Fanelli Duccio, Piazza Francesco
Dipartimento di Fisica e Astronomia, Universitá di Firenze, INFN and CSDC, Via Sansone 1, Sesto Fiorentino 50019, Firenze, Italy.
Centre de Biophysique Moléculaire (CBM), CNRS-UPR 4301, Rue C. Sadron, Orléans 45071, France.
Chaos Solitons Fractals. 2020 May;134:109761. doi: 10.1016/j.chaos.2020.109761. Epub 2020 Mar 21.
In this note we analyze the temporal dynamics of the coronavirus disease 2019 outbreak in China, Italy and France in the time window . A first analysis of simple day-lag maps points to some universality in the epidemic spreading, suggesting that simple mean-field models can be meaningfully used to gather a quantitative picture of the epidemic spreading, and notably the height and time of the peak of confirmed infected individuals. The analysis of the same data within a simple susceptible-infected-recovered-deaths model indicates that the kinetic parameter that describes the rate of recovery seems to be the same, irrespective of the country, while the infection and death rates appear to be more variable. The model places the peak in Italy around March 21 2020, with a peak number of infected individuals of about 26000 (not including recovered and dead) and a number of deaths at the end of the epidemics of about 18,000. Since the confirmed cases are believed to be between 10 and 20% of the real number of individuals who eventually get infected, the apparent mortality rate of COVID-19 falls between 4% and 8% in Italy, while it appears substantially lower, between 1% and 3% in China. Based on our calculations, we estimate that 2500 ventilation units should represent a fair figure for the peak requirement to be considered by health authorities in Italy for their strategic planning. Finally, a simulation of the effects of drastic containment measures on the outbreak in Italy indicates that a reduction of the infection rate indeed causes a quench of the epidemic peak. However, it is also seen that the infection rate needs to be cut down drastically and quickly to observe an appreciable decrease of the epidemic peak and mortality rate. This appears only possible through a concerted and disciplined, albeit painful, effort of the population as a whole.
在本报告中,我们分析了2019年冠状病毒病在中国、意大利和法国于特定时间窗口内爆发的时间动态。对简单日滞后地图的初步分析表明,疫情传播存在一些普遍性,这表明简单的平均场模型可有效地用于获取疫情传播的定量情况,尤其是确诊感染个体峰值的高度和时间。在一个简单的易感-感染-康复-死亡模型中对相同数据进行分析表明,描述康复率的动力学参数似乎相同,与国家无关,而感染率和死亡率似乎更具变异性。该模型预测意大利的峰值出现在2020年3月21日左右,感染个体峰值约为26000例(不包括康复和死亡病例),疫情结束时死亡人数约为18000例。由于确诊病例被认为占最终感染个体实际数量的10%至20%,因此意大利COVID-19的表观死亡率在4%至8%之间,而在中国则明显较低,在1%至3%之间。根据我们的计算,我们估计2500个通风设备应是意大利卫生当局在其战略规划中考虑的峰值需求的合理数字。最后,对意大利采取严厉遏制措施对疫情爆发影响的模拟表明,感染率的降低确实会导致疫情峰值的消退。然而,可以看到,感染率需要大幅且迅速降低才能观察到疫情峰值和死亡率的明显下降。这似乎只有通过全体民众协调一致且自律(尽管痛苦)的努力才有可能实现。