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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 11091-11095 Body_part denotes face http://purl.org/sig/ont/fma/fma24728

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 10803-10808 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T2 11091-11095 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T3 14153-14158 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T4 14672-14677 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 46-54 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 135-182 Disease denotes severe acute respiratory syndrome coronavirus 2 http://purl.obolibrary.org/obo/MONDO_0100096
T3 135-168 Disease denotes severe acute respiratory syndrome http://purl.obolibrary.org/obo/MONDO_0005091
T4 184-192 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T5 298-322 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T6 324-332 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 567-575 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T8 717-725 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T9 849-857 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 1034-1042 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T11 1148-1156 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T12 1215-1223 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T13 1363-1387 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T14 1389-1397 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T15 1578-1625 Disease denotes Severe acute respiratory syndrome coronavirus 2 http://purl.obolibrary.org/obo/MONDO_0100096
T16 1627-1635 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T17 1669-1677 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T18 1858-1866 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T19 2126-2134 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T20 2296-2304 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T21 2408-2416 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T22 2491-2499 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T23 2645-2653 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T24 2694-2702 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T25 2960-2969 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T26 3256-3264 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T27 3434-3442 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T28 3477-3485 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T29 3632-3641 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T30 3698-3706 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T31 3800-3808 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T32 3925-3933 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T33 3985-3993 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T34 4122-4130 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T35 4359-4367 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T36 4517-4525 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T37 4646-4654 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T38 4782-4790 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T39 5568-5576 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T40 5974-5982 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T41 6157-6165 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T42 6625-6633 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 6736-6744 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T44 6847-6855 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T45 6903-6911 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 7019-7027 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T47 7157-7181 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T48 7618-7626 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T49 7789-7797 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T50 7949-7957 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T51 8223-8232 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T52 8392-8400 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T53 8657-8665 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T54 8908-8916 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T55 9016-9024 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T56 9283-9291 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T57 9350-9374 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T58 9696-9704 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T59 9885-9893 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T60 9949-9957 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T61 10099-10107 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T62 10164-10172 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T63 10285-10293 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T64 10473-10481 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T65 10509-10517 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T66 10671-10679 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T67 10816-10824 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T68 11123-11131 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T69 11208-11232 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T70 11234-11242 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T71 11627-11635 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T72 11751-11759 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T73 12274-12282 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T74 12401-12409 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T75 12561-12569 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T76 12594-12602 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T77 12762-12770 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T78 12884-12892 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T79 13073-13081 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T80 13153-13161 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T81 13472-13480 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T82 13569-13577 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T83 13648-13656 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T84 14079-14087 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T85 14197-14211 Disease denotes viral diseases http://purl.obolibrary.org/obo/MONDO_0005108
T86 14499-14507 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T87 15311-15319 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 411-412 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T2 801-808 http://purl.obolibrary.org/obo/BFO_0000030 denotes objects
T3 1408-1411 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T4 1440-1443 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T5 1522-1524 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T6 1522-1524 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T7 1699-1705 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes humans
T8 1964-1969 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T9 2225-2226 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 2320-2321 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T11 2865-2872 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T12 2937-2941 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T13 3020-3025 http://purl.obolibrary.org/obo/CLO_0007373 denotes Lowen
T14 3044-3047 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T15 3110-3117 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T16 3132-3133 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 3167-3173 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T18 3174-3181 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T19 3719-3720 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T20 3727-3732 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T21 5434-5436 http://purl.obolibrary.org/obo/PR_000010213 denotes mb
T22 6251-6252 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 6261-6263 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T24 6558-6559 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T25 7188-7189 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 7220-7221 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T27 7328-7330 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T28 7639-7640 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T29 7877-7878 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T30 8427-8428 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T31 8606-8607 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T32 8800-8801 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T33 9185-9186 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T34 9757-9758 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T35 10045-10046 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T36 10122-10123 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T37 10340-10341 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T38 10527-10530 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T39 11091-11095 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T40 11164-11165 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T41 11274-11276 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T42 11352-11353 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T43 11423-11424 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T44 11876-11877 http://purl.obolibrary.org/obo/CLO_0001021 denotes b
T45 12358-12365 http://purl.obolibrary.org/obo/BFO_0000030 denotes objects
T46 12680-12687 http://purl.obolibrary.org/obo/BFO_0000030 denotes objects
T47 12977-12978 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T48 13659-13662 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T49 13663-13664 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T50 14000-14001 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T51 14117-14122 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T52 14144-14145 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T53 14636-14637 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T54 14663-14664 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T55 15284-15296 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T56 15455-15457 http://purl.obolibrary.org/obo/PR_000010213 denotes mb
T57 15684-15691 http://purl.obolibrary.org/obo/UBERON_0000982 denotes jointly
T58 15684-15691 http://purl.obolibrary.org/obo/UBERON_0004905 denotes jointly

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 1522-1524 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T2 2017-2019 Chemical denotes PM http://purl.obolibrary.org/obo/CHEBI_141444|http://purl.obolibrary.org/obo/CHEBI_16410|http://purl.obolibrary.org/obo/CHEBI_53551

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 14623-14632 http://purl.obolibrary.org/obo/GO_0006810 denotes transport

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-54 Sentence denotes Optimal temperature zone for the dispersal of COVID-19
T2 56-64 Sentence denotes Abstract
T3 65-251 Sentence denotes It is essential to know the environmental parameters within which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can survive to understand its global dispersal pattern.
T4 252-439 Sentence denotes We found that 60.0% of the confirmed cases of coronavirus disease 2019 (COVID-19) occurred in places where the air temperature ranged from 5 °C to 15 °C, with a peak in cases at 11.54 °C.
T5 440-566 Sentence denotes Moreover, approximately 73.8% of the confirmed cases were concentrated in regions with absolute humidity of 3 g/m3 to 10 g/m3.
T6 567-626 Sentence denotes SARS-CoV-2 appears to be spreading toward higher latitudes.
T7 627-810 Sentence denotes Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects).
T8 811-965 Sentence denotes These results strongly imply that the COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.
T9 967-985 Sentence denotes Graphical abstract
T10 987-997 Sentence denotes Highlights
T11 998-1118 Sentence denotes • We found that 60.0% of confirmed COVID-19 cases occurred in places where the air temperature ranged from 5°C to 15°C.
T12 1119-1207 Sentence denotes • Our results indicate that SARS-CoV-2 appears to be spreading toward higher latitudes.
T13 1208-1331 Sentence denotes • The COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.
T14 1333-1348 Sentence denotes 1 Introduction
T15 1349-1577 Sentence denotes Recently, the coronavirus disease 2019 (COVID-19) outbreak has quickly spread globally and has shown significant impact on public health and economy (Chinazzi et al., 2020; Li et al., 2020; Rothe et al., 2020; Yan et al., 2020).
T16 1578-1820 Sentence denotes Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative factor for the COVID-19 pandemic, can infect humans via several routes, such as inhalation of respiratory droplets or aerosols and contact with contaminated surfaces.
T17 1821-1918 Sentence denotes Previous studies have indicated that SARS-CoV-2 can be detected in indoor as well as outdoor air.
T18 1919-2090 Sentence denotes Preliminary evidence clearly proved that the virus could cluster with outdoor particulate matter (PM) under certain circumstances (Jiang et al., 2020; Setti et al., 2020).
T19 2091-2265 Sentence denotes Particularly, it is suggested that SARS-CoV-2 may have the potential to spread through aerosols, which can be produced by speaking at a normal volume (Anfinrud et al., 2020).
T20 2266-2356 Sentence denotes In addition, it is found that SARS-CoV-2 can stay for a considerably long time in the air.
T21 2357-2469 Sentence denotes The aerodynamic characteristics and propagation of SARS-CoV-2 in aerosols have been reported (Liu et al., 2020).
T22 2470-2587 Sentence denotes It was reported that SARS-CoV-2 was still found in the Diamond Princess cruise ship 17 days after people disembarked.
T23 2588-2703 Sentence denotes Therefore, it is essential to understand the survival of SARS-CoV-2 in the ambient environment to prevent COVID-19.
T24 2704-2802 Sentence denotes This information is very useful not only for the policymakers but also for the general population.
T25 2803-2943 Sentence denotes Many previous investigations have shown that the viability of viruses is strongly dependent on temperature (Tang, 2009; Huang et al., 2018).
T26 2944-3040 Sentence denotes Transmission of influenza can be reduced under high temperature conditions (Lowen et al., 2008).
T27 3041-3202 Sentence denotes It has been proven that high temperatures can effectively deactivate viruses, resulting in a large reduction in the amount of active viruses (Park et al., 2020).
T28 3203-3359 Sentence denotes Recent studies have shown that the rate of confirmed COVID-19 cases is closely related to temperature (Triplett, 2020; Luo et al., 2020; Xie and Zhu, 2020).
T29 3360-3495 Sentence denotes It is of great importance to determine the preferred temperature range of SARS-CoV-2 in order to help us prevent the COVID-19 outbreak.
T30 3496-3642 Sentence denotes Particularly, hospitals and densely populated areas can be informed about optimal temperature ranges to reduce the probability of cross-infection.
T31 3643-3733 Sentence denotes However, such knowledge is not currently available for SARS-CoV-2, which is a novel virus.
T32 3734-3818 Sentence denotes Therefore, it cannot be utilized yet to prevent the spread of the COVID-19 pandemic.
T33 3819-3934 Sentence denotes To address these knowledge gaps, we investigated the impact of ambient temperature on global dispersal of COVID-19.
T34 3935-4137 Sentence denotes The relationship between daily confirmed cases of COVID-19 and meteorological conditions (temperature and humidity) was studied using the data of approximately 3,750,000 global confirmed COVID-19 cases.
T35 4138-4211 Sentence denotes Section 2 briefly introduces the data and the methods used in this paper.
T36 4212-4315 Sentence denotes The results and the discussion are presented in Section 3 and the conclusion is presented in Section 4.
T37 4317-4336 Sentence denotes 2 Data and methods
T38 4337-4478 Sentence denotes In this study, global COVID-19 case datasets were downloaded from the Center for Systems Science and Engineering at Johns Hopkins University.
T39 4479-4624 Sentence denotes Data of more than 3,750,000 confirmed COVID-19 cases from 185 countries/regions from January 21, 2020 to May 6, 2020 were included in this paper.
T40 4625-4770 Sentence denotes The daily numbers of COVID-19 cases are being reported for each province or state in several large countries such as China and the United States.
T41 4771-4862 Sentence denotes Zonal mean COVID-19 cases on each day were calculated and fitted using normal distribution.
T42 4863-4974 Sentence denotes The center and the standard deviation of the fitted curve were used to show the most serious dispersal regions.
T43 4975-5142 Sentence denotes Corresponding observational data of daily air temperature and dew-point temperature were obtained from the Weather Underground website (https://www.wunderground.com/).
T44 5143-5261 Sentence denotes The relative humidity and the absolute humidity were calculated using daily air temperature and dew-point temperature.
T45 5262-5374 Sentence denotes Maximum/minimum temperatures and temperature difference on each day were also calculated for each state/country.
T46 5375-5587 Sentence denotes Furthermore, time-series of zonal mean temperature at 1000 mb in 2019 was calculated from National Centers for Environmental Prediction reanalysis data to study the trend of approximate global COVID-19 dispersal.
T47 5588-5806 Sentence denotes In addition, the Gridded Population of the World from 1980 to 2010 was developed by the Center for Global Environmental Research at the National Institute for Environmental Studies, Japan (Murakami and Yamagata, 2019).
T48 5807-5907 Sentence denotes We calculated zonal means of gridded populations to show the locations with high population density.
T49 5909-5934 Sentence denotes 3 Results and discussion
T50 5935-6049 Sentence denotes The global probability distribution of COVID-19 cases with respect to the ambient temperature is shown in Fig. 1 .
T51 6050-6097 Sentence denotes The temperature interval at each panel is 1 °C.
T52 6098-6267 Sentence denotes The results clearly illustrate that 60.0% of the confirmed COVID-19 cases were found in places where the air temperature ranged from 5 °C to 15 °C, with a peak at 11 °C.
T53 6268-6379 Sentence denotes However, there were few confirmed cases located at cold (lower that 0 °C) and hot (greater than 30 °C) regions.
T54 6380-6557 Sentence denotes Fitting the results using normal distribution, we found that the mean and the standard deviation of the fitted normal distribution curve were 11.54 °C and 5.47 °C, respectively.
T55 6558-6814 Sentence denotes A previous study analyzed the relationship between daily confirmed COVID-19 cases and air temperature from 122 cities in China (Xie and Zhu, 2020) and pointed out that confirmed COVID-19 cases increased by 4.861%/°C for ambient temperature lower than 3 °C.
T56 6815-6988 Sentence denotes Based on the analysis of global COVID-19 cases, our results demonstrated that confirmed COVID-19 cases increased by 27,536 cases/°C for ambient temperature lower than 10 °C.
T57 6989-7108 Sentence denotes Thus, the rate of increase in COVID-19 cases induced by temperature may have been underestimated by the previous study.
T58 7109-7352 Sentence denotes Fig. 1 Relationship of daily confirmed cases of coronavirus disease 2019 with (a) corresponding temperatures, (b) daily temperature difference, (c) maximum temperature, and (d) minimum temperature globally from January 22, 2020 to May 6, 2020.
T59 7353-7546 Sentence denotes The blue line represents the fitted normal distribution curve. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T60 7547-7749 Sentence denotes To understand the impact of ambient temperature on dispersal of global COVID-19 pandemic in a better way, we investigated the relationship between daily confirmed cases and maximum/minimum temperatures.
T61 7750-7876 Sentence denotes It was observed that most of the daily COVID-19 cases were located in regions with maximum temperature range of 5 °C to 30 °C.
T62 7877-8041 Sentence denotes A similar study by Triplett (2020) indicated that the rate of confirmed COVID-19 cases will be significantly reduced when maximum temperature reaches above 22.5 °C.
T63 8042-8146 Sentence denotes Moreover, the cases were mainly concentrated in regions with minimum temperature range of 0 °C to 15 °C.
T64 8147-8233 Sentence denotes It is well known that large daily temperature difference may easily trigger influenza.
T65 8234-8335 Sentence denotes Therefore, we investigated the relationship between daily confirmed cases and temperature difference.
T66 8336-8566 Sentence denotes The results showed that rapid increase in the number of COVID-19 cases was associated with a daily temperature difference threshold of 8 °C and the number of cases decreased when daily temperature difference was greater than 8 °C.
T67 8567-8699 Sentence denotes These results indicated that there was a nonlinear association between confirmed cases of COVID-19 and daily temperature difference.
T68 8700-8824 Sentence denotes There was an obvious relationship between the number of confirmed cases and relative humidity, with a peak at 65% (Fig. 2 ).
T69 8825-8948 Sentence denotes The distribution was much broader (30% to 100%) when compared with distribution of COVID-19 cases according to temperature.
T70 8949-9062 Sentence denotes Additionally, we analyzed the variation in the number of confirmed COVID-19 cases according to absolute humidity.
T71 9063-9202 Sentence denotes Approximately 73.8% of the cases were concentrated in regions with absolute humidity ranging from 3 g/m3 to 10 g/m3, with a peak at 5 g/m3.
T72 9203-9301 Sentence denotes This result suggests that humid conditions were conducive for the spread of the COVID-19 pandemic.
T73 9302-9441 Sentence denotes Fig. 2 Relationship of daily confirmed cases of coronavirus disease 2019 with (left) daily relative humidity and (right) absolute humidity.
T74 9442-9635 Sentence denotes The blue line represents the fitted normal distribution curve. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T75 9636-9737 Sentence denotes Fig. 3 presents the time-series zonal distribution of daily COVID-19 cases and their dispersal trend.
T76 9738-9916 Sentence denotes As shown in Fig. 3(a), the temperature zone (5 °C–15 °C) and the center of the fitted normal distribution curve of the zonal mean daily cumulative COVID-19 cases were coincident.
T77 9917-10025 Sentence denotes The center of the zone denoting COVID-19 cases moved toward higher latitude along with the temperature zone.
T78 10026-10118 Sentence denotes Notably, there was a breakpoint on March 14, 2020 due to the outbreak of COVID-19 in Europe.
T79 10119-10246 Sentence denotes As a result, the center of the zone denoting COVID-19 cases switched to 42.39°N and its standard deviation became much smaller.
T80 10247-10395 Sentence denotes Clearly, most of the cases (68.2%) of COVID-19 occurred at higher latitudes, spreading along a path where the temperature ranged from 5 °C to 15 °C.
T81 10396-10482 Sentence denotes This finding confirms that air temperature truly affects the distribution of COVID-19.
T82 10483-10582 Sentence denotes It is noteworthy that the COVID-19 pandemic has not spread to areas with high population densities.
T83 10583-10787 Sentence denotes Hence, we predict that the center of the fitted normal distribution curve of zonal mean COVID-19 cases will continually move to higher latitudes along the temperature zone between 5 °C to 15 °C over time.
T84 10788-10937 Sentence denotes Therefore, the scale of the COVID-19 pandemic will be substantially reduced in early May and might recur in large mid-latitude cities in autumn 2020.
T85 10938-11011 Sentence denotes It is very important to pay more attention to places at higher latitudes.
T86 11012-11155 Sentence denotes In addition, mid-latitude locations with higher population densities will also face the possibility of another COVID-19 outbreak in the autumn.
T87 11156-11298 Sentence denotes Fig. 3 (a) Relationship between cumulative cases of coronavirus disease 2019 (COVID-19) and temperatures from January 22, 2020 to May 6, 2020.
T88 11299-11494 Sentence denotes The orange zone represents the latitudinal zone with a mean surface temperature between 5 °C and 15 °C in 2019 according to a reanalysis of the National Centers for Environmental Prediction data.
T89 11495-11651 Sentence denotes Blue lines (red points) represent the standard derivation (center) of the fitted normal distribution curve of zonal mean cumulative COVID-19 cases each day.
T90 11652-11735 Sentence denotes The illustration is an example of the normal distribution fitted on March 13, 2020.
T91 11736-12183 Sentence denotes Notably, daily COVID-19 cases in several countries such as China, the United States, and Canada were counted separately for each province. (b) Zonal mean of the gridded populations from 1980 to 2010 developed by the Center for Global Environmental Research at the National Institute for Environmental Studies, Japan. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T92 12184-12367 Sentence denotes Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects).
T93 12368-12572 Sentence denotes Even though the dispersal of the COVID-19 outbreak is affected by many countermeasures and medical conditions, our results confirm that there is an optimal temperature zone for the survival of SARS-CoV-2.
T94 12573-12714 Sentence denotes The concentration of SARS-CoV-2 can markedly increase in the ambient environment including the surfaces of objects in this temperature range.
T95 12715-12811 Sentence denotes It is worth recognizing that the spread of the COVID-19 outbreak is affected by several factors.
T96 12812-12902 Sentence denotes In the present study, we investigated the natural factors affecting the COVID-19 pandemic.
T97 12903-13021 Sentence denotes The analysis of large datasets (samples) yielded significant results with a high degree of confidence in the findings.
T98 13022-13114 Sentence denotes These findings are important for the prediction of COVID-19 transmission in the near future.
T99 13115-13224 Sentence denotes We cannot rely on the conjecture that COVID-19 outbreak will stop with increase in the temperature in summer.
T100 13225-13416 Sentence denotes Undoubtedly, control strategies including school closure and social distancing have reduced the number of total cases considerably (Prem et al., 2020; Luo et al., 2020; Kissler et al., 2020).
T101 13418-13431 Sentence denotes 4 Conclusion
T102 13432-13621 Sentence denotes We investigated the global dispersal of COVID-19 according to ambient temperature using data of approximately 3,750,000 global confirmed COVID-19 cases from January 21, 2020 to May 6, 2020.
T103 13622-13878 Sentence denotes The results revealed that SARS-CoV-2 has a greater chance of survival in the ambient environment within the optimal temperature zone, suggesting that more attention should be paid to preventive measures when the air temperatures are between 5 °C and 15 °C.
T104 13879-14017 Sentence denotes Moreover, about 73.8% of the confirmed cases were concentrated in the absolute humidity range of 3 g/m3 to 10 g/m3, with a peak at 5 g/m3.
T105 14018-14212 Sentence denotes The present study provides information about the survival of SARS-CoV-2, the transportation of the virus in the atmosphere on a global scale, and the modeling of the dispersal of viral diseases.
T106 14213-14366 Sentence denotes Our findings are important for public health and suggest that air temperatures in hospitals and at home should be set outside the range of 5 °C to 15 °C.
T107 14367-14526 Sentence denotes Furthermore, measures to prevent the disease should be implemented in areas within the optimal climatic zone, where the survival of SARS-CoV-2 may be enhanced.
T108 14527-14678 Sentence denotes Policy makers need to establish an early warning system for pandemics by considering bioaerosol transport on a transcontinental or even a global scale.
T109 14679-14754 Sentence denotes Such system should include weather forecasting and climatological analysis.
T110 14756-14777 Sentence denotes Author's contribution
T111 14778-14780 Sentence denotes J.
T112 14781-14854 Sentence denotes H. contributed to the conceptualization, funding acquisition and writing.
T113 14855-14857 Sentence denotes Z.
T114 14858-14919 Sentence denotes H. contributed to the data curation, methodology and writing.
T115 14920-14922 Sentence denotes Q.
T116 14923-14929 Sentence denotes G., P.
T117 14930-14936 Sentence denotes D., H.
T118 14937-14947 Sentence denotes L., and Q.
T119 14948-14996 Sentence denotes D. contributed to the data curation and writing.
T120 14998-15031 Sentence denotes Declaration of competing interest
T121 15032-15202 Sentence denotes The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
T122 15204-15219 Sentence denotes Acknowledgments
T123 15220-15303 Sentence denotes Surface air temperature was provided by the Weather Underground Organization (WUO).
T124 15304-15423 Sentence denotes Global COVID-19 cases data were provided by the Center for systems science and engineering at Johns Hopkins University.
T125 15424-15497 Sentence denotes Zonal mean temperature at 1000 mb was provided from NCEP reanalysis data.
T126 15498-15660 Sentence denotes Gridded populations during 1980-2010 was provided from the Center for Global Environmental Research (CGER) at National Institute for Environmental Studies, Japan.
T127 15662-15669 Sentence denotes Funding
T128 15670-15925 Sentence denotes This work was jointly supported by the 10.13039/501100001809National Science Foundation of China (41521004, 41705077, and 41875029), and Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development (2019ZX-06).

2_test

Id Subject Object Predicate Lexical cue
32479958-32003551-139438825 1553-1557 32003551 denotes 2020
32479958-32132184-139438826 1571-1575 32132184 denotes 2020
32479958-18367530-139438827 3034-3038 18367530 denotes 2008
32479958-31631558-139438828 3196-3200 31631558 denotes 2020