Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate

Prague, Mélanie; Wittkop, Linda; Collin, Annabelle; Dutartre, Dan; Clairon, Quentin; Moireau, Philippe; Thiébaut, Rodolphe; Hejblum, Boris P.

doi:10.1101/2020.04.21.20073536

Cited by 29 publications

(53 citation statements)

References 49 publications

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“…Some of these problems can be avoided by explicit use of mathematical models that take into account the prolonged clinical progression of COVID-19 (e.g. [ 12 , 87 ]), which is the first order cause of these delays. However, our results show that transmission network structure also plays an important role.…”

Section: Discussionmentioning

confidence: 99%

“…Real-time and retrospective analyses of the growth rate of cases and deaths have suggested that in some settings the epidemic eventually slowed after the implementation of strong social distancing measures (e.g. in Wuhan and other Chinese cities [ 8 , 9 ], in Hong Kong [ 10 ], across European countries [ 11 ], French regions [ 12 ], or some US states [ 13 , 14 ]).…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

et al. 2021

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In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household “bubbles” can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

et al. 2021

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“…The degree distribution in this layer was obtained from the school contact survey data [13], which estimated that the school-going population in the United States, on average, had nSCHOOL ∼ 7.3 ± 1.8 school-related contacts daily. The variance was obtained by the variance in the mean across the school-aged (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) age groups. Individuals were assigned degrees randomly from a binomial distribution with this mean and standard deviation.…”

Section: Five-layer Network Structurementioning

confidence: 99%

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Nande

Adlam

Sheen

et al. 2020

Preprint

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In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. These findings can improve future predictions of the timescale and efficacy of interventions needed to control similar outbreaks, and highlight the need for better quantification and control of household transmission.

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“… Berger, Herkenhoff, and Mongey (2020) and Roques, Klein, Papaix, Sar, and Soubeyrand (2020) ). In Prague et al. (2020) , the lockdown effect is estimated using stochastic approximation, expectation maximization and an estimation of basic reproductive numbers.…”

Section: Introductionmentioning

confidence: 99%

Transport effect of COVID-19 pandemic in France

Guan

Prieur

Zhang

et al. 2020

Annual Reviews in Control

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An extension of the classical pandemic SIRD model is considered for the regional spread of COVID-19 in France under lockdown strategies. This compartment model divides the infected and the recovered individuals into undetected and detected compartments respectively. By fitting the extended model to the real detected data during the lockdown, an optimization algorithm is used to derive the optimal parameters, the initial condition and the epidemics start date of regions in France. Considering all the age classes together, a network model of the pandemic transport between regions in France is presented on the basis of the regional extended model and is simulated to reveal the transport effect of COVID-19 pandemic after lockdown. Using the measured values of displacement of people between cities, the pandemic network of all cities in France is simulated by using the same model and method as the pandemic network of regions. Finally, a discussion on an integro-differential equation is given and a new model for the network pandemic model of each age class is provided.

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Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate

Cited by 29 publications

References 49 publications

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Transport effect of COVID-19 pandemic in France

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