245
28 232-240 Disease denotes COVID-19 MESH:C000657245
29 363-371 Disease denotes COVID-19 MESH:C000657245
30 413-420 Disease denotes malaria MESH:D008288
31 590-598 Disease denotes COVID-19 MESH:C000657245
32 776-784 Disease denotes COVID-19 MESH:C000657245
33 848-856 Disease denotes COVID-19 MESH:C000657245
34 905-913 Disease denotes COVID-19 MESH:C000657245
35 1065-1073 Disease denotes COVID-19 MESH:C000657245
36 1111-1119 Disease denotes COVID-19 MESH:C000657245
37 1227-1235 Disease denotes COVID-19 MESH:C000657245
38 1390-1398 Disease denotes COVID-19 MESH:C000657245
39 1588-1596 Disease denotes COVID-19 MESH:C000657245
40 1707-1715 Disease denotes COVID-19 MESH:C000657245
41 1784-1792 Disease denotes COVID-19 MESH:C000657245
42 1845-1853 Disease denotes COVID-19 MESH:C000657245
43 1904-1912 Disease denotes COVID-19 MESH:C000657245
54 2287-2292 Species denotes human Tax:9606
55 2383-2423 Species denotes acute respiratory syndrome coronavirus 2 Tax:2697049
56 2425-2435 Species denotes SARS-CoV-2 Tax:2697049
57 2032-2051 Disease denotes infectious diseases MESH:D003141
58 2329-2353 Disease denotes coronavirus disease 2019 MESH:C000657245
59 2355-2363 Disease denotes COVID-19 MESH:C000657245
60 2528-2536 Disease denotes COVID-19 MESH:C000657245
61 2795-2803 Disease denotes COVID-19 MESH:C000657245
62 2826-2835 Disease denotes infection MESH:D007239
63 2885-2893 Disease denotes COVID-19 MESH:C000657245
72 3460-3465 Species denotes human Tax:9606
73 3469-3474 Species denotes human Tax:9606
74 3758-3763 Species denotes human Tax:9606
75 3114-3133 Disease denotes Infectious diseases MESH:D003141
76 3491-3499 Disease denotes COVID-19 MESH:C000657245
77 3577-3585 Disease denotes COVID-19 MESH:C000657245
78 3825-3833 Disease denotes COVID-19 MESH:C000657245
79 3966-3974 Disease denotes COVID-19 MESH:C000657245
91 4300-4310 Species denotes SARS-CoV-2 Tax:2697049
92 4681-4691 Species denotes SARS-CoV-2 Tax:2697049
93 4940-4945 Species denotes human Tax:9606
94 4735-4738 Species denotes BCG Tax:33892
95 4046-4054 Disease denotes COVID-19 MESH:C000657245
96 4136-4144 Disease denotes COVID-19 MESH:C000657245
97 4377-4385 Disease denotes COVID-19 MESH:C000657245
98 4710-4718 Disease denotes COVID-19 MESH:C000657245
99 4760-4767 Disease denotes malaria MESH:D008288
100 4925-4933 Disease denotes COVID-19 MESH:C000657245
101 5026-5034 Disease denotes COVID-19 MESH:C000657245
108 5564-5574 Species denotes SARS-CoV-2 Tax:2697049
109 5151-5159 Disease denotes COVID-19 MESH:C000657245
110 5239-5247 Disease denotes COVID-19 MESH:C000657245
111 5355-5363 Disease denotes COVID-19 MESH:C000657245
112 5439-5447 Disease denotes COVID-19 MESH:C000657245
113 5780-5788 Disease denotes COVID-19 MESH:C000657245
115 5884-5892 Disease denotes COVID-19 MESH:C000657245
138 7017-7041 Species denotes bacillus Calmette–Guérin Tax:33892
139 7821-7827 Species denotes people Tax:9606
140 8201-8207 Species denotes people Tax:9606
141 7043-7046 Species denotes BCG Tax:33892
142 7136-7139 Species denotes BCG Tax:33892
143 7300-7303 Species denotes BCG Tax:33892
144 7429-7432 Species denotes BCG Tax:33892
145 7534-7537 Species denotes BCG Tax:33892
146 7610-7613 Species denotes BCG Tax:33892
147 7801-7804 Species denotes BCG Tax:33892
148 7884-7887 Species denotes BCG Tax:33892
149 7984-7987 Species denotes BCG Tax:33892
150 8020-8023 Species denotes BCG Tax:33892
151 8066-8069 Species denotes BCG Tax:33892
152 8141-8144 Species denotes BCG Tax:33892
153 8220-8223 Species denotes BCG Tax:33892
154 8564-8567 Species denotes BCG Tax:33892
155 6827-6835 Disease denotes COVID-19 MESH:C000657245
156 6961-6968 Disease denotes malaria MESH:D008288
157 7269-7277 Disease denotes COVID-19 MESH:C000657245
158 7343-7350 Disease denotes malaria MESH:D008288
159 8168-8180 Disease denotes tuberculosis MESH:D014376
163 9082-9090 Disease denotes COVID-19 MESH:C000657245
164 9290-9298 Disease denotes COVID-19 MESH:C000657245
165 9433-9441 Disease denotes COVID-19 MESH:C000657245
170 9660-9665 Species denotes human Tax:9606
171 9636-9644 Disease denotes COVID-19 MESH:C000657245
172 9721-9729 Disease denotes COVID-19 MESH:C000657245
173 9945-9953 Disease denotes COVID-19 MESH:C000657245
181 10382-10387 Species denotes human Tax:9606
182 10517-10523 Species denotes people Tax:9606
183 10595-10598 Species denotes BCG Tax:33892
184 10351-10354 Chemical denotes GDP MESH:D006153
185 10126-10134 Disease denotes COVID-19 MESH:C000657245
186 10474-10482 Disease denotes COVID-19 MESH:C000657245
187 10578-10585 Disease denotes malaria MESH:D008288
190 10780-10788 Disease denotes COVID-19 MESH:C000657245
191 10835-10843 Disease denotes COVID-19 MESH:C000657245
195 13229-13237 Disease denotes COVID-19 MESH:C000657245
196 13587-13595 Disease denotes COVID-19 MESH:C000657245
197 13674-13682 Disease denotes COVID-19 MESH:C000657245
199 13752-13760 Disease denotes COVID-19 MESH:C000657245
202 13885-13893 Disease denotes COVID-19 MESH:C000657245
203 14073-14081 Disease denotes COVID-19 MESH:C000657245
205 14252-14260 Disease denotes COVID-19 MESH:C000657245
210 14427-14429 Gene denotes TR Gene:2149
211 14699-14707 Disease denotes COVID-19 MESH:C000657245
212 14874-14882 Disease denotes COVID-19 MESH:C000657245
213 15062-15070 Disease denotes COVID-19 MESH:C000657245
215 15205-15213 Disease denotes COVID-19 MESH:C000657245
224 15450-15458 Disease denotes COVID-19 MESH:C000657245
225 15525-15533 Disease denotes COVID-19 MESH:C000657245
226 15632-15640 Disease denotes COVID-19 MESH:C000657245
227 15705-15713 Disease denotes COVID-19 MESH:C000657245
228 15801-15809 Disease denotes COVID-19 MESH:C000657245
229 15876-15884 Disease denotes COVID-19 MESH:C000657245
230 15983-15991 Disease denotes COVID-19 MESH:C000657245
231 16056-16064 Disease denotes COVID-19 MESH:C000657245
238 16821-16826 Species denotes human Tax:9606
239 16212-16220 Disease denotes COVID-19 MESH:C000657245
240 16356-16364 Disease denotes COVID-19 MESH:C000657245
241 16586-16594 Disease denotes COVID-19 MESH:C000657245
242 16657-16665 Disease denotes COVID-19 MESH:C000657245
243 16864-16872 Disease denotes COVID-19 MESH:C000657245
245 17107-17115 Disease denotes COVID-19 MESH:C000657245
251 17547-17550 Species denotes BCG Tax:33892
252 17552-17555 Species denotes BCG Tax:33892
253 17646-17649 Species denotes BCG Tax:33892
254 17701-17708 Disease denotes Malaria MESH:D008288
255 17719-17726 Disease denotes malaria MESH:D008288
262 18750-18755 Species denotes human Tax:9606
263 18921-18926 Species denotes human Tax:9606
264 19028-19033 Species denotes human Tax:9606
265 18438-18446 Disease denotes COVID-19 MESH:C000657245
266 18793-18801 Disease denotes COVID-19 MESH:C000657245
267 18963-18971 Disease denotes COVID-19 MESH:C000657245
269 19340-19348 Disease denotes COVID-19 MESH:C000657245
281 20335-20338 Species denotes BCG Tax:33892
282 20170-20173 Chemical denotes GDP MESH:D006153
283 19992-20000 Disease denotes COVID-19 MESH:C000657245
284 20256-20264 Disease denotes COVID-19 MESH:C000657245
285 20363-20370 Disease denotes malaria MESH:D008288
286 20451-20459 Disease denotes COVID-19 MESH:C000657245
287 20567-20575 Disease denotes COVID-19 MESH:C000657245
288 20854-20862 Disease denotes COVID-19 MESH:C000657245
289 20960-20968 Disease denotes COVID-19 MESH:C000657245
290 21052-21060 Disease denotes COVID-19 MESH:C000657245
291 21169-21177 Disease denotes COVID-19 MESH:C000657245
293 21314-21322 Disease denotes COVID-19 MESH:C000657245
295 21541-21549 Disease denotes COVID-19 MESH:C000657245
304 21787-21792 Species denotes human Tax:9606
305 22297-22302 Species denotes human Tax:9606
306 21859-21862 Species denotes BCG Tax:33892
307 21829-21837 Disease denotes COVID-19 MESH:C000657245
308 21887-21894 Disease denotes malaria MESH:D008288
309 22127-22135 Disease denotes COVID-19 MESH:C000657245
310 22340-22348 Disease denotes COVID-19 MESH:C000657245
311 22560-22568 Disease denotes COVID-19 MESH:C000657245
316 24279-24284 Species denotes human Tax:9606
317 24538-24546 Disease denotes COVID-19 MESH:C000657245
335 23009-23014 Species denotes human Tax:9606
336 24158-24164 Species denotes Turkey Tax:9103
337 23342-23345 Species denotes BCG Tax:33892
338 23697-23700 Species denotes BCG Tax:33892
339 23978-23981 Species denotes BCG Tax:33892
340 24112-24115 Species denotes BCG Tax:33892
341 23099-23107 Disease denotes COVID-19 MESH:C000657245
342 23286-23294 Disease denotes COVID-19 MESH:C000657245
343 23367-23374 Disease denotes malaria MESH:D008288
344 23394-23402 Disease denotes COVID-19 MESH:C000657245
345 23565-23573 Disease denotes COVID-19 MESH:C000657245
346 23717-23725 Disease denotes COVID-19 MESH:C000657245
347 23836-23844 Disease denotes COVID-19 MESH:C000657245
348 23932-23939 Disease denotes malaria MESH:D008288
349 24076-24084 Disease denotes COVID-19 MESH:C000657245
359 24496-24501 Species denotes human Tax:9606
360 24311-24319 Disease denotes COVID-19 MESH:C000657245
361 24464-24472 Disease denotes COVID-19 MESH:C000657245
363 24598-24606 Disease denotes COVID-19 MESH:C000657245
364 24635-24643 Disease denotes COVID-19 MESH:C000657245
365 24936-24944 Disease denotes COVID-19 MESH:C000657245
373 25756-25766 Species denotes SARS-CoV-2 Tax:2697049
374 25069-25078 Disease denotes infection MESH:D007239
375 25271-25279 Disease denotes COVID-19 MESH:C000657245
376 25392-25400 Disease denotes COVID-19 MESH:C000657245
377 25453-25463 Disease denotes infections MESH:D007239
378 25658-25666 Disease denotes COVID-19 MESH:C000657245
379 25732-25740 Disease denotes COVID-19 MESH:C000657245
381 25962-25970 Disease denotes COVID-19 MESH:C000657245
385 26259-26269 Disease denotes infections MESH:D007239
386 26295-26303 Disease denotes COVID-19 MESH:C000657245
387 26423-26431 Disease denotes COVID-19 MESH:C000657245
393 28160-28166 Species denotes people Tax:9606
394 28278-28284 Species denotes people Tax:9606
395 28047-28050 Species denotes BCG Tax:33892
396 28167-28175 Disease denotes infected MESH:D007239
397 28179-28186 Disease denotes malaria MESH:D008288
399 26516-26524 Disease denotes COVID-19 MESH:C000657245
409 26612-26620 Disease denotes COVID-19 MESH:C000657245
410 26725-26733 Disease denotes COVID-19 MESH:C000657245
411 26800-26808 Disease denotes COVID-19 MESH:C000657245
412 26903-26911 Disease denotes COVID-19 MESH:C000657245
413 26976-26984 Disease denotes COVID-19 MESH:C000657245
414 27068-27076 Disease denotes COVID-19 MESH:C000657245
415 27143-27151 Disease denotes COVID-19 MESH:C000657245
416 27250-27258 Disease denotes COVID-19 MESH:C000657245
417 27323-27331 Disease denotes COVID-19 MESH:C000657245
433 28551-28556 Species denotes human Tax:9606
434 28762-28765 Species denotes BCG Tax:33892
435 29019-29022 Species denotes BCG Tax:33892
436 29295-29298 Species denotes BCG Tax:33892
437 28406-28414 Disease denotes COVID-19 MESH:C000657245
438 28446-28455 Disease denotes infection MESH:D007239
439 28646-28654 Disease denotes COVID-19 MESH:C000657245
440 28686-28695 Disease denotes infection MESH:D007239
441 28831-28839 Disease denotes COVID-19 MESH:C000657245
442 28871-28880 Disease denotes infection MESH:D007239
443 29079-29087 Disease denotes COVID-19 MESH:C000657245
444 29119-29128 Disease denotes infection MESH:D007239
445 29267-29274 Disease denotes malaria MESH:D008288
446 29361-29369 Disease denotes COVID-19 MESH:C000657245
447 29533-29541 Disease denotes COVID-19 MESH:C000657245
459 29833-29840 Species denotes persons Tax:9606
460 30187-30195 Species denotes patients Tax:9606
461 30436-30441 Species denotes human Tax:9606
462 29657-29665 Disease denotes COVID-19 MESH:C000657245
463 29723-29731 Disease denotes COVID-19 MESH:C000657245
464 29732-29741 Disease denotes infection MESH:D007239
465 29950-29958 Disease denotes COVID-19 MESH:C000657245
466 30178-30186 Disease denotes COVID-19 MESH:C000657245
467 30353-30361 Disease denotes COVID-19 MESH:C000657245
468 30472-30480 Disease denotes COVID-19 MESH:C000657245
469 30549-30557 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 2060-2085 http://purl.obolibrary.org/obo/GO_0044403 denotes host–pathogen interaction
T2 9877-9885 http://purl.obolibrary.org/obo/GO_0007612 denotes learning
T3 16325-16331 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T4 29704-29710 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T5 29879-29900 http://purl.obolibrary.org/obo/GO_0001171 denotes reverse transcription
T6 29887-29900 http://purl.obolibrary.org/obo/GO_0006351 denotes transcription

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-40 Sentence denotes Multiple drivers of the COVID-19 spread:
T2 41-117 Sentence denotes The roles of climate, international mobility, and region-specific conditions
T3 118-144 Sentence denotes Drivers of COVID-19 spread
T4 146-154 Sentence denotes Abstract
T5 155-274 Sentence denotes Following its initial appearance in December 2019, coronavirus disease 2019 (COVID-19) quickly spread around the globe.
T6 275-791 Sentence denotes Here, we evaluated the role of climate (temperature and precipitation), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and percentage of the population aged over 65 years), and human mobility (relative amounts of international visitors) in shaping the geographical patterns of COVID-19 case numbers across 1,020 countries/regions, and examined the sequential shift that occurred from December 2019 to June 30, 2020 in multiple drivers of the cumulative number of COVID-19 cases.
T7 792-897 Sentence denotes Our regression model adequately explains the cumulative COVID-19 case numbers (per 1 million population).
T8 898-1013 Sentence denotes As the COVID-19 spread progressed, the explanatory power (R2) of the model increased, reaching > 70% in April 2020.
T9 1014-1275 Sentence denotes Climate, host mobility, and host susceptibility to COVID-19 largely explained the variance among COVID-19 case numbers across locations; the relative importance of host mobility and that of host susceptibility to COVID-19 were both greater than that of climate.
T10 1276-1553 Sentence denotes Notably, the relative importance of these factors changed over time; the number of days from outbreak onset drove COVID-19 spread in the early stage, then human mobility accelerated the pandemic, and lastly climate (temperature) propelled the phase following disease expansion.
T11 1554-1731 Sentence denotes Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility.
T12 1732-2003 Sentence denotes The identification of these multiple drivers of the COVID-19 outbreak trajectory, based on mapping the spread of COVID-19, will contribute to a better understanding of the COVID-19 disease transmission risk and inform long-term preventative measures against this disease.
T13 2005-2017 Sentence denotes Introduction
T14 2018-2307 Sentence denotes The spread of infectious diseases through host–pathogen interaction is fundamentally underpinned by macroecological and biogeographical processes [1, 2]; key processes include virus origination, dispersal, and evolutional diversification through local transmissions in human societies [3].
T15 2308-2489 Sentence denotes Since December 2019, coronavirus disease 2019 (COVID-19), caused by sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly spread worldwide from Wuhan, China [4].
T16 2490-2814 Sentence denotes The disease transmission geography of COVID-19 was highly heterogeneous; some countries (e.g., Japan) had cases from the earliest stage of this pandemic, but their increase in the number of new cases was relatively moderate, whereas others (e.g., EU nations and the USA) experienced later but substantial COVID-19 outbreaks.
T17 2815-2935 Sentence denotes To predict infection risk on the global scale, the forces driving the COVID-19 outbreak patterns must be identified [5].
T18 2936-3113 Sentence denotes Additionally, capturing region-specific factors influencing the outbreak progress is critically important for improving long-term control measures against this ongoing pandemic.
T19 3114-3216 Sentence denotes Infectious diseases due to respiratory viruses are empirically characterized by a seasonal nature [6].
T20 3217-3586 Sentence denotes Moriyama et al. [7] described a framework to better understand the mechanisms of virus transmission; air temperature, absolute/relative humidity, and sunlight are jointly associated with virus viability/stability and host defense, and thereby human-to-human transmission of COVID-19 is promoted by contact rates along with host susceptibility (or immunity) to COVID-19.
T21 3587-3839 Sentence denotes From this viewpoint, several research groups have focused on relevant factors separately and quickly examined the role of climate [8–10], international mobility linked to human contact [11, 12], and community-based host susceptibility to COVID-19 [13].
T22 3840-3991 Sentence denotes However, these analyses were inconclusive, and the relative importance of these factors in promoting the disease expansion of COVID-19 remains unclear.
T23 3992-4392 Sentence denotes This study assessed multiple potential drivers of the COVID-19 spread, by conducting an analysis of time-series data on the number of confirmed COVID-19 cases from December 2019 through June 2020, as well as on country/region-specific variables, e.g. socioeconomic conditions and screening effort (number of SARS-CoV-2 PCR tests conducted), that could potentially affect the number of COVID-19 cases.
T24 4393-4547 Sentence denotes Specifically, we explored the roles of climate, international mobility, and region-specific conditions in the disease expansion by controlling covariates.
T25 4548-5059 Sentence denotes In this analysis, we evaluated the relative importance of climate (temperature and precipitation relevant to habitat suitability for SARS-CoV-2), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and the relative proportion of citizens aged over 65 years in the population, as these were hypothesized to be linked with host susceptibility to COVID-19), and human mobility (international travel) in shaping the current geographical patterns of COVID-19 spread around the world.
T26 5061-5082 Sentence denotes Materials and methods
T27 5084-5096 Sentence denotes Data sources
T28 5097-5210 Sentence denotes We compiled geographic data on the number of reported COVID-19 cases per day from December 2019 to June 30, 2020.
T29 5211-5371 Sentence denotes We collected the numbers of COVID-19 cases for 1,020 countries/regions from various sources (see S1 Appendix for a list of data sources for the COVID-19 cases).
T30 5372-5532 Sentence denotes We then calculated the length of time (in days) since the onset of COVID-19 spread as defined by the date of the first confirmed case in each country or region.
T31 5533-5789 Sentence denotes We also examined the number of SARS-CoV-2 PCR tests conducted based on data published by the World Health Organization (WHO) (https://ourworldindata.org/covid-testing) to assess the influence of sampling effort on the number of confirmed cases of COVID-19.
T32 5790-5862 Sentence denotes For each country or region, we compiled several environmental variables.
T33 5863-6046 Sentence denotes For mapping cases of COVID-19, the longitude and latitude of the largest city and area for each country or region were extracted from GADM maps and data (https://gadm.org/index.html).
T34 6047-6382 Sentence denotes Based on the geocoordinates of the cities, we collected the climatic data of mean precipitation (mm month–1) and temperature (°C) from January to June (WorldClim) using WorldClim version 2.1 climate data (https://www.worldclim.org/data/worldclim21.html) at a resolution of 2.5 arc-minutes grid cells that contained a country or region.
T35 6383-6653 Sentence denotes Regarding international travel linked to the disease transmission, we compiled the average annual number of foreign visitors (per year) for individual countries/regions from data published by the World Tourism Organization (https://www.e-unwto.org/toc/unwtotfb/current).
T36 6654-6777 Sentence denotes We then calculated the relative amount of foreign visitors per population of each country or region to use in the analysis.
T37 6778-7060 Sentence denotes Regarding region-specific host susceptibility to COVID-19, we collected data on the following three epidemiologic properties: the proportion of the population aged over 65 years, the malaria incidence (per year), and information regarding bacillus Calmette–Guérin (BCG) vaccination.
T38 7061-7287 Sentence denotes We included these attributes in our analyses based on the assumptions that BCG vaccination and/or recurrent treatment with anti-malarial medications could be associated with providing some protection against COVID-19 [13, 14].
T39 7288-8213 Sentence denotes We compiled BCG data from the WHO (https://www.who.int/malaria/data/en/) and (https://apps.who.int/gho/data/view.main.80500?lang=en) and the BCG Atlas Team (http://www.bcgatlas.org/) on the following five attributes: i) the number of years since BCG vaccination was started (BCG_year); ii) the present situation regarding BCG vaccination (BCG_type), split into all vaccinated, partly vaccinated, vaccinated once in the past, or never vaccinated; iii) the relative frequency of post-1980 (i.e., the past 40 years) BCG vaccination for people aged less than 1 year old (BCG_rate); iv) the number of BCG vaccinations (MultipleBCG), describing countries as never having vaccinated their citizens with BCG, vaccinated their citizens with BCG only once, vaccinated their citizens with BCG multiple times in the past, or currently vaccinate their citizens with BCG multiple times; and v) tuberculosis cases per 1 million people (TB).
T40 8214-8271 Sentence denotes These BCG-related variables are strongly intercorrelated.
T41 8272-8587 Sentence denotes Therefore, we reduced the dimensions of these variables (BCG_year, BCG_type, BCG_rate, MultipleBCG, and TB) by extracting the first axis of the PCA analysis: the score of the PCA 1 axis was negatively correlated with the five variables, so the PCA 1 score multiplied by –1 was defined as the BCG vaccination effect.
T42 8588-8651 Sentence denotes We also compiled socioeconomic data for each country or region.
T43 8652-9010 Sentence denotes The population size, population density (per km2) (Gridded Population of the World GPW, v4.; https://sedac.ciesin.columbia.edu/data/collection/gpw-v4), gross domestic product (GDP in US dollars), and GDP per person were obtained from national census data (World Development Indicators; https://datacatalog.worldbank.org/dataset/world-development-indicators).
T44 9012-9032 Sentence denotes Statistical analyses
T45 9033-9250 Sentence denotes The monthly pattern for the cumulative number of COVID-19 cases in each country/region was visualized in relation to the geography, biome type, and climate (mean temperature and annual precipitation) of that location.
T46 9251-9488 Sentence denotes In addition, the pattern of increasing COVID-19 case numbers was evaluated based on country type, with individual countries being classified into four types defined by the number of COVID-19 cases per week and the date of outbreak onset.
T47 9489-9897 Sentence denotes To ensure the robustness of our results, we investigated the relationship between various environmental variables (climate, host susceptibility to COVID-19, international human mobility, and socioeconomic factors) and the number of COVID-19 cases (per 1 million population) using the two different approaches: conventional multiple linear regression and random forest, which is a machine-learning model [15].
T48 9898-10045 Sentence denotes We separately modeled the cumulative number of COVID-19 cases (per 1 million population) in successive periods from December 2019 to June 30, 2020.
T49 10046-10644 Sentence denotes In the multiple regression analysis, we set the log-scaled cumulative number of COVID-19 cases within a period as the response variable and the climatic factors (mean temperature, squared mean temperature, and log-scaled monthly precipitation), socioeconomic conditions (log-scaled population density and GDP per person), international human mobility (the relative amount of foreign visitors per population) and region-specific COVID-19 susceptibility (the percentage of people aged ≥ 65 years, the log-scaled relative incidence of malaria, and the BCG vaccination effect) as explanatory variables.
T50 10645-10908 Sentence denotes To control for country/region-specific observation biases, we included the length of time (measured in days) since the first confirmed COVID-19 case in each country/region and the number of COVID-19 tests conducted (as a measure of sampling effort) as covariates.
T51 10909-11194 Sentence denotes In addition, we applied the trend surface method to take spatial autocorrelation into account as a covariate; we added the first eigenvector of the geo-distance matrix among the countries or regions, which was computed using the geocoordinates of the largest city, as a covariate [16].
T52 11195-11294 Sentence denotes The explanatory power of the model was evaluated by the adjusted coefficient of determination (R2).
T53 11295-11538 Sentence denotes We also calculated the relative importance of each explanatory variable in a regression model according to its partial coefficient of determination and determined the predominant variables that explained the variance in the response variables.
T54 11539-11621 Sentence denotes The statistical significance of each variable was determined by conducting F-test.
T55 11622-11737 Sentence denotes All the explanatory variables were standardized to have a mean of zero and a variance of one before these analyses.
T56 11738-11831 Sentence denotes The explanatory factors of the regression model were compared between the four country types.
T57 11832-11951 Sentence denotes In the random forest model, we used the same set of response and explanatory variables, as well as the same covariates.
T58 11952-12031 Sentence denotes In each run of the random forest analysis, we generated 1,000 regression trees.
T59 12032-12121 Sentence denotes The model performance was evaluated by the proportion of variance explained by the model.
T60 12122-12269 Sentence denotes We evaluated the relative importance of each explanatory variable based on the increase in the mean squared error when the variable was permutated.
T61 12270-12405 Sentence denotes Before these analyses, we tested the collinearity between the explanatory variables by calculating the variance inflation factor (VIF).
T62 12406-12561 Sentence denotes For the study period, the largest VIF value was 8.56, and the VIF at June 30, 2020 was 8.56, indicating the absence of multicollinearity in the regression.
T63 12562-12802 Sentence denotes To confirm the testing effort bias on the number of confirmed cases, we conducted an additional analysis that accounted for the number of conducted tests (i.e., sampling efforts) in individual countries/regions, as a covariate in the model.
T64 12803-12991 Sentence denotes Note that this analysis was applied to the data from 128/828 countries/regions, because testing data for many countries is currently unavailable (https://ourworldindata.org/covid-testing).
T65 12992-13204 Sentence denotes All analyses were performed with the R environment for statistical computing [17]; the ‘sf’ package was used for graphics artworks [18] and the ‘randomForest’ package was used for the random forest analysis [19].
T66 13206-13228 Sentence denotes Results and discussion
T67 13229-13400 Sentence denotes COVID-19 (as measured by the number of cases per 1 million population) spread rapidly across the globe after it first appeared in Wuhan, China in December, 2019 (Li et al.
T68 13401-13539 Sentence denotes 2020) (Fig 1; S1 Video), but the outbreak appears to have occurred in particular climates around 8°C and 26°C or biomes (Fig 2; S2 Video).
T69 13540-13715 Sentence denotes Moreover, the patterns of increasing number of COVID-19 cases per week varied among the countries that are characterized by different COVID-19 spread dates (Fig 3 and S1 Fig).
T70 13716-13832 Sentence denotes Fig 1 Geographical distribution of COVID-19 cases (per 1 million population) for 1,020 countries/regions worldwide.
T71 13833-14108 Sentence denotes (A–F) Monthly patterns for the cumulative number of COVID-19 cases on January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), and June 30, 2020 (F) based on the cumulative number of day-to-day COVID-19 cases since December 2019.
T72 14109-14122 Sentence denotes See S3 Video.
T73 14123-14224 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T74 14225-14362 Sentence denotes Fig 2 The distribution of COVID-19 cases across biome types based on the relationship between mean temperature and annual precipitation.
T75 14363-14668 Sentence denotes Biome classification is based on the scheme by Whittaker [20]. (TR) tropical rain forest; (TS) tropical seasonal forest/savanna; (TE) temperate rain forest; (SD) subtropical desert; (TD) temperate deciduous forest; (WS) woodland/shrubland; (TG) temperate grassland/desert; (BF) boreal forest, (TU) tundra.
T76 14669-15097 Sentence denotes Colors indicate the number of COVID-19 cases (per 1 million population) and also contours of climatic regions with ≥1000 cases per 1 million population. (A–F) Monthly patterns for the cumulative number of COVID-19 cases on January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), and June 30, 2020 (F) based on the cumulative number of day-to-day COVID-19 cases since December 2019.
T77 15098-15145 Sentence denotes Arrows indicate the location of Wuhan in China.
T78 15146-15159 Sentence denotes See S4 Video.
T79 15160-15275 Sentence denotes Fig 3 Patterns for the cumulative number of COVID-19 cases (per 1 million population) in relation to country type.
T80 15276-16096 Sentence denotes Based on the pattern of increasing COFVID-19 case numbers, individual countries were classified into four types (A–D): (A) 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; (B) 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; (C) 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; (D) 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.
T81 16097-16198 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T82 16199-16604 Sentence denotes Although the COVID-19 case numbers may not be suitable for conducting epidemiological analyses, such as modelling the disease growth dynamics, the available COVID-19 case data can be still informative for the implementation of containment and/or suppression measures because the number of the confirmed cases is directly linked to the consumption of medical resources for combatting the COVID-19 pandemic.
T83 16605-16925 Sentence denotes Here, we observed that the cumulative number of the COVID-19 cases (per 1 million population) according to the disease spread progression was significantly correlated with variables related to climate, international human mobility, and host susceptibility to COVID-19, at successive periods since December, 2019 (Fig 4).
T84 16926-17149 Sentence denotes Fig 4 Standardized regression coefficients and the partial coefficient of determination (r2) of each explanatory factor in the regression model explaining the cumulative number of COVID-19 cases (per 1 million population).
T85 17150-17324 Sentence denotes (A–F) Values for the period from December 2019 to January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), or June 30, 2020 (F).
T86 17325-17841 Sentence denotes Temp, mean temperature; Temp2, squared mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset.
T87 17842-17911 Sentence denotes The regressions were conducted using ordinary least squares analyses.
T88 17912-17980 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T89 17981-18058 Sentence denotes Closed symbols indicate the significance of explanatory variables (p < 0.05).
T90 18059-18133 Sentence denotes The coefficient of determination (R2) for the overall model is also shown.
T91 18134-18505 Sentence denotes A nonlinear modeling analysis was also conducted using the random forest method with the same set of response and explanatory variables and the same covariates; the results of this parallel analysis are shown in S2 Fig. The explanatory power, i.e., coefficient of determination (R2), of the model as the COVID-19 pandemic progressed, reaching >70% in April 2020 (Fig 5A).
T92 18506-18673 Sentence denotes The number of days from case onset had some explanatory power (> 20%) in January, 2020, but this factor quickly lost its influence as the pandemic progressed (Fig 5B).
T93 18674-18856 Sentence denotes As the influence of this factor waned, other variables (related to climate, human mobility, and host susceptibility to COVID-19) exhibited the increasing explanatory powers (Fig 5B).
T94 18857-18990 Sentence denotes After April 2020, the explanatory power of variables related to human mobility and host susceptibility to COVID-19 rapidly decreased.
T95 18991-19075 Sentence denotes After this, the explanatory power of human population and climate factors increased.
T96 19076-19228 Sentence denotes These results demonstrate that the impact of virus dispersability between/within regions was predominant in the beginning stage of the pandemic (Fig 5).
T97 19229-19419 Sentence denotes Fig 5 Coefficients of determination (adjusted R2) of the regression model explaining the cumulative number of COVID-19 cases (per 1 million population) from December, 2019 to June 30, 2020.
T98 19420-19575 Sentence denotes (A) Overall coefficient of determination of the regression model; (B) coefficient of partial determination (r2) for each explanatory variable in the model.
T99 19576-19721 Sentence denotes The results shown are based on data starting from January, 2020, because the number of cases in December 2019 was insufficient for this analysis.
T100 19722-19892 Sentence denotes The standardized regression coefficients of the model greatly changed (from non-significant to significant) over the period from December, 2019 to April 12, 2020 (Fig 6).
T101 19893-20095 Sentence denotes After February, 2020, the mean temperature was negatively correlated with the cumulative number of COVID-19 cases, whereas the mean precipitation was positively correlated with these values (Fig 6A–6C).
T102 20096-20287 Sentence denotes After March, 2020, relative amount of foreign visitors per population and GDP per person were predominantly positively correlated with the cumulative number of COVID-19 cases (Fig 6E and 6F).
T103 20288-20482 Sentence denotes In contrast, since February or March 2020, the BCG vaccination factors and malaria incidence were consistently negatively correlated with the cumulative number of COVID-19 cases (Fig 6G and 6H).
T104 20483-20591 Sentence denotes Population density was slightly positively correlated with the cumulative number of COVID-19 cases (Fig 6D).
T105 20592-20773 Sentence denotes The relative proportion of the population aged ≥65 years was also positively correlated with these values, except for a temporary period where it was negatively correlated (Fig 6I).
T106 20774-21029 Sentence denotes This shift from positive to negative correlation reflects the initial spread of COVID-19 in developed countries with relatively older population and the later (after May 2020) spread of COVID-19 in developing countries with relatively younger populations.
T107 21030-21193 Sentence denotes In the early stage of COVID-19 spread, the number of days from case onset was strongly positively correlated with the cumulative number of COVID-19 cases (Fig 6J).
T108 21194-21392 Sentence denotes Fig 6 Time-series pattern of the standardized regression coefficients of the model explaining the cumulative number of COVID-19 cases (per 1 million population) from December 2019 to June 30, 2020.
T109 21393-21461 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T110 21462-21608 Sentence denotes The results are based on data starting from January 2020 because the number of COVID-19 cases in December 2019 was insufficient for this analysis.
T111 21609-21734 Sentence denotes The results of the random forest model were generally consistent with those of the linear multiple regression model (S2 Fig).
T112 21735-22056 Sentence denotes The relative importance of the variables related to human mobility and host susceptibility to COVID-19 (elderly population, BCG vaccination effect, and malaria incidence) became predominant over time, whereas the relative importance of population density and the number of days from case onset decreased after March 2020.
T113 22057-22400 Sentence denotes Moreover, additional analyses, which included the number of conducted COVID-19 tests as a covariate, revealed very similar patterns of regression coefficients, and their explanatory power (S3 Fig), i.e., the roles of climate, international human mobility, and host susceptibility to COVID-19, became more pronounced as the pandemic progressed.
T114 22401-22582 Sentence denotes Therefore, the nonlinearity of epidemic and region-specific testing bias had no serious influence on identifying the environmental drivers shaping the present COVID-19 distribution.
T115 22583-22828 Sentence denotes This study generally supports the findings of several recent reports, which found that climate [8–10], international human mobility [11, 12], and community-based host susceptibility to COVID-19 [13] jointly contributed to the spread of COVID-19.
T116 22829-22995 Sentence denotes Notably, the explanatory power of these drivers substantially increased as the pandemic progressed, indicating a deterministic expansion of COVID-19 around the world.
T117 22996-23117 Sentence denotes Cross-border human mobility, which has been facilitated by globalization [21], clearly accelerated the COVID-19 pandemic.
T118 23118-23256 Sentence denotes This finding is in line with a report by Coelho et al. [12], which emphasized the role of the air transportation network in this pandemic.
T119 23257-23597 Sentence denotes In addition, region-specific COVID-19 susceptibility, which was approximated here by BCG vaccination factors, malaria incidence (because COVID-19 susceptibility may be linked to anti-malarial drug use), and the proportion of the population aged over 65 years, explained a substantial part of the variance in COVID-19 case numbers worldwide.
T120 23598-23737 Sentence denotes This data support the findings by Sala et al. [13] that there is a significant correlation between BCG vaccination and COVID-19 prevalence.
T121 23738-23812 Sentence denotes Notably, these correlation patterns may change as the pandemic progresses.
T122 23813-24178 Sentence denotes For example, while the COVID-19 case numbers (per 1 million population) exhibited a relatively robust correlation with malaria incidence, their correlation with the BCG vaccination effect weakened after April 2020, potentially as a result of the recent spread of COVID-19 into more countries with a BCG vaccination program (e.g., Japan, Russia, Turkey, and Brazil).
T123 24179-24486 Sentence denotes Our analysis using the regression model, which comprehensively accounted for climate, international human mobility, region-specific COVID-19 susceptibility, and socioeconomic conditions, revealed that climate suitability remains an important driver shaping the current distribution of COVID-19 cases [5, 9].
T124 24487-24782 Sentence denotes Although human mobility and host susceptibility to COVID-19 were found to be the main drivers in the spread of COVID-19, the uneven distribution of COVID-19 cases across biome types (Fig 2 and S2 and S4 Videos) suggests that the pandemic may be partially shaped by biogeographical patterns [22].
T125 24783-24949 Sentence denotes However, until the pandemic has lasted a full year, it will not be possible to draw reliable conclusions on the relationship between abiotic factors and COVID-19 [7].
T126 24950-25180 Sentence denotes Our predictive model does not account for variables relevant to local-scale factors that are associated with community infection or containment/suppression measures implemented against the epidemic in individual countries/regions.
T127 25181-25354 Sentence denotes Consequently, the model has residuals (Fig 7), i.e., deviations in the observed number of COVID-19 cases that reflect the influence of local-scale drivers on disease spread.
T128 25355-25888 Sentence denotes Positive deviations in the number of COVID-19 cases may indicate more serious local-scale cluster infections, e.g., in some prefectures in Japan or in parts of South East Asia, Africa, and South America, than predicted by the macro-scale driver-based model, whereas negative deviations in the number of COVID-19 cases indicate the influence of distributional disequilibrium of COVID-19 cases (because SARS-CoV-2 has only recently reached an area, e.g., Africa) or suggest the effectiveness of the present control measures in an area.
T129 25889-26081 Sentence denotes Fig 7 Residual pattern of the regression model predicting the number of COVID-19 cases (per 1 million population) for 1,020 countries/regions across the globe and for 47 prefectures in Japan.
T130 26082-26183 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T131 26184-26407 Sentence denotes There is still a distributional disequilibrium in the global prevalence of infections; the number of confirmed COVID-19 cases changes daily, and the trajectories among countries or regions differ largely (Fig 3 and S1 Fig).
T132 26408-26491 Sentence denotes The drivers of COVID-19 case numbers indicate a country-specific pattern (Table 1).
T133 26492-26565 Sentence denotes Table 1 Drivers of the COVID-19 spread in relation to the country types.
T134 26566-26673 Sentence denotes Country types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3).
T135 26674-27363 Sentence denotes 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.
T136 27364-27488 Sentence denotes The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test.
T137 27489-27587 Sentence denotes Different letters indicate the values that are significantly different (p < 0.05) from each other.
T138 27588-27626 Sentence denotes Factor Type A Type B Type C Type D
T139 27627-27714 Sentence denotes Mean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d
T140 27715-27798 Sentence denotes Mean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b
T141 27799-27868 Sentence denotes Population density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)
T142 27869-27961 Sentence denotes Relative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b
T143 27962-28046 Sentence denotes GDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c
T144 28047-28137 Sentence denotes BCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b
T145 28138-28255 Sentence denotes Relative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b
T146 28256-28602 Sentence denotes Relative 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 The type A countries, with more than 1,000 COVID-19 cases per million in which the infection peaked before mid-April, were mostly the developed countries that had predominant cross-border human mobility in relatively cool and dry climates.
T147 28603-28787 Sentence denotes The type B countries, with more than 1,000 COVID-19 cases per million in which the infection spread peaked after mid-June, were quasi-developed countries with BCG vaccination programs.
T148 28788-29035 Sentence denotes The type C countries, with less than 1,000 COVID-19 cases per million in which the infection peaked before mid-April, were countries with high temperature and humidity that are characterized by lower cross-border mobility and more BCG vaccination.
T149 29036-29311 Sentence denotes The type D countries, with less than 1,000 COVID-19 cases per million in which the infection spread peaked after mid-June, were mostly tropical developing countries with lower population density, less cross-border mobility, higher malaria incidence, and less BCG vaccination.
T150 29312-29491 Sentence denotes These country-specific factors indicate that the COVID-19 spread is not simply driven by specific environmental variables, and the underlying mechanisms are complicated (Table 1).
T151 29492-29613 Sentence denotes Therefore, evaluating the drivers of the COVID-19 spread at the present phase of disease expansion is a challenging task.
T152 29614-29742 Sentence denotes The absence of population-wide testing for COVID-19 makes it difficult to investigate the growth dynamics of COVID-19 infection.
T153 29743-29841 Sentence denotes The case data include a selection bias due to surveillance focusing mainly on symptomatic persons.
T154 29842-30143 Sentence denotes In particular, the availability of a reverse transcription polymerase chain reaction (PCR) test to identify COVID-19 cases, e.g. the number of PCR tests conducted per population, varies greatly among countries with different medical/public-health conditions (https://ourworldindata.org/covid-testing).
T155 30144-30304 Sentence denotes Therefore, the true number of the COVID-19 patients and the dynamics of the disease spread are obscured behind the prevalence of asymptomatic carriers [23, 24].
T156 30305-30496 Sentence denotes Nevertheless, our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility.
T157 30497-30779 Sentence denotes The present results, based on mapping the spread of COVID-19 and identifying multiple drivers of the outbreak trajectory, contribute to a better understanding of the disease transmission risk and may inform the application of appropriate preventative measures against this pandemic.
T158 30781-30803 Sentence denotes Supporting information
T159 30804-30907 Sentence denotes S1 Fig The distribution of four country types classified based on the COVID-19 outbreak across biomes.
T160 30908-31843 Sentence denotes 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 of 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 more than 1,000 COVID-19 cases per 1 million population. (TR) tropical rain forest; (TS) tropical seasonal forest/savanna; (TE) temperate rain forest; (SD) subtropical desert; (TD) temperate deciduous forest; (WS) woodland/shrubland; (TG) temperate grassland/desert; (BF) boreal forest; (TU) tundra.
T161 31844-31849 Sentence denotes (TIF)
T162 31850-31886 Sentence denotes Click here for additional data file.
T163 31887-32045 Sentence denotes S2 Fig Relative importance of explanatory factors in the random forest models explaining the geographical pattern of the cumulative number of COVID-19 cases.
T164 32046-32529 Sentence denotes Temp, mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset.
T165 32530-32535 Sentence denotes (TIF)
T166 32536-32572 Sentence denotes Click here for additional data file.
T167 32573-32703 Sentence denotes S3 Fig Results of additional analyses using the number of conducted COVID-19 tests (sampling effort) as a covariate in the model.
T168 32704-33210 Sentence denotes Temp, mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset; Test, number of tests.
T169 33211-33216 Sentence denotes (TIF)
T170 33217-33253 Sentence denotes Click here for additional data file.
T171 33254-33319 Sentence denotes S1 Appendix List of data sources for the COVID-19 cases numbers.
T172 33320-33326 Sentence denotes (DOCX)
T173 33327-33363 Sentence denotes Click here for additional data file.
T174 33364-33403 Sentence denotes S1 Video https://youtu.be/ZIDMtbek-48.
T175 33404-33409 Sentence denotes (TXT)
T176 33410-33446 Sentence denotes Click here for additional data file.
T177 33447-33486 Sentence denotes S2 Video https://youtu.be/KlnpUY51D3k.
T178 33487-33492 Sentence denotes (TXT)
T179 33493-33529 Sentence denotes Click here for additional data file.
T180 33530-33569 Sentence denotes S3 Video https://youtu.be/UQViOcMFhNk.
T181 33570-33575 Sentence denotes (TXT)
T182 33576-33612 Sentence denotes Click here for additional data file.
T183 33613-33652 Sentence denotes S4 Video https://youtu.be/3DpjGoTrk-E.
T184 33653-33658 Sentence denotes (TXT)
T185 33659-33695 Sentence denotes Click here for additional data file.
T186 33697-33771 Sentence denotes We are grateful to the Kubota-lab technical staff for the data management.

2_test

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