Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana

Alessio Andronico (), Cécile Tran Kiem, Juliette Paireau, Tiphanie Succo, Paolo Bosetti, Noémie Lefrancq, Mathieu Nacher, Félix Djossou, Alice Sanna, Claude Flamand, Henrik Salje, Cyril Rousseau and Simon Cauchemez
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Alessio Andronico: Institut Pasteur, UMR2000, CNRS
Cécile Tran Kiem: Institut Pasteur, UMR2000, CNRS
Juliette Paireau: Institut Pasteur, UMR2000, CNRS
Tiphanie Succo: French National Public Health Agency
Paolo Bosetti: Institut Pasteur, UMR2000, CNRS
Noémie Lefrancq: Institut Pasteur, UMR2000, CNRS
Mathieu Nacher: Centre d’Investigation Clinique Antilles Guyane, CIC INSERM 1424, Centre Hospitalier Andrée Rosemon
Félix Djossou: Centre Hospitalier de Cayenne
Alice Sanna: Agence Régionale de Santé de Guyane
Claude Flamand: Institut Pasteur, UMR2000, CNRS
Henrik Salje: Institut Pasteur, UMR2000, CNRS
Cyril Rousseau: French National Public Health Agency
Simon Cauchemez: Institut Pasteur, UMR2000, CNRS

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-21944-4

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