Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic
Objectives Assessment of recuperation and death times of a population inflicted by an epidemic has only been feasible through studying a sample of individuals via time-to-event analysis, which requires identified participants. Therefore, we aimed to introduce an original model to estimate the averag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-01-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/1/e065487.full |
| _version_ | 1811176151678189568 |
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| author | Ali Rezania Elaheh Ghorbani Davood Hassanian-Moghaddam Farnaz Faeghi Hossein Hassanian-Moghaddam |
| author_facet | Ali Rezania Elaheh Ghorbani Davood Hassanian-Moghaddam Farnaz Faeghi Hossein Hassanian-Moghaddam |
| author_sort | Ali Rezania |
| collection | DOAJ |
| description | Objectives Assessment of recuperation and death times of a population inflicted by an epidemic has only been feasible through studying a sample of individuals via time-to-event analysis, which requires identified participants. Therefore, we aimed to introduce an original model to estimate the average recovery/death times of infected population of contagious diseases without the need to undertake survival analysis and just through the data of unidentified infected, recovered and dead cases.Design Cross-sectional study.Setting An internet source that asserted from official sources of each government. The model includes two techniques—curve fitting and optimisation problems. First, in the curve fitting process, the data of the three classes are simultaneously fitted to functions with defined constraints to derive the average times. In the optimisation problems, data are directly fed to the technique to achieve the average times. Further, the model is applied to the available data of COVID-19 of 200 million people throughout the globe.Results The average times obtained by the two techniques indicated conformity with one another showing p values of 0.69, 0.51, 0.48 and 0.13 with one, two, three and four surges in our timespan, respectively. Two types of irregularity are detectable in the data, significant difference between the infected population and the sum of the recovered and deceased population (discrepancy) and abrupt increase in the cumulative distributions (step). Two indices, discrepancy index (DI) and error of fit index (EI), are developed to quantify these irregularities and correlate them with the conformity of the time averages obtained by the two techniques. The correlations between DI and EI and the quantified conformity of the results were −0.74 and −0.93, respectively.Conclusion The results of statistical analyses point out that the proposed model is suitable to estimate the average times between recovery and death. |
| first_indexed | 2024-04-10T19:48:40Z |
| format | Article |
| id | doaj.art-988a4bf76f6a424887c6f47c314f1f34 |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-04-10T19:48:40Z |
| publishDate | 2023-01-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-988a4bf76f6a424887c6f47c314f1f342023-01-28T18:00:10ZengBMJ Publishing GroupBMJ Open2044-60552023-01-0113110.1136/bmjopen-2022-065487Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemicAli Rezania0Elaheh Ghorbani1Davood Hassanian-Moghaddam2Farnaz Faeghi3Hossein Hassanian-Moghaddam4Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, IranDepartment of Polymer Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IranDepartment of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, IranDepartment of Polymer Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IranSocial Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IranObjectives Assessment of recuperation and death times of a population inflicted by an epidemic has only been feasible through studying a sample of individuals via time-to-event analysis, which requires identified participants. Therefore, we aimed to introduce an original model to estimate the average recovery/death times of infected population of contagious diseases without the need to undertake survival analysis and just through the data of unidentified infected, recovered and dead cases.Design Cross-sectional study.Setting An internet source that asserted from official sources of each government. The model includes two techniques—curve fitting and optimisation problems. First, in the curve fitting process, the data of the three classes are simultaneously fitted to functions with defined constraints to derive the average times. In the optimisation problems, data are directly fed to the technique to achieve the average times. Further, the model is applied to the available data of COVID-19 of 200 million people throughout the globe.Results The average times obtained by the two techniques indicated conformity with one another showing p values of 0.69, 0.51, 0.48 and 0.13 with one, two, three and four surges in our timespan, respectively. Two types of irregularity are detectable in the data, significant difference between the infected population and the sum of the recovered and deceased population (discrepancy) and abrupt increase in the cumulative distributions (step). Two indices, discrepancy index (DI) and error of fit index (EI), are developed to quantify these irregularities and correlate them with the conformity of the time averages obtained by the two techniques. The correlations between DI and EI and the quantified conformity of the results were −0.74 and −0.93, respectively.Conclusion The results of statistical analyses point out that the proposed model is suitable to estimate the average times between recovery and death.https://bmjopen.bmj.com/content/13/1/e065487.full |
| spellingShingle | Ali Rezania Elaheh Ghorbani Davood Hassanian-Moghaddam Farnaz Faeghi Hossein Hassanian-Moghaddam Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic BMJ Open |
| title | Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic |
| title_full | Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic |
| title_fullStr | Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic |
| title_full_unstemmed | Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic |
| title_short | Novel model prediction time-to-event analysis: data validation and estimation of 200 million cases in the global COVID-19 epidemic |
| title_sort | novel model prediction time to event analysis data validation and estimation of 200 million cases in the global covid 19 epidemic |
| url | https://bmjopen.bmj.com/content/13/1/e065487.full |
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