论文标题
Covid-19的关键社区规模 - 一种基于模型的方法,以提供锁定背后的理由
Critical community size for COVID-19 -- a model based approach to provide a rationale behind the lockdown
论文作者
论文摘要
背景:限制性的大规模隔离或锁定已被实施,是与19日作战的最重要的控制措施。许多国家已经执行了2-4周的锁定,并根据当前的疾病情况延长了这一时期。 COVID-19的估计通信性的14天很可能促使了这一决定。但是,如果固定时间(随后爆发之后),易感人群降至一定阈值以下,则感染自然会在小社区中死亡,除非Bartlett在1957年提出了该疾病。该阈值被称为关键社区规模(CCS)。方法:我们提出了一个SEIR模型,该模型解释了Covid-19疾病动力学。使用我们的模型,我们计算了特定于国家 /地区的预期灭绝时间(TTE)和CCS,这些时间基本上可以确定所需的锁定天数和隔离人群大小的理想数量。调查结果:随着给定的乡村死亡率,恢复和其他参数的率,我们已经确定,如果在某个地方易感人群的总数下降到CCS以下,则感染将在TTE天数之后不复存在,除非从外部引入。但是这种疾病几乎会早日死亡。我们已经计算了对锁定天数理想数量的国家特定估计。因此,较小的锁定阶段足以包含COVID-19。在警告时,我们的模型表明,几乎一年后,感染的另一个增加,但幅度较小。
Background: Restrictive mass quarantine or lockdown has been implemented as the most important controlling measure to fight against COVID-19. Many countries have enforced 2 - 4 weeks' lockdown and are extending the period depending on their current disease scenario. Most probably the 14-day period of estimated communicability of COVID-19 prompted such decision. But the idea that, if the susceptible population drops below certain threshold, the infection would naturally die out in small communities after a fixed time (following the outbreak), unless the disease is reintroduced from outside, was proposed by Bartlett in 1957. This threshold was termed as Critical Community Size (CCS). Methods: We propose an SEIR model that explains COVID-19 disease dynamics. Using our model, we have calculated country-specific expected time to extinction (TTE) and CCS that would essentially determine the ideal number of lockdown days required and size of quarantined population. Findings: With the given country-wise rates of death, recovery and other parameters, we have identified that, if at a place the total number of susceptible population drops below CCS, infection will cease to exist after a period of TTE days, unless it is introduced from outside. But the disease will almost die out much sooner. We have calculated the country-specific estimate of the ideal number of lockdown days. Thus, smaller lockdown phase is sufficient to contain COVID-19. On a cautionary note, our model indicates another rise in infection almost a year later but on a lesser magnitude.