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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T4 1166-1174 Body_part denotes Appendix http://purl.org/sig/ont/fma/fma14542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T58 50-58 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T59 144-152 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T60 1225-1233 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T61 1329-1337 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T62 1436-1444 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T63 1665-1673 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T64 1807-1815 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T65 1886-1894 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T66 5429-5437 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T67 5981-5989 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T80 553-554 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T81 808-809 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T82 814-815 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T83 1127-1128 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T84 1133-1134 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T85 1175-1176 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T86 1519-1520 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T87 1746-1747 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T88 2342-2343 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T89 2389-2390 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T90 2436-2437 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T91 3571-3574 http://purl.obolibrary.org/obo/CLO_0007874 denotes m/s
T92 3887-3890 http://purl.obolibrary.org/obo/CLO_0007874 denotes m/s
T93 4443-4446 http://purl.obolibrary.org/obo/CLO_0007874 denotes m/s
T94 4754-4757 http://purl.obolibrary.org/obo/CLO_0007874 denotes m/s
T95 5047-5059 http://purl.obolibrary.org/obo/OBI_0000968 denotes instrumental
T96 5665-5666 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T115 0-9 Sentence denotes Variables
T116 10-199 Sentence denotes January 19, 2020, is the first day that COVID-19 cases were reported outside of Wuhan, so we collect the daily number of new cases of COVID-19 for 305 cities from January 19 to February 29.
T117 200-281 Sentence denotes All these data are reported by 32 provincial-level Health Commissions in China10.
T118 282-432 Sentence denotes Figure 2 shows the time patterns of daily confirmed new cases in Wuhan, in Hubei province outside Wuhan, and in non-Hubei provinces of mainland China.
T119 433-653 Sentence denotes Because Hubei province started to include clinically diagnosed cases into new confirmed cases on February 12, we notice a spike in the number of new cases in Wuhan and other cities in Hubei province on this day (Fig. 2).
T120 654-763 Sentence denotes The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects.
T121 764-853 Sentence denotes As robustness checks, we re-estimate models A and B without the cities in Hubei province.
T122 854-1135 Sentence denotes In addition, since the number of clinically diagnosed cases at the city level was reported for the days of February 12, 13, and 14, we recalculated the daily number of new cases for the 3 days by removing the clinically diagnosed cases from our data and re-estimate models A and B.
T123 1136-1178 Sentence denotes Our main findings still hold (Appendix B).
T124 1179-1251 Sentence denotes Fig. 2 Number of daily new confirmed cases of COVID-19 in mainland China
T125 1252-1404 Sentence denotes Regarding the explanatory variables, we calculate the number of new cases of COVID-19 in the preceding first and second weeks for each city on each day.
T126 1405-1796 Sentence denotes To estimate the impacts of new COVID-19 cases in other cities, we first calculate the geographic distance between a city and all other cities using the latitudes and longitudes of the centroids of each city and then calculate the weighted sum of the number of COVID-19 new cases in all other cities using the inverse of log distance between a city and each of the other cities as the weight.
T127 1797-1962 Sentence denotes Since the COVID-19 outbreak started from Wuhan, we also calculate the weighted number of COVID-19 new cases in Wuhan using the inverse of log distance as the weight.
T128 1963-2172 Sentence denotes Furthermore, to explore the mediating impact of population flow from Wuhan, we collect the daily population flow index from Baidu that proxies for the total intensity of migration from Wuhan to other cities11.
T129 2173-2289 Sentence denotes Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019.
T130 2290-2455 Sentence denotes We then interact the flow index with the share that a destination city takes (Fig. 4) to construct a measure on the population flow from Wuhan to a destination city.
T131 2456-2641 Sentence denotes Other mediating variables include population density, GDP per capita, and the number of doctors at the city level, which we collect from the most recent China city statistical yearbook.
T132 2642-2701 Sentence denotes Table 1 presents the summary statistics of these variables.
T133 2702-2816 Sentence denotes On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei.
T134 2817-2896 Sentence denotes Compared with cities in Hubei province, cities outside Hubei have more doctors.
T135 2897-2945 Sentence denotes Fig. 3 Baidu index of population flow from Wuhan
T136 2946-3001 Sentence denotes Fig. 4 Destination shares in population flow from Wuhan
T137 3002-3028 Sentence denotes Table 1 Summary statistics
T138 3029-3053 Sentence denotes Variable N Mean Std dev.
T139 3054-3058 Sentence denotes Min.
T140 3059-3070 Sentence denotes Median Max.
T141 3071-3087 Sentence denotes Non Hubei cities
T142 3088-3108 Sentence denotes City characteristics
T143 3109-3169 Sentence denotes GDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549
T144 3170-3240 Sentence denotes Population density, per km2 288 428.881 374.138 9.049 327.115 3444.092
T145 3241-3296 Sentence denotes # of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938
T146 3297-3333 Sentence denotes Time varying variables, Jan 19–Feb 1
T147 3334-3400 Sentence denotes Daily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000
T148 3401-3475 Sentence denotes Weekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833
T149 3476-3543 Sentence denotes Weekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570
T150 3544-3609 Sentence denotes Weekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386
T151 3610-3646 Sentence denotes Time varying variables, Feb 1–Feb 29
T152 3647-3714 Sentence denotes Daily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000
T153 3715-3791 Sentence denotes Weekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791
T154 3792-3859 Sentence denotes Weekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432
T155 3860-3925 Sentence denotes Weekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129
T156 3926-3967 Sentence denotes Cities in Hubei province, excluding Wuhan
T157 3968-3988 Sentence denotes City characteristics
T158 3989-4047 Sentence denotes GDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998
T159 4048-4117 Sentence denotes Population density, per km2 16 416.501 220.834 24.409 438.820 846.263
T160 4118-4171 Sentence denotes # of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393
T161 4172-4208 Sentence denotes Time varying variables, Jan 19–Feb 1
T162 4209-4277 Sentence denotes Daily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000
T163 4278-4348 Sentence denotes Weekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889
T164 4349-4415 Sentence denotes Weekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633
T165 4416-4480 Sentence denotes Weekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439
T166 4481-4517 Sentence denotes Time varying variables, Feb 1–Feb 29
T167 4518-4586 Sentence denotes Daily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000
T168 4587-4659 Sentence denotes Weekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413
T169 4660-4726 Sentence denotes Weekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535
T170 4727-4791 Sentence denotes Weekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174
T171 4792-4875 Sentence denotes Variables of the city characteristics are obtained from City Statistical Yearbooks.
T172 4876-4932 Sentence denotes Time varying variables are observed daily for each city.
T173 4933-5002 Sentence denotes Weekly average weather variables are averages over the preceding week
T174 5003-5099 Sentence denotes We rely on meteorological data to construct instrumental variables for the endogenous variables.
T175 5100-5358 Sentence denotes The National Oceanic and Atmospheric Administration (NOAA) provides average, maximum, and minimum temperatures, air pressure, average and maximum wind speeds, precipitation, snowfall amount, and dew point for 362 weather stations at the daily level in China.
T176 5359-5627 Sentence denotes To merge the meteorological variables with the number of new cases of COVID-19, we first calculate daily weather variables for each city on each day from 2019 December to 2020 February from station-level weather records following the inverse distance weighting method.
T177 5628-5810 Sentence denotes Specifically, for each city, we draw a circle of 100 km from the city’s centroid and calculate the weighted average daily weather variables using stations within the 100-km circle12.
T178 5811-5905 Sentence denotes We use the inverse of the distance between the city’s centroid and each station as the weight.
T179 5906-6018 Sentence denotes Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date.

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
155 1225-1233 Disease denotes COVID-19 MESH:C000657245
158 50-58 Disease denotes COVID-19 MESH:C000657245
159 144-152 Disease denotes COVID-19 MESH:C000657245
163 1329-1337 Disease denotes COVID-19 MESH:C000657245
164 1436-1444 Disease denotes COVID-19 MESH:C000657245
165 1665-1673 Disease denotes COVID-19 MESH:C000657245
169 2714-2717 Chemical denotes GDP MESH:D006153
170 1807-1815 Disease denotes COVID-19 MESH:C000657245
171 1886-1894 Disease denotes COVID-19 MESH:C000657245
174 5429-5437 Disease denotes COVID-19 MESH:C000657245
175 5981-5989 Disease denotes COVID-19 MESH:C000657245