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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T4","span":{"begin":1166,"end":1174},"obj":"Body_part"}],"attributes":[{"id":"A4","pred":"fma_id","subj":"T4","obj":"http://purl.org/sig/ont/fma/fma14542"}],"text":"Variables\nJanuary 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. All these data are reported by 32 provincial-level Health Commissions in China10. 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. 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). The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects. As robustness checks, we re-estimate models A and B without the cities in Hubei province. 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. Our main findings still hold (Appendix B).\nFig. 2 Number of daily new confirmed cases of COVID-19 in mainland China\nRegarding 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. 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.\nSince 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. 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. Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019. 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. 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. Table 1 presents the summary statistics of these variables. On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei. Compared with cities in Hubei province, cities outside Hubei have more doctors.\nFig. 3 Baidu index of population flow from Wuhan\nFig. 4 Destination shares in population flow from Wuhan\nTable 1 Summary statistics\nVariable N Mean Std dev. Min. Median Max.\nNon Hubei cities\nCity characteristics\nGDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549\nPopulation density, per km2 288 428.881 374.138 9.049 327.115 3444.092\n# of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000\nWeekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833\nWeekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570\nWeekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000\nWeekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791\nWeekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432\nWeekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129\nCities in Hubei province, excluding Wuhan\nCity characteristics\nGDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998\nPopulation density, per km2 16 416.501 220.834 24.409 438.820 846.263\n# of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000\nWeekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889\nWeekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633\nWeekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000\nWeekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413\nWeekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535\nWeekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174\nVariables of the city characteristics are obtained from City Statistical Yearbooks. Time varying variables are observed daily for each city. Weekly average weather variables are averages over the preceding week\nWe rely on meteorological data to construct instrumental variables for the endogenous variables. 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. 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. 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. We use the inverse of the distance between the city’s centroid and each station as the weight. Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date."}

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T58","span":{"begin":50,"end":58},"obj":"Disease"},{"id":"T59","span":{"begin":144,"end":152},"obj":"Disease"},{"id":"T60","span":{"begin":1225,"end":1233},"obj":"Disease"},{"id":"T61","span":{"begin":1329,"end":1337},"obj":"Disease"},{"id":"T62","span":{"begin":1436,"end":1444},"obj":"Disease"},{"id":"T63","span":{"begin":1665,"end":1673},"obj":"Disease"},{"id":"T64","span":{"begin":1807,"end":1815},"obj":"Disease"},{"id":"T65","span":{"begin":1886,"end":1894},"obj":"Disease"},{"id":"T66","span":{"begin":5429,"end":5437},"obj":"Disease"},{"id":"T67","span":{"begin":5981,"end":5989},"obj":"Disease"}],"attributes":[{"id":"A58","pred":"mondo_id","subj":"T58","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A59","pred":"mondo_id","subj":"T59","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A60","pred":"mondo_id","subj":"T60","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A61","pred":"mondo_id","subj":"T61","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A62","pred":"mondo_id","subj":"T62","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A63","pred":"mondo_id","subj":"T63","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A64","pred":"mondo_id","subj":"T64","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A65","pred":"mondo_id","subj":"T65","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A66","pred":"mondo_id","subj":"T66","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A67","pred":"mondo_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"Variables\nJanuary 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. All these data are reported by 32 provincial-level Health Commissions in China10. 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. 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). The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects. As robustness checks, we re-estimate models A and B without the cities in Hubei province. 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. Our main findings still hold (Appendix B).\nFig. 2 Number of daily new confirmed cases of COVID-19 in mainland China\nRegarding 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. 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.\nSince 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. 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. Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019. 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. 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. Table 1 presents the summary statistics of these variables. On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei. Compared with cities in Hubei province, cities outside Hubei have more doctors.\nFig. 3 Baidu index of population flow from Wuhan\nFig. 4 Destination shares in population flow from Wuhan\nTable 1 Summary statistics\nVariable N Mean Std dev. Min. Median Max.\nNon Hubei cities\nCity characteristics\nGDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549\nPopulation density, per km2 288 428.881 374.138 9.049 327.115 3444.092\n# of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000\nWeekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833\nWeekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570\nWeekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000\nWeekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791\nWeekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432\nWeekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129\nCities in Hubei province, excluding Wuhan\nCity characteristics\nGDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998\nPopulation density, per km2 16 416.501 220.834 24.409 438.820 846.263\n# of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000\nWeekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889\nWeekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633\nWeekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000\nWeekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413\nWeekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535\nWeekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174\nVariables of the city characteristics are obtained from City Statistical Yearbooks. Time varying variables are observed daily for each city. Weekly average weather variables are averages over the preceding week\nWe rely on meteorological data to construct instrumental variables for the endogenous variables. 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. 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. 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. We use the inverse of the distance between the city’s centroid and each station as the weight. Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T80","span":{"begin":553,"end":554},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T81","span":{"begin":808,"end":809},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T82","span":{"begin":814,"end":815},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T83","span":{"begin":1127,"end":1128},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T84","span":{"begin":1133,"end":1134},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T85","span":{"begin":1175,"end":1176},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T86","span":{"begin":1519,"end":1520},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T87","span":{"begin":1746,"end":1747},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T88","span":{"begin":2342,"end":2343},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T89","span":{"begin":2389,"end":2390},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T90","span":{"begin":2436,"end":2437},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T91","span":{"begin":3571,"end":3574},"obj":"http://purl.obolibrary.org/obo/CLO_0007874"},{"id":"T92","span":{"begin":3887,"end":3890},"obj":"http://purl.obolibrary.org/obo/CLO_0007874"},{"id":"T93","span":{"begin":4443,"end":4446},"obj":"http://purl.obolibrary.org/obo/CLO_0007874"},{"id":"T94","span":{"begin":4754,"end":4757},"obj":"http://purl.obolibrary.org/obo/CLO_0007874"},{"id":"T95","span":{"begin":5047,"end":5059},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"},{"id":"T96","span":{"begin":5665,"end":5666},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Variables\nJanuary 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. All these data are reported by 32 provincial-level Health Commissions in China10. 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. 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). The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects. As robustness checks, we re-estimate models A and B without the cities in Hubei province. 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. Our main findings still hold (Appendix B).\nFig. 2 Number of daily new confirmed cases of COVID-19 in mainland China\nRegarding 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. 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.\nSince 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. 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. Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019. 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. 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. Table 1 presents the summary statistics of these variables. On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei. Compared with cities in Hubei province, cities outside Hubei have more doctors.\nFig. 3 Baidu index of population flow from Wuhan\nFig. 4 Destination shares in population flow from Wuhan\nTable 1 Summary statistics\nVariable N Mean Std dev. Min. Median Max.\nNon Hubei cities\nCity characteristics\nGDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549\nPopulation density, per km2 288 428.881 374.138 9.049 327.115 3444.092\n# of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000\nWeekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833\nWeekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570\nWeekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000\nWeekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791\nWeekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432\nWeekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129\nCities in Hubei province, excluding Wuhan\nCity characteristics\nGDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998\nPopulation density, per km2 16 416.501 220.834 24.409 438.820 846.263\n# of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393\nTime varying variables, Jan 19–Feb 1\nDaily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000\nWeekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889\nWeekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633\nWeekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439\nTime varying variables, Feb 1–Feb 29\nDaily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000\nWeekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413\nWeekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535\nWeekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174\nVariables of the city characteristics are obtained from City Statistical Yearbooks. Time varying variables are observed daily for each city. Weekly average weather variables are averages over the preceding week\nWe rely on meteorological data to construct instrumental variables for the endogenous variables. 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. 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. 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. We use the inverse of the distance between the city’s centroid and each station as the weight. Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date."}

LitCovid-sentences

LitCovid-PubTator