Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France

Abstract

Most European countries have responded to the COVID-19 threat by nationwide implementation of barrier measures and lockdown. However, assuming that population immunity will build up through the epidemic, it is likely to rebound once these measures are relaxed, possibly leading to a second or multiple repeated lockdowns. In this report, we present results of epidemiological modelling that has helped inform policy making in France. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in France, and examined the potential impact of post-quarantine measures, including social distancing, mask-wearing, and shielding of the population the most vulnerable to severe COVID-19 infection, on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is effective in containing the viral spread, it would be unlikely to prevent a rebound of the epidemic once lifted, regardless of its duration. Both social distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing the overwhelming of ICUs and a second lockdown. However, these measures coupled with shielding of vulnerable people would be associated with better outcomes, including lower cumulative incidence, mortality, and maintaining an adequate number of ICU beds to prevent a second lockdown. Benefits would nonetheless be markedly reduced if these measures were not applied by most people or not maintained for a sufficiently long period, as herd immunity progressively establishes in the less vulnerable population.

Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France

“…Using a simpler SIR model but a more robust statistical framework Roques et al (2020), found an R 0 between 3.1 and 3.3, but also estimated a temporal reproduction number (R t ) after the lock-down of 0.47 (with a 95% CI of 0.45-0.50). Hoertel et al (2020) developed a detailed 3 All rights reserved. No reuse allowed without permission.…”

Reducing community infection by institutional interventions: a minimal SEIRJ model applied to COVID-19

“…If the quarantined population return to the susceptible state, and under the assumption that the infectiousness and removal rates remain the same, R t can easily increase above 1 and a second wave of infection is probable. The estimate when to lift lockdowns can be improved and is better addressed by using stochastic models which can give interval estimates of when the community is disease-free 9,14,18,24,25 . While we have implemented a stochastic realization of the model to obtain the time to control the situation, this requires time and computational resources.…”

“…Further studies must be done and account for varied dynamics. For instance, agent-based models 18 and social network models 14 have also been used to address similar community interventions.…”

“…Moreover, the virus may still spread within households, in hospitals, and in shops. Several modelling studies have supported the effectiveness of lockdowns 11,[15][16][17][18] .…”

“…Lastly, lockdowns involve controlled or complete isolation of a local area. Lockdowns have been shown to be effective in controlling the spread of diseases [11][12][13][15][16][17][18] ; of note is that the subexponential growth of the number of infections in China after the initial spread is attributed to lockdown measures 15 . Most countries implemented lockdowns under varying versions, stages and duration, but such lockdowns incur unsustainable economic and social costs.…”