Impact of mass testing during an epidemic rebound of SARS-CoV-2: a modelling study using the example of France

Paolo Bosetti (1) , Cécile Tran Kiem (1, 2) , Yazdan Yazdanpanah (3) , Arnaud Fontanet (4, 5) , Bruno Lina (6, 7) , Vittoria Colizza (8) , Simon Cauchemez (1)

1 Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases
2 Collège Doctoral
3 IAME (UMR_S_1137 / U1137) - Infection, Anti-microbiens, Modélisation, Evolution
4 Epidémiologie des Maladies Emergentes - Emerging Diseases Epidemiology
5 CNAM - Conservatoire National des Arts et Métiers [CNAM]
6 IAI - Institut des Agents Infectieux [Lyon]
7 VirPath - Virology and human respiratory Pathologies - Virology and human respiratory Pathologies
8 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique

We used a mathematical model to evaluate the impact of mass testing in the control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Under optimistic assumptions, one round of mass testing may reduce daily infections by up to 20–30%. Consequently, very frequent testing would be required to control a quickly growing epidemic if other control measures were to be relaxed. Mass testing is most relevant when epidemic growth remains limited through a combination of interventions.

Mots clés: Covid-19; SARS-CoV-2; epidemic; mass testing

Domaines: Statistiques [math.ST]

Paolo Bosetti, Cécile Tran Kiem, Yazdan Yazdanpanah, Arnaud Fontanet, Bruno Lina, et al.. Impact of mass testing during an epidemic rebound of SARS-CoV-2: a modelling study using the example of France. Eurosurveillance, 2021, 26 (1), pp.100087. ⟨10.2807/1560-7917.ES.2020.26.1.2001978⟩. ⟨pasteur-03325593⟩