Laboratoire L3MA, DSI et IUT, Université des Antilles, Schoelcher, Martinique.
Institute 2iE, B.P. 594, Ouagadougou, Burkina Faso.
Epidemiol Infect. 2020 Sep 22;148:e221. doi: 10.1017/S0950268820002162.
The main objective of this paper is to address the following question: are the containment measures imposed by most of the world governments effective and sufficient to stop the epidemic of COVID-19 beyond the lock-down period? In this paper, we propose a mathematical model which allows us to investigate and analyse this problem. We show by means of the reproductive number, ${\cal R}_0$ that the containment measures appear to have slowed the growth of the outbreak. Nevertheless, these measures remain only effective as long as a very large fraction of population, p, greater than the critical value $1-1/{\cal R}_0$ remains confined. Using French current data, we give some simulation experiments with five scenarios including: (i) the validation of model with p estimated to 93%, (ii) the study of the effectiveness of containment measures, (iii) the study of the effectiveness of the large-scale testing, (iv) the study of the social distancing and wearing masks measures and (v) the study taking into account the combination of the large-scale test of detection of infected individuals and the social distancing with linear progressive easing of restrictions. The latter scenario was shown to be effective at overcoming the outbreak if the transmission rate decreases to 75% and the number of tests of detection is multiplied by three. We also noticed that if the measures studied in our five scenarios are taken separately then the second wave might occur at least as far as the parameter values remain unchanged.
世界上大多数政府实施的遏制措施在封锁期之外是否有效且足以阻止 COVID-19 疫情的蔓延?在本文中,我们提出了一个数学模型,通过该模型可以研究和分析这个问题。我们通过基本再生数 ${\cal R}_0$ 表明,遏制措施似乎已经减缓了疫情的增长。然而,只要非常大比例的人口(大于临界值 $1-1/{\cal R}_0$)仍然受到限制,这些措施才是有效的。利用法国当前的数据,我们进行了五个场景的模拟实验,包括:(i)使用估计为 93%的 p 值对模型进行验证,(ii)研究遏制措施的有效性,(iii)研究大规模检测的有效性,(iv)研究社交隔离和佩戴口罩措施的有效性,以及(v)研究将大规模检测感染个体与社交隔离和逐步放宽限制相结合的措施。如果传播率降低到 75%并且检测数量增加三倍,那么后一种情况被证明可以有效地克服疫情。我们还注意到,如果在我们的五个场景中分别实施所研究的措施,那么只要参数值保持不变,第二波疫情至少会发生。