School of Mathematics and Statistics, Carleton University, Ottawa, K1S 5B6, Canada.
Centre de Recherches Mathématiques, Université de Montréal, Montréal, H3T 1J4, Canada.
Sci Rep. 2021 Dec 9;11(1):23763. doi: 10.1038/s41598-021-02982-w.
We derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to some optimization algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate (1) the total cases into 14 groups corresponding to 14 incubation periods, (2) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and (3) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval (CI) 22.00-22.27), and the 90th percentile is 28.91 days (95% CI 28.71-29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI 20.92-21.12), and a long recovery period, mean 38.88 days (95% CI 38.61-39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.
我们基于一组具有广泛时滞的耦合时滞微分方程,推导了一个新的大规模隔室模型,以估计 COVID-19 以及更一般的任何传染病的潜伏期、恢复期和死亡期。这得益于应用于公开的确诊病例、康复病例和死亡人数数据库的一些优化算法。为此,我们将(1)总病例分为 14 组,对应 14 个潜伏期,(2)康复病例分为 406 组,对应潜伏期和恢复期的组合,以及(3)死亡病例分为 406 组,对应潜伏期和死亡期的组合。在本文中,我们重点研究了恢复期和死亡期及其与潜伏期的相关性。我们得到的估计平均恢复期为 22.14 天(95%置信区间(CI)22.00-22.27),第 90 个百分位数为 28.91 天(95% CI 28.71-29.13),这与统计支持的研究一致。双模态伽马分布表明,有两组康复个体具有较短的恢复期,平均 21.02 天(95% CI 20.92-21.12),和较长的恢复期,平均 38.88 天(95% CI 38.61-39.15)。我们的研究表明,死亡期和恢复期的特征相似。从双变量分析中,我们观察到在潜伏期和恢复期方面,康复个体具有高概率域。在分析潜伏期和死亡期的双变量分布时,也得到了类似的死亡域。