Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Collège Doctoral, Sorbonne Université, Paris, France.
Nat Commun. 2021 Mar 12;12(1):1634. doi: 10.1038/s41467-021-21944-4.
While general lockdowns have proven effective to control SARS-CoV-2 epidemics, they come with enormous costs for society. It is therefore essential to identify control strategies with lower social and economic impact. Here, we report and evaluate the control strategy implemented during a large SARS-CoV-2 epidemic in June-July 2020 in French Guiana that relied on curfews, targeted lockdowns, and other measures. We find that the combination of these interventions coincided with a reduction in the basic reproduction number of SARS-CoV-2 from 1.7 to 1.1, which was sufficient to avoid hospital saturation. We estimate that thanks to the young demographics, the risk of hospitalisation following infection was 0.3 times that of metropolitan France and that about 20% of the population was infected by July. Our model projections are consistent with a recent seroprevalence study. The study showcases how mathematical modelling can be used to support healthcare planning in a context of high uncertainty.
虽然全面封锁已被证明可有效控制 SARS-CoV-2 疫情,但它们会给社会带来巨大的代价。因此,确定对社会和经济影响较小的控制策略至关重要。在这里,我们报告并评估了 2020 年 6 月至 7 月期间在法属圭亚那实施的一项大型 SARS-CoV-2 疫情控制策略,该策略依赖于宵禁、有针对性的封锁和其他措施。我们发现,这些干预措施的结合使 SARS-CoV-2 的基本繁殖数从 1.7 减少到 1.1,这足以避免医院饱和。我们估计,由于人口结构年轻,感染后的住院风险是法国本土的 0.3 倍,到 7 月约有 20%的人口感染。我们的模型预测与最近的血清流行率研究一致。该研究展示了如何在高度不确定的情况下使用数学模型来支持医疗保健规划。