The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were...
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Autor*in:	Jing Li [verfasserIn] Lishi Wang [verfasserIn] Sumin Guo [verfasserIn] Ning Xie [verfasserIn] Lan Yao [verfasserIn] Yanhong Cao [verfasserIn] Sara W. Day [verfasserIn] Scott C. Howard [verfasserIn] J. Carolyn Graff [verfasserIn] Tianshu Gu [verfasserIn] Jiafu Ji [verfasserIn] Weikuan Gu [verfasserIn] Dianjun Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation

Übergeordnetes Werk:	In: Data in Brief - Elsevier, 2015, 30(2020), Seite 105619-
Übergeordnetes Werk:	volume:30 ; year:2020 ; pages:105619-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dib.2020.105619

Katalog-ID:	DOAJ078122058

Internformat


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520			\|a The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.
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allfields	10.1016/j.dib.2020.105619 doi (DE-627)DOAJ078122058 (DE-599)DOAJ9795d20ad87a44c0993e29b95a228932 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Jing Li verfasserin aut The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA. PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General) Lishi Wang verfasserin aut Sumin Guo verfasserin aut Ning Xie verfasserin aut Lan Yao verfasserin aut Yanhong Cao verfasserin aut Sara W. Day verfasserin aut Scott C. Howard verfasserin aut J. Carolyn Graff verfasserin aut Tianshu Gu verfasserin aut Jiafu Ji verfasserin aut Weikuan Gu verfasserin aut Dianjun Sun verfasserin aut In Data in Brief Elsevier, 2015 30(2020), Seite 105619- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:30 year:2020 pages:105619- https://doi.org/10.1016/j.dib.2020.105619 kostenfrei https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340920305138 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2020 105619-
spelling	10.1016/j.dib.2020.105619 doi (DE-627)DOAJ078122058 (DE-599)DOAJ9795d20ad87a44c0993e29b95a228932 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Jing Li verfasserin aut The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA. PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General) Lishi Wang verfasserin aut Sumin Guo verfasserin aut Ning Xie verfasserin aut Lan Yao verfasserin aut Yanhong Cao verfasserin aut Sara W. Day verfasserin aut Scott C. Howard verfasserin aut J. Carolyn Graff verfasserin aut Tianshu Gu verfasserin aut Jiafu Ji verfasserin aut Weikuan Gu verfasserin aut Dianjun Sun verfasserin aut In Data in Brief Elsevier, 2015 30(2020), Seite 105619- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:30 year:2020 pages:105619- https://doi.org/10.1016/j.dib.2020.105619 kostenfrei https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340920305138 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2020 105619-
allfields_unstemmed	10.1016/j.dib.2020.105619 doi (DE-627)DOAJ078122058 (DE-599)DOAJ9795d20ad87a44c0993e29b95a228932 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Jing Li verfasserin aut The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA. PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General) Lishi Wang verfasserin aut Sumin Guo verfasserin aut Ning Xie verfasserin aut Lan Yao verfasserin aut Yanhong Cao verfasserin aut Sara W. Day verfasserin aut Scott C. Howard verfasserin aut J. Carolyn Graff verfasserin aut Tianshu Gu verfasserin aut Jiafu Ji verfasserin aut Weikuan Gu verfasserin aut Dianjun Sun verfasserin aut In Data in Brief Elsevier, 2015 30(2020), Seite 105619- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:30 year:2020 pages:105619- https://doi.org/10.1016/j.dib.2020.105619 kostenfrei https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340920305138 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2020 105619-
allfieldsGer	10.1016/j.dib.2020.105619 doi (DE-627)DOAJ078122058 (DE-599)DOAJ9795d20ad87a44c0993e29b95a228932 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Jing Li verfasserin aut The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA. PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General) Lishi Wang verfasserin aut Sumin Guo verfasserin aut Ning Xie verfasserin aut Lan Yao verfasserin aut Yanhong Cao verfasserin aut Sara W. Day verfasserin aut Scott C. Howard verfasserin aut J. Carolyn Graff verfasserin aut Tianshu Gu verfasserin aut Jiafu Ji verfasserin aut Weikuan Gu verfasserin aut Dianjun Sun verfasserin aut In Data in Brief Elsevier, 2015 30(2020), Seite 105619- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:30 year:2020 pages:105619- https://doi.org/10.1016/j.dib.2020.105619 kostenfrei https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340920305138 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2020 105619-
allfieldsSound	10.1016/j.dib.2020.105619 doi (DE-627)DOAJ078122058 (DE-599)DOAJ9795d20ad87a44c0993e29b95a228932 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Jing Li verfasserin aut The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA. PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General) Lishi Wang verfasserin aut Sumin Guo verfasserin aut Ning Xie verfasserin aut Lan Yao verfasserin aut Yanhong Cao verfasserin aut Sara W. Day verfasserin aut Scott C. Howard verfasserin aut J. Carolyn Graff verfasserin aut Tianshu Gu verfasserin aut Jiafu Ji verfasserin aut Weikuan Gu verfasserin aut Dianjun Sun verfasserin aut In Data in Brief Elsevier, 2015 30(2020), Seite 105619- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:30 year:2020 pages:105619- https://doi.org/10.1016/j.dib.2020.105619 kostenfrei https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340920305138 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2020 105619-
language	English
source	In Data in Brief 30(2020), Seite 105619- volume:30 year:2020 pages:105619-
sourceStr	In Data in Brief 30(2020), Seite 105619- volume:30 year:2020 pages:105619-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation Computer applications to medicine. Medical informatics Science (General)
isfreeaccess_bool	true
container_title	Data in Brief
authorswithroles_txt_mv	Jing Li @@aut@@ Lishi Wang @@aut@@ Sumin Guo @@aut@@ Ning Xie @@aut@@ Lan Yao @@aut@@ Yanhong Cao @@aut@@ Sara W. Day @@aut@@ Scott C. Howard @@aut@@ J. Carolyn Graff @@aut@@ Tianshu Gu @@aut@@ Jiafu Ji @@aut@@ Weikuan Gu @@aut@@ Dianjun Sun @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	797838937
id	DOAJ078122058
language_de	englisch
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callnumber-first	R - Medicine
author	Jing Li
spellingShingle	Jing Li misc R858-859.7 misc Q1-390 misc PIBA misc Coronavirus misc COVID-19 misc Death Rate misc Normal distribution misc Estimation misc Computer applications to medicine. Medical informatics misc Science (General) The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19
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topic_title	R858-859.7 Q1-390 The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19 PIBA Coronavirus COVID-19 Death Rate Normal distribution Estimation
topic	misc R858-859.7 misc Q1-390 misc PIBA misc Coronavirus misc COVID-19 misc Death Rate misc Normal distribution misc Estimation misc Computer applications to medicine. Medical informatics misc Science (General)
topic_unstemmed	misc R858-859.7 misc Q1-390 misc PIBA misc Coronavirus misc COVID-19 misc Death Rate misc Normal distribution misc Estimation misc Computer applications to medicine. Medical informatics misc Science (General)
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title	The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19
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title_full	The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19
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author_browse	Jing Li Lishi Wang Sumin Guo Ning Xie Lan Yao Yanhong Cao Sara W. Day Scott C. Howard J. Carolyn Graff Tianshu Gu Jiafu Ji Weikuan Gu Dianjun Sun
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title_sort	data set for patient information based algorithm to predict mortality cause by covid-19
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title_auth	The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19
abstract	The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.
abstractGer	The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.
abstract_unstemmed	The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis.A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates.Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.
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title_short	The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19
url	https://doi.org/10.1016/j.dib.2020.105619 https://doaj.org/article/9795d20ad87a44c0993e29b95a228932 http://www.sciencedirect.com/science/article/pii/S2352340920305138 https://doaj.org/toc/2352-3409
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up_date	2024-07-03T16:04:46.792Z
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The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19

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